Technology Stack Evaluator A comprehensive evaluation framework for comparing technologies, frameworks, cloud providers, and complete technology stacks. Provides data-driven recommendations with TCO analysis, security assessment, ecosystem health scoring, and migration path analysis. Capabilities This skill provides eight comprehensive evaluation capabilities: - Technology Comparison : Head-to-head comparisons of frameworks, languages, and tools (React vs Vue, PostgreSQL vs MongoDB, Node.js vs Python) - Stack Evaluation : Assess complete technology stacks for specific use cases (real-time col…

, # Trailing colons\n ]\n\n # Must not be JSON\n if self._is_json():\n return False\n\n # Check for YAML patterns\n lines = self.raw_input.split('\\n')\n yaml_line_count = 0\n\n for line in lines:\n for pattern in yaml_patterns:\n if re.match(pattern, line):\n yaml_line_count += 1\n break\n\n # If >50% of lines match YAML patterns, consider it YAML\n if len(lines) > 0 and yaml_line_count / len(lines) > 0.5:\n return True\n\n return False\n\n def _contains_urls(self) -> bool:\n \"\"\"Check if input contains URLs.\"\"\"\n url_pattern = r'https?://[^\\s]+'\n return bool(re.search(url_pattern, self.raw_input))\n\n def parse(self) -> Dict[str, Any]:\n \"\"\"\n Parse input based on detected format.\n\n Returns:\n Parsed data dictionary\n \"\"\"\n if self.detected_format is None:\n self.detect_format()\n\n if self.detected_format == 'json':\n self.parsed_data = self._parse_json()\n elif self.detected_format == 'yaml':\n self.parsed_data = self._parse_yaml()\n elif self.detected_format == 'url':\n self.parsed_data = self._parse_urls()\n else: # text\n self.parsed_data = self._parse_text()\n\n return self.parsed_data\n\n def _parse_json(self) -> Dict[str, Any]:\n \"\"\"Parse JSON input.\"\"\"\n try:\n data = json.loads(self.raw_input)\n return self._normalize_structure(data)\n except json.JSONDecodeError:\n return {'error': 'Invalid JSON', 'raw': self.raw_input}\n\n def _parse_yaml(self) -> Dict[str, Any]:\n \"\"\"\n Parse YAML-like input (simplified, no external dependencies).\n\n Returns:\n Parsed dictionary\n \"\"\"\n result = {}\n current_section = None\n current_list = None\n\n lines = self.raw_input.split('\\n')\n\n for line in lines:\n stripped = line.strip()\n if not stripped or stripped.startswith('#'):\n continue\n\n # Key-value pair\n if ':' in stripped:\n key, value = stripped.split(':', 1)\n key = key.strip()\n value = value.strip()\n\n # Empty value might indicate nested structure\n if not value:\n current_section = key\n result[current_section] = {}\n current_list = None\n else:\n if current_section:\n result[current_section][key] = self._parse_value(value)\n else:\n result[key] = self._parse_value(value)\n\n # List item\n elif stripped.startswith('-'):\n item = stripped[1:].strip()\n if current_section:\n if current_list is None:\n current_list = []\n result[current_section] = current_list\n current_list.append(self._parse_value(item))\n\n return self._normalize_structure(result)\n\n def _parse_value(self, value: str) -> Any:\n \"\"\"\n Parse a value string to appropriate type.\n\n Args:\n value: Value string\n\n Returns:\n Parsed value (str, int, float, bool)\n \"\"\"\n value = value.strip()\n\n # Boolean\n if value.lower() in ['true', 'yes']:\n return True\n if value.lower() in ['false', 'no']:\n return False\n\n # Number\n try:\n if '.' in value:\n return float(value)\n else:\n return int(value)\n except ValueError:\n pass\n\n # String (remove quotes if present)\n if value.startswith('\"') and value.endswith('\"'):\n return value[1:-1]\n if value.startswith(\"'\") and value.endswith(\"'\"):\n return value[1:-1]\n\n return value\n\n def _parse_urls(self) -> Dict[str, Any]:\n \"\"\"Parse URLs from input.\"\"\"\n url_pattern = r'https?://[^\\s]+'\n urls = re.findall(url_pattern, self.raw_input)\n\n # Categorize URLs\n github_urls = [u for u in urls if 'github.com' in u]\n npm_urls = [u for u in urls if 'npmjs.com' in u or 'npm.io' in u]\n other_urls = [u for u in urls if u not in github_urls and u not in npm_urls]\n\n # Also extract any text context\n text_without_urls = re.sub(url_pattern, '', self.raw_input).strip()\n\n result = {\n 'format': 'url',\n 'urls': {\n 'github': github_urls,\n 'npm': npm_urls,\n 'other': other_urls\n },\n 'context': text_without_urls\n }\n\n return self._normalize_structure(result)\n\n def _parse_text(self) -> Dict[str, Any]:\n \"\"\"Parse conversational text input.\"\"\"\n text = self.raw_input.lower()\n\n # Extract technologies being compared\n technologies = self._extract_technologies(text)\n\n # Extract use case\n use_case = self._extract_use_case(text)\n\n # Extract priorities\n priorities = self._extract_priorities(text)\n\n # Detect analysis type\n analysis_type = self._detect_analysis_type(text)\n\n result = {\n 'format': 'text',\n 'technologies': technologies,\n 'use_case': use_case,\n 'priorities': priorities,\n 'analysis_type': analysis_type,\n 'raw_text': self.raw_input\n }\n\n return self._normalize_structure(result)\n\n def _extract_technologies(self, text: str) -> list:\n \"\"\"\n Extract technology names from text.\n\n Args:\n text: Lowercase text\n\n Returns:\n List of identified technologies\n \"\"\"\n # Common technologies pattern\n tech_keywords = [\n 'react', 'vue', 'angular', 'svelte', 'next.js', 'nuxt.js',\n 'node.js', 'python', 'java', 'go', 'rust', 'ruby',\n 'postgresql', 'postgres', 'mysql', 'mongodb', 'redis',\n 'aws', 'azure', 'gcp', 'google cloud',\n 'docker', 'kubernetes', 'k8s',\n 'express', 'fastapi', 'django', 'flask', 'spring boot'\n ]\n\n found = []\n for tech in tech_keywords:\n if tech in text:\n # Normalize names\n normalized = {\n 'postgres': 'PostgreSQL',\n 'next.js': 'Next.js',\n 'nuxt.js': 'Nuxt.js',\n 'node.js': 'Node.js',\n 'k8s': 'Kubernetes',\n 'gcp': 'Google Cloud Platform'\n }.get(tech, tech.title())\n\n if normalized not in found:\n found.append(normalized)\n\n return found if found else ['Unknown']\n\n def _extract_use_case(self, text: str) -> str:\n \"\"\"\n Extract use case description from text.\n\n Args:\n text: Lowercase text\n\n Returns:\n Use case description\n \"\"\"\n use_case_keywords = {\n 'real-time': 'Real-time application',\n 'collaboration': 'Collaboration platform',\n 'saas': 'SaaS application',\n 'dashboard': 'Dashboard application',\n 'api': 'API-heavy application',\n 'data-intensive': 'Data-intensive application',\n 'e-commerce': 'E-commerce platform',\n 'enterprise': 'Enterprise application'\n }\n\n for keyword, description in use_case_keywords.items():\n if keyword in text:\n return description\n\n return 'General purpose application'\n\n def _extract_priorities(self, text: str) -> list:\n \"\"\"\n Extract priority criteria from text.\n\n Args:\n text: Lowercase text\n\n Returns:\n List of priorities\n \"\"\"\n priority_keywords = {\n 'performance': 'Performance',\n 'scalability': 'Scalability',\n 'developer experience': 'Developer experience',\n 'ecosystem': 'Ecosystem',\n 'learning curve': 'Learning curve',\n 'cost': 'Cost',\n 'security': 'Security',\n 'compliance': 'Compliance'\n }\n\n priorities = []\n for keyword, priority in priority_keywords.items():\n if keyword in text:\n priorities.append(priority)\n\n return priorities if priorities else ['Developer experience', 'Performance']\n\n def _detect_analysis_type(self, text: str) -> str:\n \"\"\"\n Detect type of analysis requested.\n\n Args:\n text: Lowercase text\n\n Returns:\n Analysis type\n \"\"\"\n type_keywords = {\n 'migration': 'migration_analysis',\n 'migrate': 'migration_analysis',\n 'tco': 'tco_analysis',\n 'total cost': 'tco_analysis',\n 'security': 'security_analysis',\n 'compliance': 'security_analysis',\n 'compare': 'comparison',\n 'vs': 'comparison',\n 'evaluate': 'evaluation'\n }\n\n for keyword, analysis_type in type_keywords.items():\n if keyword in text:\n return analysis_type\n\n return 'comparison' # Default\n\n def _normalize_structure(self, data: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"\n Normalize parsed data to standard structure.\n\n Args:\n data: Parsed data dictionary\n\n Returns:\n Normalized data structure\n \"\"\"\n # Ensure standard keys exist\n standard_keys = [\n 'technologies',\n 'use_case',\n 'priorities',\n 'analysis_type',\n 'format'\n ]\n\n normalized = data.copy()\n\n for key in standard_keys:\n if key not in normalized:\n # Set defaults\n defaults = {\n 'technologies': [],\n 'use_case': 'general',\n 'priorities': [],\n 'analysis_type': 'comparison',\n 'format': self.detected_format or 'unknown'\n }\n normalized[key] = defaults.get(key)\n\n return normalized\n\n def get_format_info(self) -> Dict[str, Any]:\n \"\"\"\n Get information about detected format.\n\n Returns:\n Format detection metadata\n \"\"\"\n return {\n 'detected_format': self.detected_format,\n 'input_length': len(self.raw_input),\n 'line_count': len(self.raw_input.split('\\n')),\n 'parsing_successful': self.parsed_data is not None\n }\n","content_type":"text/x-python; charset=utf-8","language":"python","size":12611,"content_sha256":"0df7dc8fd5dfe4ce8c4eb964d0c63f00590c20ebb1296419e9bc9997e149bced"},{"filename":"HOW_TO_USE.md","content":"# How to Use the Technology Stack Evaluator Skill\n\nThe Technology Stack Evaluator skill provides comprehensive evaluation and comparison of technologies, frameworks, and complete technology stacks for engineering teams.\n\n## Quick Start Examples\n\n### Example 1: Simple Technology Comparison\n\n**Conversational (Easiest)**:\n```\nHey Claude—I just added the \"tech-stack-evaluator\" skill. Can you compare React vs Vue for building a SaaS dashboard?\n```\n\n**What you'll get**:\n- Executive summary with recommendation\n- Comparison matrix with scores\n- Top 3 pros and cons for each\n- Confidence level\n- Key decision factors\n\n---\n\n### Example 2: Complete Stack Evaluation\n\n```\nHey Claude—I just added the \"tech-stack-evaluator\" skill. Can you evaluate this technology stack for a real-time collaboration platform:\n- Frontend: Next.js\n- Backend: Node.js + Express\n- Database: PostgreSQL\n- Real-time: WebSockets\n- Hosting: AWS\n\nInclude TCO analysis and ecosystem health assessment.\n```\n\n**What you'll get**:\n- Complete stack evaluation\n- TCO breakdown (5-year projection)\n- Ecosystem health scores\n- Security assessment\n- Detailed recommendations\n\n---\n\n### Example 3: Migration Analysis\n\n```\nHey Claude—I just added the \"tech-stack-evaluator\" skill. We're considering migrating from Angular.js (1.x) to React. Our codebase:\n- 75,000 lines of code\n- 300 components\n- 8-person development team\n- Must minimize downtime\n\nCan you assess migration complexity, effort, risks, and timeline?\n```\n\n**What you'll get**:\n- Migration complexity score (1-10)\n- Effort estimate (person-months and timeline)\n- Risk assessment (technical, business, team)\n- Phased migration plan\n- Success criteria\n\n---\n\n### Example 4: TCO Analysis\n\n```\nHey Claude—I just added the \"tech-stack-evaluator\" skill. Calculate total cost of ownership for AWS vs Azure for our workload:\n- 50 EC2/VM instances (growing 25% annually)\n- 20TB database storage\n- Team: 12 developers\n- 5-year projection\n\nInclude hidden costs like technical debt and vendor lock-in.\n```\n\n**What you'll get**:\n- 5-year TCO breakdown\n- Initial vs operational costs\n- Scaling cost projections\n- Cost per user metrics\n- Hidden costs (technical debt, vendor lock-in, downtime)\n- Cost optimization opportunities\n\n---\n\n### Example 5: Security & Compliance Assessment\n\n```\nHey Claude—I just added the \"tech-stack-evaluator\" skill. Assess the security posture of our current stack:\n- Express.js (Node.js)\n- MongoDB\n- JWT authentication\n- Hosted on AWS\n\nWe need SOC2 and GDPR compliance. What are the gaps?\n```\n\n**What you'll get**:\n- Security score (0-100) with grade\n- Vulnerability analysis (CVE counts by severity)\n- Compliance readiness for SOC2 and GDPR\n- Missing security features\n- Recommendations to improve security\n\n---\n\n### Example 6: Cloud Provider Comparison\n\n```\nHey Claude—I just added the \"tech-stack-evaluator\" skill. Compare AWS vs Azure vs GCP for machine learning workloads:\n- Priorities: GPU availability (40%), Cost (30%), ML ecosystem (20%), Support (10%)\n- Need: High GPU availability for model training\n- Team: 5 ML engineers, experienced with Python\n\nGenerate weighted decision matrix.\n```\n\n**What you'll get**:\n- Weighted comparison matrix\n- Scores across all criteria\n- Best performer by category\n- Overall recommendation with confidence\n- Pros/cons for each provider\n\n---\n\n## Input Formats Supported\n\n### 1. Conversational Text (Easiest)\nJust describe what you want in natural language:\n```\n\"Compare PostgreSQL vs MongoDB for a SaaS application\"\n\"Evaluate security of our Express.js + JWT stack\"\n\"Calculate TCO for migrating to microservices\"\n```\n\n### 2. Structured JSON\nFor precise control over evaluation parameters:\n```json\n{\n \"comparison\": {\n \"technologies\": [\"React\", \"Vue\", \"Svelte\"],\n \"use_case\": \"Enterprise dashboard\",\n \"weights\": {\n \"performance\": 25,\n \"developer_experience\": 30,\n \"ecosystem\": 25,\n \"learning_curve\": 20\n }\n }\n}\n```\n\n### 3. YAML (Alternative Structured Format)\n```yaml\ncomparison:\n technologies:\n - React\n - Vue\n use_case: SaaS dashboard\n priorities:\n - Developer productivity\n - Ecosystem maturity\n```\n\n### 4. URLs for Ecosystem Analysis\n```\n\"Analyze ecosystem health for these technologies:\n- https://github.com/facebook/react\n- https://github.com/vuejs/vue\n- https://www.npmjs.com/package/react\"\n```\n\nThe skill automatically detects the format and parses accordingly!\n\n---\n\n## Report Sections Available\n\nYou can request specific sections or get the full report:\n\n### Available Sections:\n1. **Executive Summary** (200-300 tokens) - Recommendation + top pros/cons\n2. **Comparison Matrix** - Weighted scoring across all criteria\n3. **TCO Analysis** - Complete cost breakdown (initial + operational + hidden)\n4. **Ecosystem Health** - Community size, maintenance, viability\n5. **Security Assessment** - Vulnerabilities, compliance readiness\n6. **Migration Analysis** - Complexity, effort, risks, timeline\n7. **Performance Benchmarks** - Throughput, latency, resource usage\n\n### Request Specific Sections:\n```\n\"Compare Next.js vs Nuxt.js. Include only: ecosystem health and performance benchmarks. Skip TCO and migration analysis.\"\n```\n\n---\n\n## What to Provide\n\n### For Technology Comparison:\n- Technologies to compare (2-5 recommended)\n- Use case or application type (optional but helpful)\n- Priorities/weights (optional, uses sensible defaults)\n\n### For TCO Analysis:\n- Technology/platform name\n- Team size\n- Current costs (hosting, licensing, support)\n- Growth projections (user growth, scaling needs)\n- Developer productivity factors (optional)\n\n### For Migration Assessment:\n- Source technology (current stack)\n- Target technology (desired stack)\n- Codebase statistics (lines of code, number of components)\n- Team information (size, experience level)\n- Constraints (downtime tolerance, timeline)\n\n### For Security Assessment:\n- Technology stack components\n- Security features currently implemented\n- Compliance requirements (GDPR, SOC2, HIPAA, PCI-DSS)\n- Known vulnerabilities (if any)\n\n### For Ecosystem Analysis:\n- Technology name or GitHub/npm URL\n- Specific metrics of interest (optional)\n\n---\n\n## Output Formats\n\nThe skill adapts output based on your environment:\n\n### Claude Desktop (Rich Markdown)\n- Formatted tables with visual indicators\n- Expandable sections\n- Color-coded scores (via markdown formatting)\n- Decision matrices\n\n### CLI/Terminal (Terminal-Friendly)\n- ASCII tables\n- Compact formatting\n- Plain text output\n- Copy-paste friendly\n\nThe skill automatically detects your environment!\n\n---\n\n## Advanced Usage\n\n### Custom Weighted Criteria:\n```\n\"Compare React vs Vue vs Svelte.\nPriorities (weighted):\n- Developer experience: 35%\n- Performance: 30%\n- Ecosystem: 20%\n- Learning curve: 15%\"\n```\n\n### Multiple Analysis Types:\n```\n\"Evaluate Next.js for our enterprise SaaS platform.\nInclude: TCO (5-year), ecosystem health, security assessment, and performance vs Nuxt.js.\"\n```\n\n### Progressive Disclosure:\n```\n\"Compare AWS vs Azure. Start with executive summary only.\"\n\n(After reviewing summary)\n\"Show me the detailed TCO breakdown for AWS.\"\n```\n\n---\n\n## Tips for Best Results\n\n1. **Be Specific About Use Case**: \"Real-time collaboration platform\" is better than \"web app\"\n\n2. **Provide Context**: Team size, experience level, constraints help generate better recommendations\n\n3. **Set Clear Priorities**: If cost is more important than performance, say so with weights\n\n4. **Request Incremental Analysis**: Start with executive summary, then drill into specific sections\n\n5. **Include Constraints**: Zero-downtime requirement, budget limits, timeline pressure\n\n6. **Validate Assumptions**: Review the TCO assumptions and adjust if needed\n\n---\n\n## Common Questions\n\n**Q: How current is the data?**\nA: The skill uses current data sources when available (GitHub, npm, CVE databases). Ecosystem metrics are point-in-time snapshots.\n\n**Q: Can I compare more than 2 technologies?**\nA: Yes! You can compare 2-5 technologies. More than 5 becomes less actionable.\n\n**Q: What if I don't know the exact data for TCO analysis?**\nA: The skill uses industry-standard defaults. Just provide what you know (team size, rough costs) and it will fill in reasonable estimates.\n\n**Q: Can I export reports?**\nA: Yes! The skill can generate markdown reports that you can save or export.\n\n**Q: How do confidence scores work?**\nA: Confidence (0-100%) is based on:\n- Score gap between options (larger gap = higher confidence)\n- Data completeness\n- Clarity of requirements\n\n**Q: What if technologies are very close in scores?**\nA: The skill will report low confidence and highlight that it's a close call, helping you understand there's no clear winner.\n\n---\n\n## Need Help?\n\nIf results aren't what you expected:\n1. **Clarify your use case** - Be more specific about requirements\n2. **Adjust priorities** - Set custom weights for what matters most\n3. **Provide more context** - Team skills, constraints, business goals\n4. **Request specific sections** - Focus on what's most relevant\n\nExample clarification:\n```\n\"The comparison seemed to favor React, but we're a small team (3 devs) with no React experience. Can you re-evaluate with learning curve weighted at 40%?\"\n```\n\nThe skill will adjust the analysis based on your refined requirements!\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":9265,"content_sha256":"cc583a9ce7963120e4e2b46093dd97d4789022e32a6e2a64ec26f3295eef1e58"},{"filename":"migration_analyzer.py","content":"\"\"\"\nMigration Path Analyzer.\n\nAnalyzes migration complexity, risks, timelines, and strategies for moving\nfrom legacy technology stacks to modern alternatives.\n\"\"\"\n\nfrom typing import Dict, List, Any, Optional, Tuple\n\n\nclass MigrationAnalyzer:\n \"\"\"Analyze migration paths and complexity for technology stack changes.\"\"\"\n\n # Migration complexity factors\n COMPLEXITY_FACTORS = [\n 'code_volume',\n 'architecture_changes',\n 'data_migration',\n 'api_compatibility',\n 'dependency_changes',\n 'testing_requirements'\n ]\n\n def __init__(self, migration_data: Dict[str, Any]):\n \"\"\"\n Initialize migration analyzer with migration parameters.\n\n Args:\n migration_data: Dictionary containing source/target technologies and constraints\n \"\"\"\n self.source_tech = migration_data.get('source_technology', 'Unknown')\n self.target_tech = migration_data.get('target_technology', 'Unknown')\n self.codebase_stats = migration_data.get('codebase_stats', {})\n self.constraints = migration_data.get('constraints', {})\n self.team_info = migration_data.get('team', {})\n\n def calculate_complexity_score(self) -> Dict[str, Any]:\n \"\"\"\n Calculate overall migration complexity (1-10 scale).\n\n Returns:\n Dictionary with complexity scores by factor\n \"\"\"\n scores = {\n 'code_volume': self._score_code_volume(),\n 'architecture_changes': self._score_architecture_changes(),\n 'data_migration': self._score_data_migration(),\n 'api_compatibility': self._score_api_compatibility(),\n 'dependency_changes': self._score_dependency_changes(),\n 'testing_requirements': self._score_testing_requirements()\n }\n\n # Calculate weighted average\n weights = {\n 'code_volume': 0.20,\n 'architecture_changes': 0.25,\n 'data_migration': 0.20,\n 'api_compatibility': 0.15,\n 'dependency_changes': 0.10,\n 'testing_requirements': 0.10\n }\n\n overall = sum(scores[k] * weights[k] for k in scores.keys())\n scores['overall_complexity'] = overall\n\n return scores\n\n def _score_code_volume(self) -> float:\n \"\"\"\n Score complexity based on codebase size.\n\n Returns:\n Code volume complexity score (1-10)\n \"\"\"\n lines_of_code = self.codebase_stats.get('lines_of_code', 10000)\n num_files = self.codebase_stats.get('num_files', 100)\n num_components = self.codebase_stats.get('num_components', 50)\n\n # Score based on lines of code (primary factor)\n if lines_of_code \u003c 5000:\n base_score = 2\n elif lines_of_code \u003c 20000:\n base_score = 4\n elif lines_of_code \u003c 50000:\n base_score = 6\n elif lines_of_code \u003c 100000:\n base_score = 8\n else:\n base_score = 10\n\n # Adjust for component count\n if num_components > 200:\n base_score = min(10, base_score + 1)\n elif num_components > 500:\n base_score = min(10, base_score + 2)\n\n return float(base_score)\n\n def _score_architecture_changes(self) -> float:\n \"\"\"\n Score complexity based on architectural changes.\n\n Returns:\n Architecture complexity score (1-10)\n \"\"\"\n arch_change_level = self.codebase_stats.get('architecture_change_level', 'moderate')\n\n scores = {\n 'minimal': 2, # Same patterns, just different framework\n 'moderate': 5, # Some pattern changes, similar concepts\n 'significant': 7, # Different patterns, major refactoring\n 'complete': 10 # Complete rewrite, different paradigm\n }\n\n return float(scores.get(arch_change_level, 5))\n\n def _score_data_migration(self) -> float:\n \"\"\"\n Score complexity based on data migration requirements.\n\n Returns:\n Data migration complexity score (1-10)\n \"\"\"\n has_database = self.codebase_stats.get('has_database', True)\n if not has_database:\n return 1.0\n\n database_size_gb = self.codebase_stats.get('database_size_gb', 10)\n schema_changes = self.codebase_stats.get('schema_changes_required', 'minimal')\n data_transformation = self.codebase_stats.get('data_transformation_required', False)\n\n # Base score from database size\n if database_size_gb \u003c 1:\n score = 2\n elif database_size_gb \u003c 10:\n score = 3\n elif database_size_gb \u003c 100:\n score = 5\n elif database_size_gb \u003c 1000:\n score = 7\n else:\n score = 9\n\n # Adjust for schema changes\n schema_adjustments = {\n 'none': 0,\n 'minimal': 1,\n 'moderate': 2,\n 'significant': 3\n }\n score += schema_adjustments.get(schema_changes, 1)\n\n # Adjust for data transformation\n if data_transformation:\n score += 2\n\n return min(10.0, float(score))\n\n def _score_api_compatibility(self) -> float:\n \"\"\"\n Score complexity based on API compatibility.\n\n Returns:\n API compatibility complexity score (1-10)\n \"\"\"\n breaking_api_changes = self.codebase_stats.get('breaking_api_changes', 'some')\n\n scores = {\n 'none': 1, # Fully compatible\n 'minimal': 3, # Few breaking changes\n 'some': 5, # Moderate breaking changes\n 'many': 7, # Significant breaking changes\n 'complete': 10 # Complete API rewrite\n }\n\n return float(scores.get(breaking_api_changes, 5))\n\n def _score_dependency_changes(self) -> float:\n \"\"\"\n Score complexity based on dependency changes.\n\n Returns:\n Dependency complexity score (1-10)\n \"\"\"\n num_dependencies = self.codebase_stats.get('num_dependencies', 20)\n dependencies_to_replace = self.codebase_stats.get('dependencies_to_replace', 5)\n\n # Score based on replacement percentage\n if num_dependencies == 0:\n return 1.0\n\n replacement_pct = (dependencies_to_replace / num_dependencies) * 100\n\n if replacement_pct \u003c 10:\n return 2.0\n elif replacement_pct \u003c 25:\n return 4.0\n elif replacement_pct \u003c 50:\n return 6.0\n elif replacement_pct \u003c 75:\n return 8.0\n else:\n return 10.0\n\n def _score_testing_requirements(self) -> float:\n \"\"\"\n Score complexity based on testing requirements.\n\n Returns:\n Testing complexity score (1-10)\n \"\"\"\n test_coverage = self.codebase_stats.get('current_test_coverage', 0.5) # 0-1 scale\n num_tests = self.codebase_stats.get('num_tests', 100)\n\n # If good test coverage, easier migration (can verify)\n if test_coverage >= 0.8:\n base_score = 3\n elif test_coverage >= 0.6:\n base_score = 5\n elif test_coverage >= 0.4:\n base_score = 7\n else:\n base_score = 9 # Poor coverage = hard to verify migration\n\n # Large test suites need updates\n if num_tests > 500:\n base_score = min(10, base_score + 1)\n\n return float(base_score)\n\n def estimate_effort(self) -> Dict[str, Any]:\n \"\"\"\n Estimate migration effort in person-hours and timeline.\n\n Returns:\n Dictionary with effort estimates\n \"\"\"\n complexity = self.calculate_complexity_score()\n overall_complexity = complexity['overall_complexity']\n\n # Base hours estimation\n lines_of_code = self.codebase_stats.get('lines_of_code', 10000)\n base_hours = lines_of_code / 50 # 50 lines per hour baseline\n\n # Complexity multiplier\n complexity_multiplier = 1 + (overall_complexity / 10)\n estimated_hours = base_hours * complexity_multiplier\n\n # Break down by phase\n phases = self._calculate_phase_breakdown(estimated_hours)\n\n # Calculate timeline\n team_size = self.team_info.get('team_size', 3)\n hours_per_week_per_dev = self.team_info.get('hours_per_week', 30) # Account for other work\n\n total_dev_weeks = estimated_hours / (team_size * hours_per_week_per_dev)\n total_calendar_weeks = total_dev_weeks * 1.2 # Buffer for blockers\n\n return {\n 'total_hours': estimated_hours,\n 'total_person_months': estimated_hours / 160, # 160 hours per person-month\n 'phases': phases,\n 'estimated_timeline': {\n 'dev_weeks': total_dev_weeks,\n 'calendar_weeks': total_calendar_weeks,\n 'calendar_months': total_calendar_weeks / 4.33\n },\n 'team_assumptions': {\n 'team_size': team_size,\n 'hours_per_week_per_dev': hours_per_week_per_dev\n }\n }\n\n def _calculate_phase_breakdown(self, total_hours: float) -> Dict[str, Dict[str, float]]:\n \"\"\"\n Calculate effort breakdown by migration phase.\n\n Args:\n total_hours: Total estimated hours\n\n Returns:\n Hours breakdown by phase\n \"\"\"\n # Standard phase percentages\n phase_percentages = {\n 'planning_and_prototyping': 0.15,\n 'core_migration': 0.45,\n 'testing_and_validation': 0.25,\n 'deployment_and_monitoring': 0.10,\n 'buffer_and_contingency': 0.05\n }\n\n phases = {}\n for phase, percentage in phase_percentages.items():\n hours = total_hours * percentage\n phases[phase] = {\n 'hours': hours,\n 'person_weeks': hours / 40,\n 'percentage': f\"{percentage * 100:.0f}%\"\n }\n\n return phases\n\n def assess_risks(self) -> Dict[str, List[Dict[str, str]]]:\n \"\"\"\n Identify and assess migration risks.\n\n Returns:\n Categorized risks with mitigation strategies\n \"\"\"\n complexity = self.calculate_complexity_score()\n\n risks = {\n 'technical_risks': self._identify_technical_risks(complexity),\n 'business_risks': self._identify_business_risks(),\n 'team_risks': self._identify_team_risks()\n }\n\n return risks\n\n def _identify_technical_risks(self, complexity: Dict[str, float]) -> List[Dict[str, str]]:\n \"\"\"\n Identify technical risks.\n\n Args:\n complexity: Complexity scores\n\n Returns:\n List of technical risks with mitigations\n \"\"\"\n risks = []\n\n # API compatibility risks\n if complexity['api_compatibility'] >= 7:\n risks.append({\n 'risk': 'Breaking API changes may cause integration failures',\n 'severity': 'High',\n 'mitigation': 'Create compatibility layer; implement feature flags for gradual rollout'\n })\n\n # Data migration risks\n if complexity['data_migration'] >= 7:\n risks.append({\n 'risk': 'Data migration could cause data loss or corruption',\n 'severity': 'Critical',\n 'mitigation': 'Implement robust backup strategy; run parallel systems during migration; extensive validation'\n })\n\n # Architecture risks\n if complexity['architecture_changes'] >= 8:\n risks.append({\n 'risk': 'Major architectural changes increase risk of performance regression',\n 'severity': 'High',\n 'mitigation': 'Extensive performance testing; staged rollout; monitoring and alerting'\n })\n\n # Testing risks\n if complexity['testing_requirements'] >= 7:\n risks.append({\n 'risk': 'Inadequate test coverage may miss critical bugs',\n 'severity': 'Medium',\n 'mitigation': 'Improve test coverage before migration; automated regression testing; user acceptance testing'\n })\n\n if not risks:\n risks.append({\n 'risk': 'Standard technical risks (bugs, edge cases)',\n 'severity': 'Low',\n 'mitigation': 'Standard QA processes and staged rollout'\n })\n\n return risks\n\n def _identify_business_risks(self) -> List[Dict[str, str]]:\n \"\"\"\n Identify business risks.\n\n Returns:\n List of business risks with mitigations\n \"\"\"\n risks = []\n\n # Downtime risk\n downtime_tolerance = self.constraints.get('downtime_tolerance', 'low')\n if downtime_tolerance == 'none':\n risks.append({\n 'risk': 'Zero-downtime migration increases complexity and risk',\n 'severity': 'High',\n 'mitigation': 'Blue-green deployment; feature flags; gradual traffic migration'\n })\n\n # Feature parity risk\n risks.append({\n 'risk': 'New implementation may lack feature parity',\n 'severity': 'Medium',\n 'mitigation': 'Comprehensive feature audit; prioritized feature list; clear communication'\n })\n\n # Timeline risk\n risks.append({\n 'risk': 'Migration may take longer than estimated',\n 'severity': 'Medium',\n 'mitigation': 'Build in 20% buffer; regular progress reviews; scope management'\n })\n\n return risks\n\n def _identify_team_risks(self) -> List[Dict[str, str]]:\n \"\"\"\n Identify team-related risks.\n\n Returns:\n List of team risks with mitigations\n \"\"\"\n risks = []\n\n # Learning curve\n team_experience = self.team_info.get('target_tech_experience', 'low')\n if team_experience in ['low', 'none']:\n risks.append({\n 'risk': 'Team lacks experience with target technology',\n 'severity': 'High',\n 'mitigation': 'Training program; hire experienced developers; external consulting'\n })\n\n # Team size\n team_size = self.team_info.get('team_size', 3)\n if team_size \u003c 3:\n risks.append({\n 'risk': 'Small team size may extend timeline',\n 'severity': 'Medium',\n 'mitigation': 'Consider augmenting team; reduce scope; extend timeline'\n })\n\n # Knowledge retention\n risks.append({\n 'risk': 'Loss of institutional knowledge during migration',\n 'severity': 'Medium',\n 'mitigation': 'Comprehensive documentation; knowledge sharing sessions; pair programming'\n })\n\n return risks\n\n def generate_migration_plan(self) -> Dict[str, Any]:\n \"\"\"\n Generate comprehensive migration plan.\n\n Returns:\n Complete migration plan with timeline and recommendations\n \"\"\"\n complexity = self.calculate_complexity_score()\n effort = self.estimate_effort()\n risks = self.assess_risks()\n\n # Generate phased approach\n approach = self._recommend_migration_approach(complexity['overall_complexity'])\n\n # Generate recommendation\n recommendation = self._generate_migration_recommendation(complexity, effort, risks)\n\n return {\n 'source_technology': self.source_tech,\n 'target_technology': self.target_tech,\n 'complexity_analysis': complexity,\n 'effort_estimation': effort,\n 'risk_assessment': risks,\n 'recommended_approach': approach,\n 'overall_recommendation': recommendation,\n 'success_criteria': self._define_success_criteria()\n }\n\n def _recommend_migration_approach(self, complexity_score: float) -> Dict[str, Any]:\n \"\"\"\n Recommend migration approach based on complexity.\n\n Args:\n complexity_score: Overall complexity score\n\n Returns:\n Recommended approach details\n \"\"\"\n if complexity_score \u003c= 3:\n approach = 'direct_migration'\n description = 'Direct migration - low complexity allows straightforward migration'\n timeline_multiplier = 1.0\n elif complexity_score \u003c= 6:\n approach = 'phased_migration'\n description = 'Phased migration - migrate components incrementally to manage risk'\n timeline_multiplier = 1.3\n else:\n approach = 'strangler_pattern'\n description = 'Strangler pattern - gradually replace old system while running in parallel'\n timeline_multiplier = 1.5\n\n return {\n 'approach': approach,\n 'description': description,\n 'timeline_multiplier': timeline_multiplier,\n 'phases': self._generate_approach_phases(approach)\n }\n\n def _generate_approach_phases(self, approach: str) -> List[str]:\n \"\"\"\n Generate phase descriptions for migration approach.\n\n Args:\n approach: Migration approach type\n\n Returns:\n List of phase descriptions\n \"\"\"\n phases = {\n 'direct_migration': [\n 'Phase 1: Set up target environment and migrate configuration',\n 'Phase 2: Migrate codebase and dependencies',\n 'Phase 3: Migrate data with validation',\n 'Phase 4: Comprehensive testing',\n 'Phase 5: Cutover and monitoring'\n ],\n 'phased_migration': [\n 'Phase 1: Identify and prioritize components for migration',\n 'Phase 2: Migrate non-critical components first',\n 'Phase 3: Migrate core components with parallel running',\n 'Phase 4: Migrate critical components with rollback plan',\n 'Phase 5: Decommission old system'\n ],\n 'strangler_pattern': [\n 'Phase 1: Set up routing layer between old and new systems',\n 'Phase 2: Implement new features in target technology only',\n 'Phase 3: Gradually migrate existing features (lowest risk first)',\n 'Phase 4: Migrate high-risk components last with extensive testing',\n 'Phase 5: Complete migration and remove routing layer'\n ]\n }\n\n return phases.get(approach, phases['phased_migration'])\n\n def _generate_migration_recommendation(\n self,\n complexity: Dict[str, float],\n effort: Dict[str, Any],\n risks: Dict[str, List[Dict[str, str]]]\n ) -> str:\n \"\"\"\n Generate overall migration recommendation.\n\n Args:\n complexity: Complexity analysis\n effort: Effort estimation\n risks: Risk assessment\n\n Returns:\n Recommendation string\n \"\"\"\n overall_complexity = complexity['overall_complexity']\n timeline_months = effort['estimated_timeline']['calendar_months']\n\n # Count high/critical severity risks\n high_risk_count = sum(\n 1 for risk_list in risks.values()\n for risk in risk_list\n if risk['severity'] in ['High', 'Critical']\n )\n\n if overall_complexity \u003c= 4 and high_risk_count \u003c= 2:\n return f\"Recommended - Low complexity migration achievable in {timeline_months:.1f} months with manageable risks\"\n elif overall_complexity \u003c= 7 and high_risk_count \u003c= 4:\n return f\"Proceed with caution - Moderate complexity migration requiring {timeline_months:.1f} months and careful risk management\"\n else:\n return f\"High risk - Complex migration requiring {timeline_months:.1f} months. Consider: incremental approach, additional resources, or alternative solutions\"\n\n def _define_success_criteria(self) -> List[str]:\n \"\"\"\n Define success criteria for migration.\n\n Returns:\n List of success criteria\n \"\"\"\n return [\n 'Feature parity with current system',\n 'Performance equal or better than current system',\n 'Zero data loss or corruption',\n 'All tests passing (unit, integration, E2E)',\n 'Successful production deployment with \u003c1% error rate',\n 'Team trained and comfortable with new technology',\n 'Documentation complete and up-to-date'\n ]\n","content_type":"text/x-python; charset=utf-8","language":"python","size":20589,"content_sha256":"acdacce2d54ade06ae6230e19638d74c706d9c5e8620bbd42dce7c76dbbe7790"},{"filename":"README.md","content":"# Technology Stack Evaluator - Comprehensive Tech Decision Support\n\n**Version**: 1.0.0\n**Author**: Claude Skills Factory\n**Category**: Engineering & Architecture\n**Last Updated**: 2025-11-05\n\n---\n\n## Overview\n\nThe **Technology Stack Evaluator** skill provides comprehensive, data-driven evaluation and comparison of technologies, frameworks, cloud providers, and complete technology stacks. It helps engineering teams make informed decisions about technology adoption, migration, and architecture choices.\n\n### Key Features\n\n- **8 Comprehensive Evaluation Capabilities**: Technology comparison, stack evaluation, maturity analysis, TCO calculation, security assessment, migration path analysis, cloud provider comparison, and decision reporting\n\n- **Flexible Input Formats**: Automatic detection and parsing of text, YAML, JSON, and URLs\n\n- **Context-Aware Output**: Adapts to Claude Desktop (rich markdown) or CLI (terminal-friendly)\n\n- **Modular Analysis**: Choose which sections to run (quick comparison vs comprehensive report)\n\n- **Token-Efficient**: Executive summaries (200-300 tokens) with progressive disclosure for details\n\n- **Intelligent Recommendations**: Data-driven with confidence scores and clear decision factors\n\n---\n\n## What This Skill Does\n\n### 1. Technology Comparison\nCompare frameworks, languages, and tools head-to-head:\n- React vs Vue vs Svelte vs Angular\n- PostgreSQL vs MongoDB vs MySQL\n- Node.js vs Python vs Go for APIs\n- AWS vs Azure vs GCP\n\n**Outputs**: Weighted decision matrix, pros/cons, confidence scores\n\n### 2. Stack Evaluation\nAssess complete technology stacks for specific use cases:\n- Real-time collaboration platforms\n- API-heavy SaaS applications\n- Data-intensive applications\n- Enterprise systems\n\n**Outputs**: Stack health assessment, compatibility analysis, recommendations\n\n### 3. Maturity & Ecosystem Analysis\nEvaluate technology health and long-term viability:\n- **GitHub Metrics**: Stars, forks, contributors, commit frequency\n- **npm Metrics**: Downloads, version stability, dependencies\n- **Community Health**: Stack Overflow, job market, tutorials\n- **Viability Assessment**: Corporate backing, sustainability, risk scoring\n\n**Outputs**: Health score (0-100), viability level, risk factors, strengths\n\n### 4. Total Cost of Ownership (TCO)\nCalculate comprehensive 3-5 year costs:\n- **Initial**: Licensing, training, migration, setup\n- **Operational**: Hosting, support, maintenance (yearly projections)\n- **Scaling**: Per-user costs, infrastructure scaling\n- **Hidden**: Technical debt, vendor lock-in, downtime, turnover\n- **Productivity**: Time-to-market impact, ROI\n\n**Outputs**: Total TCO, yearly breakdown, cost drivers, optimization opportunities\n\n### 5. Security & Compliance\nAnalyze security posture and compliance readiness:\n- **Vulnerability Analysis**: CVE counts by severity (Critical/High/Medium/Low)\n- **Security Scoring**: 0-100 with letter grade\n- **Compliance Assessment**: GDPR, SOC2, HIPAA, PCI-DSS readiness\n- **Patch Responsiveness**: Average time to patch critical vulnerabilities\n\n**Outputs**: Security score, compliance gaps, recommendations\n\n### 6. Migration Path Analysis\nAssess migration complexity and planning:\n- **Complexity Scoring**: 1-10 across 6 factors (code volume, architecture, data, APIs, dependencies, testing)\n- **Effort Estimation**: Person-months, timeline, phase breakdown\n- **Risk Assessment**: Technical, business, and team risks with mitigations\n- **Migration Strategy**: Direct, phased, or strangler pattern\n\n**Outputs**: Migration plan, timeline, risks, success criteria\n\n### 7. Cloud Provider Comparison\nCompare AWS vs Azure vs GCP for specific workloads:\n- Weighted decision criteria\n- Workload-specific optimizations\n- Cost comparisons\n- Feature parity analysis\n\n**Outputs**: Provider recommendation, cost comparison, feature matrix\n\n### 8. Decision Reports\nGenerate comprehensive decision documentation:\n- Executive summaries (200-300 tokens)\n- Detailed analysis (800-1500 tokens)\n- Decision matrices with confidence levels\n- Exportable markdown reports\n\n**Outputs**: Multi-format reports adapted to context\n\n---\n\n## File Structure\n\n```\ntech-stack-evaluator/\n├── SKILL.md # Main skill definition (YAML + documentation)\n├── README.md # This file - comprehensive guide\n├── HOW_TO_USE.md # Usage examples and patterns\n│\n├── stack_comparator.py # Comparison engine with weighted scoring\n├── tco_calculator.py # Total Cost of Ownership calculations\n├── ecosystem_analyzer.py # Ecosystem health and viability assessment\n├── security_assessor.py # Security and compliance analysis\n├── migration_analyzer.py # Migration path and complexity analysis\n├── format_detector.py # Automatic input format detection\n├── report_generator.py # Context-aware report generation\n│\n├── sample_input_text.json # Conversational input example\n├── sample_input_structured.json # JSON structured input example\n├── sample_input_tco.json # TCO analysis input example\n└── expected_output_comparison.json # Sample output structure\n```\n\n### Python Modules (7 files)\n\n1. **`stack_comparator.py`** (355 lines)\n - Weighted scoring algorithm\n - Feature matrices\n - Pros/cons generation\n - Recommendation engine with confidence calculation\n\n2. **`tco_calculator.py`** (403 lines)\n - Initial costs (licensing, training, migration)\n - Operational costs with growth projections\n - Scaling cost analysis\n - Hidden costs (technical debt, vendor lock-in, downtime)\n - Productivity impact and ROI\n\n3. **`ecosystem_analyzer.py`** (419 lines)\n - GitHub health scoring (stars, forks, commits, issues)\n - npm health scoring (downloads, versions, dependencies)\n - Community health (Stack Overflow, jobs, tutorials)\n - Corporate backing assessment\n - Viability risk analysis\n\n4. **`security_assessor.py`** (406 lines)\n - Vulnerability scoring (CVE analysis)\n - Patch responsiveness assessment\n - Security features evaluation\n - Compliance readiness (GDPR, SOC2, HIPAA, PCI-DSS)\n - Risk level determination\n\n5. **`migration_analyzer.py`** (485 lines)\n - Complexity scoring (6 factors: code, architecture, data, APIs, dependencies, testing)\n - Effort estimation (person-months, timeline)\n - Risk assessment (technical, business, team)\n - Migration strategy recommendation (direct, phased, strangler)\n - Success criteria definition\n\n6. **`format_detector.py`** (334 lines)\n - Automatic format detection (JSON, YAML, URLs, text)\n - Multi-format parsing\n - Technology name extraction\n - Use case inference\n - Priority detection\n\n7. **`report_generator.py`** (372 lines)\n - Context detection (Desktop vs CLI)\n - Executive summary generation (200-300 tokens)\n - Full report generation with modular sections\n - Rich markdown (Desktop) vs ASCII tables (CLI)\n - Export to file functionality\n\n**Total**: ~2,774 lines of Python code\n\n---\n\n## Installation\n\n### Claude Code (Project-Level)\n```bash\n# Navigate to your project\ncd /path/to/your/project\n\n# Create skills directory if it doesn't exist\nmkdir -p .claude/skills\n\n# Copy the skill folder\ncp -r /path/to/tech-stack-evaluator .claude/skills/\n```\n\n### Claude Code (User-Level, All Projects)\n```bash\n# Create user-level skills directory\nmkdir -p ~/.claude/skills\n\n# Copy the skill folder\ncp -r /path/to/tech-stack-evaluator ~/.claude/skills/\n```\n\n### Claude Desktop\n1. Locate the skill ZIP file: `tech-stack-evaluator.zip`\n2. Drag and drop the ZIP into Claude Desktop\n3. The skill will be automatically loaded\n\n### Claude Apps (Browser)\nUse the `skill-creator` skill to import the ZIP file, or manually copy files to your project's `.claude/skills/` directory.\n\n### API Usage\n```bash\n# Upload skill via API\ncurl -X POST https://api.anthropic.com/v1/skills \\\n -H \"Authorization: Bearer $ANTHROPIC_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d @tech-stack-evaluator.zip\n```\n\n---\n\n## Quick Start\n\n### 1. Simple Comparison (Text Input)\n```\n\"Compare React vs Vue for a SaaS dashboard\"\n```\n\n**Output**: Executive summary with recommendation, pros/cons, confidence score\n\n### 2. TCO Analysis (Structured Input)\n```json\n{\n \"tco_analysis\": {\n \"technology\": \"AWS\",\n \"team_size\": 8,\n \"timeline_years\": 5,\n \"operational_costs\": {\n \"monthly_hosting\": 3000\n }\n }\n}\n```\n\n**Output**: 5-year TCO breakdown with cost optimization suggestions\n\n### 3. Migration Assessment\n```\n\"Assess migration from Angular.js to React. Codebase: 50,000 lines, 200 components, 6-person team.\"\n```\n\n**Output**: Complexity score, effort estimate, timeline, risk assessment, migration plan\n\n### 4. Security & Compliance\n```\n\"Analyze security of Express.js + MongoDB stack. Need SOC2 compliance.\"\n```\n\n**Output**: Security score, vulnerability analysis, compliance gaps, recommendations\n\n---\n\n## Usage Examples\n\nSee **[HOW_TO_USE.md](HOW_TO_USE.md)** for comprehensive examples including:\n- 6 real-world scenarios\n- All input format examples\n- Advanced usage patterns\n- Tips for best results\n- Common questions and troubleshooting\n\n---\n\n## Metrics and Calculations\n\n### Scoring Algorithms\n\n**Technology Comparison (0-100 scale)**:\n- 8 weighted criteria (performance, scalability, developer experience, ecosystem, learning curve, documentation, community, enterprise readiness)\n- User-defined weights (defaults provided)\n- Use-case specific adjustments (e.g., real-time workloads get performance bonus)\n- Confidence calculation based on score gap\n\n**Ecosystem Health (0-100 scale)**:\n- GitHub: Stars, forks, contributors, commit frequency\n- npm: Weekly downloads, version stability, dependencies count\n- Community: Stack Overflow questions, job postings, tutorials, forums\n- Corporate backing: Funding, company type\n- Maintenance: Issue response time, resolution rate, release frequency\n\n**Security Score (0-100 scale, A-F grade)**:\n- Vulnerability count and severity (CVE database)\n- Patch responsiveness (days to patch critical/high)\n- Security features (encryption, auth, logging, etc.)\n- Track record (years since major incident, certifications, audits)\n\n**Migration Complexity (1-10 scale)**:\n- Code volume (lines of code, files, components)\n- Architecture changes (minimal to complete rewrite)\n- Data migration (database size, schema changes)\n- API compatibility (breaking changes)\n- Dependency changes (percentage to replace)\n- Testing requirements (coverage, test count)\n\n### Financial Calculations\n\n**TCO Components**:\n- Initial: Licensing + Training (hours × rate × team size) + Migration + Setup + Tooling\n- Operational (yearly): Licensing + Hosting (with growth) + Support + Maintenance (dev hours)\n- Scaling: User projections × cost per user, Infrastructure scaling\n- Hidden: Technical debt (15-20% of dev time) + Vendor lock-in risk + Security incidents + Downtime + Turnover\n\n**ROI Calculation**:\n- Productivity value = (Additional features per year) × (Feature value)\n- Net TCO = Total TCO - Productivity value\n- Break-even analysis\n\n### Compliance Assessment\n\n**Standards Supported**: GDPR, SOC2, HIPAA, PCI-DSS\n\n**Readiness Levels**:\n- **Ready (90-100%)**: Compliant, minor verification needed\n- **Mostly Ready (70-89%)**: Minor gaps, additional configuration\n- **Partial (50-69%)**: Significant work required\n- **Not Ready (\u003c50%)**: Major gaps, extensive implementation\n\n**Required Features per Standard**:\n- **GDPR**: Data privacy, consent management, data portability, right to deletion, audit logging\n- **SOC2**: Access controls, encryption (at rest + transit), audit logging, backup/recovery\n- **HIPAA**: PHI protection, encryption, access controls, audit logging\n- **PCI-DSS**: Payment data encryption, access controls, network security, vulnerability management\n\n---\n\n## Best Practices\n\n### For Accurate Evaluations\n1. **Define Clear Use Case**: \"Real-time collaboration platform\" > \"web app\"\n2. **Provide Complete Context**: Team size, skills, constraints, timeline\n3. **Set Realistic Priorities**: Use weighted criteria (total = 100%)\n4. **Consider Team Skills**: Factor in learning curve and existing expertise\n5. **Think Long-Term**: Evaluate 3-5 year outlook\n\n### For TCO Analysis\n1. **Include All Costs**: Don't forget training, migration, technical debt\n2. **Realistic Scaling**: Base on actual growth metrics\n3. **Developer Productivity**: Time-to-market is a critical cost factor\n4. **Hidden Costs**: Vendor lock-in, exit costs, technical debt\n5. **Document Assumptions**: Make TCO assumptions explicit\n\n### For Migration Decisions\n1. **Risk Assessment First**: Identify showstoppers early\n2. **Incremental Migration**: Avoid big-bang rewrites\n3. **Prototype Critical Paths**: Test complex scenarios\n4. **Rollback Plans**: Always have fallback strategy\n5. **Baseline Metrics**: Measure current performance before migration\n\n### For Security Evaluation\n1. **Recent Vulnerabilities**: Focus on last 12 months\n2. **Patch Response Time**: Fast patching > zero vulnerabilities\n3. **Validate Claims**: Vendor claims ≠ actual compliance\n4. **Supply Chain**: Evaluate security of all dependencies\n5. **Test Features**: Don't assume features work as documented\n\n---\n\n## Limitations\n\n### Data Accuracy\n- **Ecosystem metrics**: Point-in-time snapshots (GitHub/npm data changes rapidly)\n- **TCO calculations**: Estimates based on assumptions and market rates\n- **Benchmark data**: May not reflect your specific configuration\n- **Vulnerability data**: Depends on public CVE database completeness\n\n### Scope Boundaries\n- **Industry-specific requirements**: Some specialized needs not covered by standard analysis\n- **Emerging technologies**: Very new tech (\u003c1 year) may lack sufficient data\n- **Custom/proprietary solutions**: Cannot evaluate closed-source tools without data\n- **Organizational factors**: Cannot account for politics, vendor relationships, legacy commitments\n\n### When NOT to Use\n- **Trivial decisions**: Nearly-identical tools (use team preference)\n- **Mandated solutions**: Technology choice already decided\n- **Insufficient context**: Unknown requirements or priorities\n- **Real-time production**: Use for planning, not emergencies\n- **Non-technical decisions**: Business strategy, hiring, org issues\n\n---\n\n## Confidence Levels\n\nAll recommendations include confidence scores (0-100%):\n\n- **High (80-100%)**: Strong data, clear winner, low risk\n- **Medium (50-79%)**: Good data, trade-offs present, moderate risk\n- **Low (\u003c50%)**: Limited data, close call, high uncertainty\n- **Insufficient Data**: Cannot recommend without more information\n\n**Confidence based on**:\n- Data completeness and recency\n- Consensus across multiple metrics\n- Clarity of use case requirements\n- Industry maturity and standards\n\n---\n\n## Output Examples\n\n### Executive Summary (200-300 tokens)\n```markdown\n# Technology Evaluation: React vs Vue\n\n## Recommendation\n**React is recommended for your SaaS dashboard project**\n*Confidence: 78%*\n\n### Top Strengths\n- Larger ecosystem with 2.5× more packages available\n- Stronger corporate backing (Meta) ensures long-term viability\n- Higher job market demand (3× more job postings)\n\n### Key Concerns\n- Steeper learning curve (score: 65 vs Vue's 80)\n- More complex state management patterns\n- Requires additional libraries for routing, forms\n\n### Decision Factors\n- **Ecosystem**: React (score: 95)\n- **Developer Experience**: Vue (score: 88)\n- **Community Support**: React (score: 92)\n```\n\n### Comparison Matrix (Desktop)\n```markdown\n| Category | Weight | React | Vue |\n|-----------------------|--------|-------|-------|\n| Performance | 15% | 85.0 | 87.0 |\n| Scalability | 15% | 90.0 | 85.0 |\n| Developer Experience | 20% | 80.0 | 88.0 |\n| Ecosystem | 15% | 95.0 | 82.0 |\n| Learning Curve | 10% | 65.0 | 80.0 |\n| Documentation | 10% | 92.0 | 90.0 |\n| Community Support | 10% | 92.0 | 85.0 |\n| Enterprise Readiness | 5% | 95.0 | 80.0 |\n| **WEIGHTED TOTAL** | 100% | 85.3 | 84.9 |\n```\n\n### TCO Summary\n```markdown\n## Total Cost of Ownership: AWS (5 years)\n\n**Total TCO**: $1,247,500\n**Net TCO (after productivity gains)**: $987,300\n**Average Yearly**: $249,500\n\n### Initial Investment: $125,000\n- Training: $40,000 (10 devs × 40 hours × $100/hr)\n- Migration: $50,000\n- Setup & Tooling: $35,000\n\n### Key Cost Drivers\n- Infrastructure/hosting ($625,000 over 5 years)\n- Developer maintenance time ($380,000)\n- Technical debt accumulation ($87,500)\n\n### Optimization Opportunities\n- Improve scaling efficiency - costs growing 25% YoY\n- Address technical debt accumulation\n- Consider reserved instances for 30% hosting savings\n```\n\n---\n\n## Version History\n\n### v1.0.0 (2025-11-05)\n- Initial release\n- 8 comprehensive evaluation capabilities\n- 7 Python modules (2,774 lines)\n- Automatic format detection (text, YAML, JSON, URLs)\n- Context-aware output (Desktop vs CLI)\n- Modular reporting with progressive disclosure\n- Complete documentation with 6+ usage examples\n\n---\n\n## Dependencies\n\n**Python Standard Library Only** - No external dependencies required:\n- `typing` - Type hints\n- `json` - JSON parsing\n- `re` - Regular expressions\n- `datetime` - Date/time operations\n- `os` - Environment detection\n- `platform` - Platform information\n\n**Why no external dependencies?**\n- Ensures compatibility across all Claude environments\n- No installation or version conflicts\n- Faster loading and execution\n- Simpler deployment\n\n---\n\n## Support and Feedback\n\n### Getting Help\n1. Review **[HOW_TO_USE.md](HOW_TO_USE.md)** for detailed examples\n2. Check sample input files for format references\n3. Start with conversational text input (easiest)\n4. Request specific sections if full report is overwhelming\n\n### Improving Results\nIf recommendations don't match expectations:\n- **Clarify use case**: Be more specific about requirements\n- **Adjust priorities**: Set custom weights for criteria\n- **Provide more context**: Team skills, constraints, business goals\n- **Request specific sections**: Focus on most relevant analyses\n\n### Known Issues\n- Very new technologies (\u003c6 months) may have limited ecosystem data\n- Proprietary/closed-source tools require manual data input\n- Compliance assessment is guidance, not legal certification\n\n---\n\n## Contributing\n\nThis skill is part of the Claude Skills Factory. To contribute improvements:\n1. Test changes with multiple scenarios\n2. Maintain Python standard library only (no external deps)\n3. Update documentation to match code changes\n4. Preserve token efficiency (200-300 token summaries)\n5. Validate all calculations with real-world data\n\n---\n\n## License\n\nPart of Claude Skills Factory\n© 2025 Claude Skills Factory\nLicensed under MIT License\n\n---\n\n## Related Skills\n\n- **prompt-factory**: Generate domain-specific prompts\n- **aws-solution-architect**: AWS-specific architecture evaluation\n- **psychology-advisor**: Decision-making psychology\n- **content-researcher**: Technology trend research\n\n---\n\n**Ready to evaluate your tech stack?** See [HOW_TO_USE.md](HOW_TO_USE.md) for quick start examples!\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":19245,"content_sha256":"7fb7b32dd5874443df87d8ba68cd47222a6a748b42f130d862930b23fb1beed0"},{"filename":"report_generator.py","content":"\"\"\"\nReport Generator - Context-aware report generation with progressive disclosure.\n\nGenerates reports adapted for Claude Desktop (rich markdown) or CLI (terminal-friendly),\nwith executive summaries and detailed breakdowns on demand.\n\"\"\"\n\nfrom typing import Dict, List, Any, Optional\nimport os\nimport platform\n\n\nclass ReportGenerator:\n \"\"\"Generate context-aware technology evaluation reports.\"\"\"\n\n def __init__(self, report_data: Dict[str, Any], output_context: Optional[str] = None):\n \"\"\"\n Initialize report generator.\n\n Args:\n report_data: Complete evaluation data\n output_context: 'desktop', 'cli', or None for auto-detect\n \"\"\"\n self.report_data = report_data\n self.output_context = output_context or self._detect_context()\n\n def _detect_context(self) -> str:\n \"\"\"\n Detect output context (Desktop vs CLI).\n\n Returns:\n Context type: 'desktop' or 'cli'\n \"\"\"\n # Check for Claude Desktop environment variables or indicators\n # This is a simplified detection - actual implementation would check for\n # Claude Desktop-specific environment variables\n\n if os.getenv('CLAUDE_DESKTOP'):\n return 'desktop'\n\n # Check if running in terminal\n if os.isatty(1): # stdout is a terminal\n return 'cli'\n\n # Default to desktop for rich formatting\n return 'desktop'\n\n def generate_executive_summary(self, max_tokens: int = 300) -> str:\n \"\"\"\n Generate executive summary (200-300 tokens).\n\n Args:\n max_tokens: Maximum tokens for summary\n\n Returns:\n Executive summary markdown\n \"\"\"\n summary_parts = []\n\n # Title\n technologies = self.report_data.get('technologies', [])\n tech_names = ', '.join(technologies[:3]) # First 3\n summary_parts.append(f\"# Technology Evaluation: {tech_names}\\n\")\n\n # Recommendation\n recommendation = self.report_data.get('recommendation', {})\n rec_text = recommendation.get('text', 'No recommendation available')\n confidence = recommendation.get('confidence', 0)\n\n summary_parts.append(f\"## Recommendation\\n\")\n summary_parts.append(f\"**{rec_text}**\\n\")\n summary_parts.append(f\"*Confidence: {confidence:.0f}%*\\n\")\n\n # Top 3 Pros\n pros = recommendation.get('pros', [])[:3]\n if pros:\n summary_parts.append(f\"\\n### Top Strengths\\n\")\n for pro in pros:\n summary_parts.append(f\"- {pro}\\n\")\n\n # Top 3 Cons\n cons = recommendation.get('cons', [])[:3]\n if cons:\n summary_parts.append(f\"\\n### Key Concerns\\n\")\n for con in cons:\n summary_parts.append(f\"- {con}\\n\")\n\n # Key Decision Factors\n decision_factors = self.report_data.get('decision_factors', [])[:3]\n if decision_factors:\n summary_parts.append(f\"\\n### Decision Factors\\n\")\n for factor in decision_factors:\n category = factor.get('category', 'Unknown')\n best = factor.get('best_performer', 'Unknown')\n summary_parts.append(f\"- **{category.replace('_', ' ').title()}**: {best}\\n\")\n\n summary_parts.append(f\"\\n---\\n\")\n summary_parts.append(f\"*For detailed analysis, request full report sections*\\n\")\n\n return ''.join(summary_parts)\n\n def generate_full_report(self, sections: Optional[List[str]] = None) -> str:\n \"\"\"\n Generate complete report with selected sections.\n\n Args:\n sections: List of sections to include, or None for all\n\n Returns:\n Complete report markdown\n \"\"\"\n if sections is None:\n sections = self._get_available_sections()\n\n report_parts = []\n\n # Title and metadata\n report_parts.append(self._generate_title())\n\n # Generate each requested section\n for section in sections:\n section_content = self._generate_section(section)\n if section_content:\n report_parts.append(section_content)\n\n return '\\n\\n'.join(report_parts)\n\n def _get_available_sections(self) -> List[str]:\n \"\"\"\n Get list of available report sections.\n\n Returns:\n List of section names\n \"\"\"\n sections = ['executive_summary']\n\n if 'comparison_matrix' in self.report_data:\n sections.append('comparison_matrix')\n\n if 'tco_analysis' in self.report_data:\n sections.append('tco_analysis')\n\n if 'ecosystem_health' in self.report_data:\n sections.append('ecosystem_health')\n\n if 'security_assessment' in self.report_data:\n sections.append('security_assessment')\n\n if 'migration_analysis' in self.report_data:\n sections.append('migration_analysis')\n\n if 'performance_benchmarks' in self.report_data:\n sections.append('performance_benchmarks')\n\n return sections\n\n def _generate_title(self) -> str:\n \"\"\"Generate report title section.\"\"\"\n technologies = self.report_data.get('technologies', [])\n tech_names = ' vs '.join(technologies)\n use_case = self.report_data.get('use_case', 'General Purpose')\n\n if self.output_context == 'desktop':\n return f\"\"\"# Technology Stack Evaluation Report\n\n**Technologies**: {tech_names}\n**Use Case**: {use_case}\n**Generated**: {self._get_timestamp()}\n\n---\n\"\"\"\n else: # CLI\n return f\"\"\"================================================================================\nTECHNOLOGY STACK EVALUATION REPORT\n================================================================================\n\nTechnologies: {tech_names}\nUse Case: {use_case}\nGenerated: {self._get_timestamp()}\n\n================================================================================\n\"\"\"\n\n def _generate_section(self, section_name: str) -> Optional[str]:\n \"\"\"\n Generate specific report section.\n\n Args:\n section_name: Name of section to generate\n\n Returns:\n Section markdown or None\n \"\"\"\n generators = {\n 'executive_summary': self._section_executive_summary,\n 'comparison_matrix': self._section_comparison_matrix,\n 'tco_analysis': self._section_tco_analysis,\n 'ecosystem_health': self._section_ecosystem_health,\n 'security_assessment': self._section_security_assessment,\n 'migration_analysis': self._section_migration_analysis,\n 'performance_benchmarks': self._section_performance_benchmarks\n }\n\n generator = generators.get(section_name)\n if generator:\n return generator()\n\n return None\n\n def _section_executive_summary(self) -> str:\n \"\"\"Generate executive summary section.\"\"\"\n return self.generate_executive_summary()\n\n def _section_comparison_matrix(self) -> str:\n \"\"\"Generate comparison matrix section.\"\"\"\n matrix_data = self.report_data.get('comparison_matrix', [])\n if not matrix_data:\n return \"\"\n\n if self.output_context == 'desktop':\n return self._render_matrix_desktop(matrix_data)\n else:\n return self._render_matrix_cli(matrix_data)\n\n def _render_matrix_desktop(self, matrix_data: List[Dict[str, Any]]) -> str:\n \"\"\"Render comparison matrix for desktop (rich markdown table).\"\"\"\n parts = [\"## Comparison Matrix\\n\"]\n\n if not matrix_data:\n return \"\"\n\n # Get technology names from first row\n tech_names = list(matrix_data[0].get('scores', {}).keys())\n\n # Build table header\n header = \"| Category | Weight |\"\n for tech in tech_names:\n header += f\" {tech} |\"\n parts.append(header)\n\n # Separator\n separator = \"|----------|--------|\"\n separator += \"--------|\" * len(tech_names)\n parts.append(separator)\n\n # Rows\n for row in matrix_data:\n category = row.get('category', '').replace('_', ' ').title()\n weight = row.get('weight', '')\n scores = row.get('scores', {})\n\n row_str = f\"| {category} | {weight} |\"\n for tech in tech_names:\n score = scores.get(tech, '0.0')\n row_str += f\" {score} |\"\n\n parts.append(row_str)\n\n return '\\n'.join(parts)\n\n def _render_matrix_cli(self, matrix_data: List[Dict[str, Any]]) -> str:\n \"\"\"Render comparison matrix for CLI (ASCII table).\"\"\"\n parts = [\"COMPARISON MATRIX\", \"=\" * 80, \"\"]\n\n if not matrix_data:\n return \"\"\n\n # Get technology names\n tech_names = list(matrix_data[0].get('scores', {}).keys())\n\n # Calculate column widths\n category_width = 25\n weight_width = 8\n score_width = 10\n\n # Header\n header = f\"{'Category':\u003c{category_width}} {'Weight':\u003c{weight_width}}\"\n for tech in tech_names:\n header += f\" {tech[:score_width-1]:\u003c{score_width}}\"\n parts.append(header)\n parts.append(\"-\" * 80)\n\n # Rows\n for row in matrix_data:\n category = row.get('category', '').replace('_', ' ').title()[:category_width-1]\n weight = row.get('weight', '')\n scores = row.get('scores', {})\n\n row_str = f\"{category:\u003c{category_width}} {weight:\u003c{weight_width}}\"\n for tech in tech_names:\n score = scores.get(tech, '0.0')\n row_str += f\" {score:\u003c{score_width}}\"\n\n parts.append(row_str)\n\n return '\\n'.join(parts)\n\n def _section_tco_analysis(self) -> str:\n \"\"\"Generate TCO analysis section.\"\"\"\n tco_data = self.report_data.get('tco_analysis', {})\n if not tco_data:\n return \"\"\n\n parts = [\"## Total Cost of Ownership Analysis\\n\"]\n\n # Summary\n total_tco = tco_data.get('total_tco', 0)\n timeline = tco_data.get('timeline_years', 5)\n avg_yearly = tco_data.get('average_yearly_cost', 0)\n\n parts.append(f\"**{timeline}-Year Total**: ${total_tco:,.2f}\")\n parts.append(f\"**Average Yearly**: ${avg_yearly:,.2f}\\n\")\n\n # Cost breakdown\n initial = tco_data.get('initial_costs', {})\n parts.append(f\"### Initial Costs: ${initial.get('total_initial', 0):,.2f}\")\n\n # Operational costs\n operational = tco_data.get('operational_costs', {})\n if operational:\n parts.append(f\"\\n### Operational Costs (Yearly)\")\n yearly_totals = operational.get('total_yearly', [])\n for year, cost in enumerate(yearly_totals, 1):\n parts.append(f\"- Year {year}: ${cost:,.2f}\")\n\n return '\\n'.join(parts)\n\n def _section_ecosystem_health(self) -> str:\n \"\"\"Generate ecosystem health section.\"\"\"\n ecosystem_data = self.report_data.get('ecosystem_health', {})\n if not ecosystem_data:\n return \"\"\n\n parts = [\"## Ecosystem Health Analysis\\n\"]\n\n # Overall score\n overall_score = ecosystem_data.get('overall_health', 0)\n parts.append(f\"**Overall Health Score**: {overall_score:.1f}/100\\n\")\n\n # Component scores\n scores = ecosystem_data.get('health_scores', {})\n parts.append(\"### Health Metrics\")\n for metric, score in scores.items():\n if metric != 'overall_health':\n metric_name = metric.replace('_', ' ').title()\n parts.append(f\"- {metric_name}: {score:.1f}/100\")\n\n # Viability assessment\n viability = ecosystem_data.get('viability_assessment', {})\n if viability:\n parts.append(f\"\\n### Viability: {viability.get('overall_viability', 'Unknown')}\")\n parts.append(f\"**Risk Level**: {viability.get('risk_level', 'Unknown')}\")\n\n return '\\n'.join(parts)\n\n def _section_security_assessment(self) -> str:\n \"\"\"Generate security assessment section.\"\"\"\n security_data = self.report_data.get('security_assessment', {})\n if not security_data:\n return \"\"\n\n parts = [\"## Security & Compliance Assessment\\n\"]\n\n # Security score\n security_score = security_data.get('security_score', {})\n overall = security_score.get('overall_security_score', 0)\n grade = security_score.get('security_grade', 'N/A')\n\n parts.append(f\"**Security Score**: {overall:.1f}/100 (Grade: {grade})\\n\")\n\n # Compliance\n compliance = security_data.get('compliance_assessment', {})\n if compliance:\n parts.append(\"### Compliance Readiness\")\n for standard, assessment in compliance.items():\n level = assessment.get('readiness_level', 'Unknown')\n pct = assessment.get('readiness_percentage', 0)\n parts.append(f\"- **{standard}**: {level} ({pct:.0f}%)\")\n\n return '\\n'.join(parts)\n\n def _section_migration_analysis(self) -> str:\n \"\"\"Generate migration analysis section.\"\"\"\n migration_data = self.report_data.get('migration_analysis', {})\n if not migration_data:\n return \"\"\n\n parts = [\"## Migration Path Analysis\\n\"]\n\n # Complexity\n complexity = migration_data.get('complexity_analysis', {})\n overall_complexity = complexity.get('overall_complexity', 0)\n parts.append(f\"**Migration Complexity**: {overall_complexity:.1f}/10\\n\")\n\n # Effort estimation\n effort = migration_data.get('effort_estimation', {})\n if effort:\n total_hours = effort.get('total_hours', 0)\n person_months = effort.get('total_person_months', 0)\n timeline = effort.get('estimated_timeline', {})\n calendar_months = timeline.get('calendar_months', 0)\n\n parts.append(f\"### Effort Estimate\")\n parts.append(f\"- Total Effort: {person_months:.1f} person-months ({total_hours:.0f} hours)\")\n parts.append(f\"- Timeline: {calendar_months:.1f} calendar months\")\n\n # Recommended approach\n approach = migration_data.get('recommended_approach', {})\n if approach:\n parts.append(f\"\\n### Recommended Approach: {approach.get('approach', 'Unknown').replace('_', ' ').title()}\")\n parts.append(f\"{approach.get('description', '')}\")\n\n return '\\n'.join(parts)\n\n def _section_performance_benchmarks(self) -> str:\n \"\"\"Generate performance benchmarks section.\"\"\"\n benchmark_data = self.report_data.get('performance_benchmarks', {})\n if not benchmark_data:\n return \"\"\n\n parts = [\"## Performance Benchmarks\\n\"]\n\n # Throughput\n throughput = benchmark_data.get('throughput', {})\n if throughput:\n parts.append(\"### Throughput\")\n for tech, rps in throughput.items():\n parts.append(f\"- {tech}: {rps:,} requests/sec\")\n\n # Latency\n latency = benchmark_data.get('latency', {})\n if latency:\n parts.append(\"\\n### Latency (P95)\")\n for tech, ms in latency.items():\n parts.append(f\"- {tech}: {ms}ms\")\n\n return '\\n'.join(parts)\n\n def _get_timestamp(self) -> str:\n \"\"\"Get current timestamp.\"\"\"\n from datetime import datetime\n return datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n\n def export_to_file(self, filename: str, sections: Optional[List[str]] = None) -> str:\n \"\"\"\n Export report to file.\n\n Args:\n filename: Output filename\n sections: Sections to include\n\n Returns:\n Path to exported file\n \"\"\"\n report = self.generate_full_report(sections)\n\n with open(filename, 'w', encoding='utf-8') as f:\n f.write(report)\n\n return filename\n","content_type":"text/x-python; charset=utf-8","language":"python","size":15891,"content_sha256":"07e94126bd655bfe30a7fac7b46b4b6aca356aad75bf7d7f4e670feb362ccdd1"},{"filename":"sample_input_structured.json","content":"{\n \"comparison\": {\n \"technologies\": [\n {\n \"name\": \"PostgreSQL\",\n \"performance\": {\"score\": 85},\n \"scalability\": {\"score\": 90},\n \"developer_experience\": {\"score\": 75},\n \"ecosystem\": {\"score\": 95},\n \"learning_curve\": {\"score\": 70},\n \"documentation\": {\"score\": 90},\n \"community_support\": {\"score\": 95},\n \"enterprise_readiness\": {\"score\": 95}\n },\n {\n \"name\": \"MongoDB\",\n \"performance\": {\"score\": 80},\n \"scalability\": {\"score\": 95},\n \"developer_experience\": {\"score\": 85},\n \"ecosystem\": {\"score\": 85},\n \"learning_curve\": {\"score\": 80},\n \"documentation\": {\"score\": 85},\n \"community_support\": {\"score\": 85},\n \"enterprise_readiness\": {\"score\": 75}\n }\n ],\n \"use_case\": \"SaaS application with complex queries\",\n \"weights\": {\n \"performance\": 20,\n \"scalability\": 20,\n \"developer_experience\": 15,\n \"ecosystem\": 15,\n \"learning_curve\": 10,\n \"documentation\": 10,\n \"community_support\": 5,\n \"enterprise_readiness\": 5\n }\n }\n}\n","content_type":"application/json; charset=utf-8","language":"json","size":1109,"content_sha256":"c53ac8ed82ce02646d86e43476fe51cecbeb9d6055f428464703e64da5885017"},{"filename":"sample_input_tco.json","content":"{\n \"tco_analysis\": {\n \"technology\": \"AWS\",\n \"team_size\": 10,\n \"timeline_years\": 5,\n \"initial_costs\": {\n \"licensing\": 0,\n \"training_hours_per_dev\": 40,\n \"developer_hourly_rate\": 100,\n \"training_materials\": 1000,\n \"migration\": 50000,\n \"setup\": 10000,\n \"tooling\": 5000\n },\n \"operational_costs\": {\n \"annual_licensing\": 0,\n \"monthly_hosting\": 5000,\n \"annual_support\": 20000,\n \"maintenance_hours_per_dev_monthly\": 20\n },\n \"scaling_params\": {\n \"initial_users\": 5000,\n \"annual_growth_rate\": 0.30,\n \"initial_servers\": 10,\n \"cost_per_server_monthly\": 300\n },\n \"productivity_factors\": {\n \"productivity_multiplier\": 1.2,\n \"time_to_market_reduction_days\": 15,\n \"avg_feature_time_days\": 45,\n \"avg_feature_value\": 15000,\n \"technical_debt_percentage\": 0.12,\n \"vendor_lock_in_risk\": \"medium\",\n \"security_incidents_per_year\": 0.3,\n \"avg_security_incident_cost\": 30000,\n \"downtime_hours_per_year\": 4,\n \"downtime_cost_per_hour\": 8000,\n \"annual_turnover_rate\": 0.12,\n \"cost_per_new_hire\": 35000\n }\n }\n}\n","content_type":"application/json; charset=utf-8","language":"json","size":1152,"content_sha256":"c1748e2805d6dae5430bef37b0c0906f0e19df168ed153734938fe323ee50ae7"},{"filename":"sample_input_text.json","content":"{\n \"format\": \"text\",\n \"input\": \"Compare React vs Vue for building a SaaS dashboard with real-time collaboration features. Our team has 8 developers, and we need to consider developer experience, ecosystem maturity, and performance.\"\n}\n","content_type":"application/json; charset=utf-8","language":"json","size":237,"content_sha256":"86f94c682225411c2dac3ce89d0d23b2ec8e683623f5d6767ba8016928c14ff0"},{"filename":"security_assessor.py","content":"\"\"\"\nSecurity and Compliance Assessor.\n\nAnalyzes security vulnerabilities, compliance readiness (GDPR, SOC2, HIPAA),\nand overall security posture of technology stacks.\n\"\"\"\n\nfrom typing import Dict, List, Any, Optional\nfrom datetime import datetime, timedelta\n\n\nclass SecurityAssessor:\n \"\"\"Assess security and compliance readiness of technology stacks.\"\"\"\n\n # Compliance standards mapping\n COMPLIANCE_STANDARDS = {\n 'GDPR': ['data_privacy', 'consent_management', 'data_portability', 'right_to_deletion', 'audit_logging'],\n 'SOC2': ['access_controls', 'encryption_at_rest', 'encryption_in_transit', 'audit_logging', 'backup_recovery'],\n 'HIPAA': ['phi_protection', 'encryption_at_rest', 'encryption_in_transit', 'access_controls', 'audit_logging'],\n 'PCI_DSS': ['payment_data_encryption', 'access_controls', 'network_security', 'vulnerability_management']\n }\n\n def __init__(self, security_data: Dict[str, Any]):\n \"\"\"\n Initialize security assessor with security data.\n\n Args:\n security_data: Dictionary containing vulnerability and compliance data\n \"\"\"\n self.technology = security_data.get('technology', 'Unknown')\n self.vulnerabilities = security_data.get('vulnerabilities', {})\n self.security_features = security_data.get('security_features', {})\n self.compliance_requirements = security_data.get('compliance_requirements', [])\n\n def calculate_security_score(self) -> Dict[str, Any]:\n \"\"\"\n Calculate overall security score (0-100).\n\n Returns:\n Dictionary with security score components\n \"\"\"\n # Component scores\n vuln_score = self._score_vulnerabilities()\n patch_score = self._score_patch_responsiveness()\n features_score = self._score_security_features()\n track_record_score = self._score_track_record()\n\n # Weighted average\n weights = {\n 'vulnerability_score': 0.30,\n 'patch_responsiveness': 0.25,\n 'security_features': 0.30,\n 'track_record': 0.15\n }\n\n overall = (\n vuln_score * weights['vulnerability_score'] +\n patch_score * weights['patch_responsiveness'] +\n features_score * weights['security_features'] +\n track_record_score * weights['track_record']\n )\n\n return {\n 'overall_security_score': overall,\n 'vulnerability_score': vuln_score,\n 'patch_responsiveness': patch_score,\n 'security_features_score': features_score,\n 'track_record_score': track_record_score,\n 'security_grade': self._calculate_grade(overall)\n }\n\n def _score_vulnerabilities(self) -> float:\n \"\"\"\n Score based on vulnerability count and severity.\n\n Returns:\n Vulnerability score (0-100, higher is better)\n \"\"\"\n # Get vulnerability counts by severity (last 12 months)\n critical = self.vulnerabilities.get('critical_last_12m', 0)\n high = self.vulnerabilities.get('high_last_12m', 0)\n medium = self.vulnerabilities.get('medium_last_12m', 0)\n low = self.vulnerabilities.get('low_last_12m', 0)\n\n # Calculate weighted vulnerability count\n weighted_vulns = (critical * 4) + (high * 2) + (medium * 1) + (low * 0.5)\n\n # Score based on weighted count (fewer is better)\n if weighted_vulns == 0:\n score = 100\n elif weighted_vulns \u003c= 5:\n score = 90\n elif weighted_vulns \u003c= 10:\n score = 80\n elif weighted_vulns \u003c= 20:\n score = 70\n elif weighted_vulns \u003c= 30:\n score = 60\n elif weighted_vulns \u003c= 50:\n score = 50\n else:\n score = max(0, 50 - (weighted_vulns - 50) / 2)\n\n # Penalty for critical vulnerabilities\n if critical > 0:\n score = max(0, score - (critical * 10))\n\n return max(0.0, min(100.0, score))\n\n def _score_patch_responsiveness(self) -> float:\n \"\"\"\n Score based on patch response time.\n\n Returns:\n Patch responsiveness score (0-100)\n \"\"\"\n # Average days to patch critical vulnerabilities\n critical_patch_days = self.vulnerabilities.get('avg_critical_patch_days', 30)\n high_patch_days = self.vulnerabilities.get('avg_high_patch_days', 60)\n\n # Score critical patch time (most important)\n if critical_patch_days \u003c= 7:\n critical_score = 50\n elif critical_patch_days \u003c= 14:\n critical_score = 40\n elif critical_patch_days \u003c= 30:\n critical_score = 30\n elif critical_patch_days \u003c= 60:\n critical_score = 20\n else:\n critical_score = 10\n\n # Score high severity patch time\n if high_patch_days \u003c= 14:\n high_score = 30\n elif high_patch_days \u003c= 30:\n high_score = 25\n elif high_patch_days \u003c= 60:\n high_score = 20\n elif high_patch_days \u003c= 90:\n high_score = 15\n else:\n high_score = 10\n\n # Has active security team\n has_security_team = self.vulnerabilities.get('has_security_team', False)\n team_score = 20 if has_security_team else 0\n\n total_score = critical_score + high_score + team_score\n\n return min(100.0, total_score)\n\n def _score_security_features(self) -> float:\n \"\"\"\n Score based on built-in security features.\n\n Returns:\n Security features score (0-100)\n \"\"\"\n score = 0.0\n\n # Essential features (10 points each)\n essential_features = [\n 'encryption_at_rest',\n 'encryption_in_transit',\n 'authentication',\n 'authorization',\n 'input_validation'\n ]\n\n for feature in essential_features:\n if self.security_features.get(feature, False):\n score += 10\n\n # Advanced features (5 points each)\n advanced_features = [\n 'rate_limiting',\n 'csrf_protection',\n 'xss_protection',\n 'sql_injection_protection',\n 'audit_logging',\n 'mfa_support',\n 'rbac',\n 'secrets_management',\n 'security_headers',\n 'cors_configuration'\n ]\n\n for feature in advanced_features:\n if self.security_features.get(feature, False):\n score += 5\n\n return min(100.0, score)\n\n def _score_track_record(self) -> float:\n \"\"\"\n Score based on historical security track record.\n\n Returns:\n Track record score (0-100)\n \"\"\"\n score = 50.0 # Start at neutral\n\n # Years since major security incident\n years_since_major = self.vulnerabilities.get('years_since_major_incident', 5)\n if years_since_major >= 3:\n score += 30\n elif years_since_major >= 1:\n score += 15\n else:\n score -= 10\n\n # Security certifications\n has_certifications = self.vulnerabilities.get('has_security_certifications', False)\n if has_certifications:\n score += 20\n\n # Bug bounty program\n has_bug_bounty = self.vulnerabilities.get('has_bug_bounty_program', False)\n if has_bug_bounty:\n score += 10\n\n # Security audits\n security_audits = self.vulnerabilities.get('security_audits_per_year', 0)\n score += min(20, security_audits * 10)\n\n return min(100.0, max(0.0, score))\n\n def _calculate_grade(self, score: float) -> str:\n \"\"\"\n Convert score to letter grade.\n\n Args:\n score: Security score (0-100)\n\n Returns:\n Letter grade\n \"\"\"\n if score >= 90:\n return \"A\"\n elif score >= 80:\n return \"B\"\n elif score >= 70:\n return \"C\"\n elif score >= 60:\n return \"D\"\n else:\n return \"F\"\n\n def assess_compliance(self, standards: List[str] = None) -> Dict[str, Dict[str, Any]]:\n \"\"\"\n Assess compliance readiness for specified standards.\n\n Args:\n standards: List of compliance standards to assess (defaults to all required)\n\n Returns:\n Dictionary of compliance assessments by standard\n \"\"\"\n if standards is None:\n standards = self.compliance_requirements\n\n results = {}\n\n for standard in standards:\n if standard not in self.COMPLIANCE_STANDARDS:\n results[standard] = {\n 'readiness': 'Unknown',\n 'score': 0,\n 'status': 'Unknown standard'\n }\n continue\n\n readiness = self._assess_standard_readiness(standard)\n results[standard] = readiness\n\n return results\n\n def _assess_standard_readiness(self, standard: str) -> Dict[str, Any]:\n \"\"\"\n Assess readiness for a specific compliance standard.\n\n Args:\n standard: Compliance standard name\n\n Returns:\n Readiness assessment\n \"\"\"\n required_features = self.COMPLIANCE_STANDARDS[standard]\n met_count = 0\n total_count = len(required_features)\n missing_features = []\n\n for feature in required_features:\n if self.security_features.get(feature, False):\n met_count += 1\n else:\n missing_features.append(feature)\n\n # Calculate readiness percentage\n readiness_pct = (met_count / total_count * 100) if total_count > 0 else 0\n\n # Determine readiness level\n if readiness_pct >= 90:\n readiness_level = \"Ready\"\n status = \"Compliant - meets all requirements\"\n elif readiness_pct >= 70:\n readiness_level = \"Mostly Ready\"\n status = \"Minor gaps - additional configuration needed\"\n elif readiness_pct >= 50:\n readiness_level = \"Partial\"\n status = \"Significant work required\"\n else:\n readiness_level = \"Not Ready\"\n status = \"Major gaps - extensive implementation needed\"\n\n return {\n 'readiness_level': readiness_level,\n 'readiness_percentage': readiness_pct,\n 'status': status,\n 'features_met': met_count,\n 'features_required': total_count,\n 'missing_features': missing_features,\n 'recommendation': self._generate_compliance_recommendation(readiness_level, missing_features)\n }\n\n def _generate_compliance_recommendation(self, readiness_level: str, missing_features: List[str]) -> str:\n \"\"\"\n Generate compliance recommendation.\n\n Args:\n readiness_level: Current readiness level\n missing_features: List of missing features\n\n Returns:\n Recommendation string\n \"\"\"\n if readiness_level == \"Ready\":\n return \"Proceed with compliance audit and certification\"\n elif readiness_level == \"Mostly Ready\":\n return f\"Implement missing features: {', '.join(missing_features[:3])}\"\n elif readiness_level == \"Partial\":\n return f\"Significant implementation needed. Start with: {', '.join(missing_features[:3])}\"\n else:\n return \"Not recommended without major security enhancements\"\n\n def identify_vulnerabilities(self) -> Dict[str, Any]:\n \"\"\"\n Identify and categorize vulnerabilities.\n\n Returns:\n Categorized vulnerability report\n \"\"\"\n # Current vulnerabilities\n current = {\n 'critical': self.vulnerabilities.get('critical_last_12m', 0),\n 'high': self.vulnerabilities.get('high_last_12m', 0),\n 'medium': self.vulnerabilities.get('medium_last_12m', 0),\n 'low': self.vulnerabilities.get('low_last_12m', 0)\n }\n\n # Historical vulnerabilities (last 3 years)\n historical = {\n 'critical': self.vulnerabilities.get('critical_last_3y', 0),\n 'high': self.vulnerabilities.get('high_last_3y', 0),\n 'medium': self.vulnerabilities.get('medium_last_3y', 0),\n 'low': self.vulnerabilities.get('low_last_3y', 0)\n }\n\n # Common vulnerability types\n common_types = self.vulnerabilities.get('common_vulnerability_types', [\n 'SQL Injection',\n 'XSS',\n 'CSRF',\n 'Authentication Issues'\n ])\n\n return {\n 'current_vulnerabilities': current,\n 'total_current': sum(current.values()),\n 'historical_vulnerabilities': historical,\n 'total_historical': sum(historical.values()),\n 'common_types': common_types,\n 'severity_distribution': self._calculate_severity_distribution(current),\n 'trend': self._analyze_vulnerability_trend(current, historical)\n }\n\n def _calculate_severity_distribution(self, vulnerabilities: Dict[str, int]) -> Dict[str, str]:\n \"\"\"\n Calculate percentage distribution of vulnerability severities.\n\n Args:\n vulnerabilities: Vulnerability counts by severity\n\n Returns:\n Percentage distribution\n \"\"\"\n total = sum(vulnerabilities.values())\n if total == 0:\n return {k: \"0%\" for k in vulnerabilities.keys()}\n\n return {\n severity: f\"{(count / total * 100):.1f}%\"\n for severity, count in vulnerabilities.items()\n }\n\n def _analyze_vulnerability_trend(self, current: Dict[str, int], historical: Dict[str, int]) -> str:\n \"\"\"\n Analyze vulnerability trend.\n\n Args:\n current: Current vulnerabilities\n historical: Historical vulnerabilities\n\n Returns:\n Trend description\n \"\"\"\n current_total = sum(current.values())\n historical_avg = sum(historical.values()) / 3 # 3-year average\n\n if current_total \u003c historical_avg * 0.7:\n return \"Improving - fewer vulnerabilities than historical average\"\n elif current_total \u003c historical_avg * 1.2:\n return \"Stable - consistent with historical average\"\n else:\n return \"Concerning - more vulnerabilities than historical average\"\n\n def generate_security_report(self) -> Dict[str, Any]:\n \"\"\"\n Generate comprehensive security assessment report.\n\n Returns:\n Complete security analysis\n \"\"\"\n security_score = self.calculate_security_score()\n compliance = self.assess_compliance()\n vulnerabilities = self.identify_vulnerabilities()\n\n # Generate recommendations\n recommendations = self._generate_security_recommendations(\n security_score,\n compliance,\n vulnerabilities\n )\n\n return {\n 'technology': self.technology,\n 'security_score': security_score,\n 'compliance_assessment': compliance,\n 'vulnerability_analysis': vulnerabilities,\n 'recommendations': recommendations,\n 'overall_risk_level': self._determine_risk_level(security_score['overall_security_score'])\n }\n\n def _generate_security_recommendations(\n self,\n security_score: Dict[str, Any],\n compliance: Dict[str, Dict[str, Any]],\n vulnerabilities: Dict[str, Any]\n ) -> List[str]:\n \"\"\"\n Generate security recommendations.\n\n Args:\n security_score: Security score data\n compliance: Compliance assessment\n vulnerabilities: Vulnerability analysis\n\n Returns:\n List of recommendations\n \"\"\"\n recommendations = []\n\n # Security score recommendations\n if security_score['overall_security_score'] \u003c 70:\n recommendations.append(\"Improve overall security posture - score below acceptable threshold\")\n\n # Vulnerability recommendations\n current_critical = vulnerabilities['current_vulnerabilities']['critical']\n if current_critical > 0:\n recommendations.append(f\"Address {current_critical} critical vulnerabilities immediately\")\n\n # Patch responsiveness\n if security_score['patch_responsiveness'] \u003c 60:\n recommendations.append(\"Improve vulnerability patch response time\")\n\n # Security features\n if security_score['security_features_score'] \u003c 70:\n recommendations.append(\"Implement additional security features (MFA, audit logging, RBAC)\")\n\n # Compliance recommendations\n for standard, assessment in compliance.items():\n if assessment['readiness_level'] == \"Not Ready\":\n recommendations.append(f\"{standard}: {assessment['recommendation']}\")\n\n if not recommendations:\n recommendations.append(\"Security posture is strong - continue monitoring and maintenance\")\n\n return recommendations\n\n def _determine_risk_level(self, security_score: float) -> str:\n \"\"\"\n Determine overall risk level.\n\n Args:\n security_score: Overall security score\n\n Returns:\n Risk level description\n \"\"\"\n if security_score >= 85:\n return \"Low Risk - Strong security posture\"\n elif security_score >= 70:\n return \"Medium Risk - Acceptable with monitoring\"\n elif security_score >= 55:\n return \"High Risk - Security improvements needed\"\n else:\n return \"Critical Risk - Not recommended for production use\"\n","content_type":"text/x-python; charset=utf-8","language":"python","size":17733,"content_sha256":"0b498feeb302332978a2498c7a83de1a4b362eaba39ff598e07595e610359590"},{"filename":"skill-report.json","content":"{\n \"schema_version\": \"2.0\",\n \"meta\": {\n \"generated_at\": \"2026-01-16T14:58:55.731Z\",\n \"slug\": \"alirezarezvani-tech-stack-evaluator\",\n \"source_url\": \"https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/tech-stack-evaluator\",\n \"source_ref\": \"main\",\n \"model\": \"claude\",\n \"analysis_version\": \"3.0.0\",\n \"source_type\": \"community\",\n \"content_hash\": \"ffdc15c08e3e48fed12fbd423b1620deeefb3c7193d908216ac06192f0791280\",\n \"tree_hash\": \"caca9f62b822fa7127c88a54530229561da4c582abcf546d75ec3f83360c4496\"\n },\n \"skill\": {\n \"name\": \"tech-stack-evaluator\",\n \"description\": \"Comprehensive technology stack evaluation and comparison tool with TCO analysis, security assessment, and intelligent recommendations for engineering teams\",\n \"summary\": \"Comprehensive technology stack evaluation and comparison tool with TCO analysis, security assessment...\",\n \"icon\": \"📊\",\n \"version\": \"1.0.0\",\n \"author\": \"alirezarezvani\",\n \"license\": \"MIT\",\n \"category\": \"engineering\",\n \"tags\": [\n \"technology evaluation\",\n \"TCO analysis\",\n \"security assessment\",\n \"migration planning\",\n \"cloud comparison\"\n ],\n \"supported_tools\": [\n \"claude\",\n \"codex\",\n \"claude-code\"\n ],\n \"risk_factors\": [\n \"scripts\",\n \"filesystem\",\n \"env_access\"\n ]\n },\n \"security_audit\": {\n \"risk_level\": \"safe\",\n \"is_blocked\": false,\n \"safe_to_publish\": true,\n \"summary\": \"All 219 static findings are false positives. The scanner detected benign documentation patterns including security terminology, code block formatting, and technology names. No actual code execution, network requests, or credential access exists. Uses Python standard library only.\",\n \"risk_factor_evidence\": [\n {\n \"factor\": \"scripts\",\n \"evidence\": [\n {\n \"file\": \"stack_comparator.py\",\n \"line_start\": 1,\n \"line_end\": 50\n },\n {\n \"file\": \"security_assessor.py\",\n \"line_start\": 1,\n \"line_end\": 50\n },\n {\n \"file\": \"format_detector.py\",\n \"line_start\": 1,\n \"line_end\": 50\n }\n ]\n },\n {\n \"factor\": \"filesystem\",\n \"evidence\": [\n {\n \"file\": \"report_generator.py\",\n \"line_start\": 444,\n \"line_end\": 461\n }\n ]\n },\n {\n \"factor\": \"env_access\",\n \"evidence\": [\n {\n \"file\": \"report_generator.py\",\n \"line_start\": 38,\n \"line_end\": 38\n }\n ]\n }\n ],\n \"critical_findings\": [],\n \"high_findings\": [],\n \"medium_findings\": [],\n \"low_findings\": [],\n \"dangerous_patterns\": [],\n \"files_scanned\": 16,\n \"total_lines\": 5387,\n \"audit_model\": \"claude\",\n \"audited_at\": \"2026-01-16T14:58:55.731Z\"\n },\n \"content\": {\n \"user_title\": \"Evaluate Technology Stacks with Data-Driven Analysis\",\n \"value_statement\": \"Engineering teams need objective technology decisions but lack tools to compare frameworks, cloud providers, and stacks comprehensively. This skill provides weighted scoring, TCO analysis, security assessment, and migration planning with confidence scores.\",\n \"seo_keywords\": [\n \"technology stack evaluator\",\n \"Claude skill\",\n \"framework comparison tool\",\n \"TCO analysis\",\n \"cloud provider comparison\",\n \"migration planning tool\",\n \"security assessment\",\n \"Claude Code skill\",\n \"technology decision matrix\",\n \"React vs Vue comparison\"\n ],\n \"actual_capabilities\": [\n \"Compare frameworks and languages head-to-head with weighted scoring matrices\",\n \"Calculate total cost of ownership including hidden costs like technical debt\",\n \"Assess security posture and compliance readiness for GDPR, SOC2, HIPAA\",\n \"Analyze migration complexity with effort estimates and risk assessment\",\n \"Evaluate cloud providers with workload-specific cost and feature analysis\",\n \"Generate executive summaries with confidence scores and recommendations\"\n ],\n \"limitations\": [\n \"Ecosystem metrics are point-in-time snapshots from public databases\",\n \"TCO calculations use industry defaults when specific data is unavailable\",\n \"Cannot evaluate closed-source or internal tools without data access\",\n \"Recommendations assume provided context is accurate and complete\"\n ],\n \"use_cases\": [\n {\n \"target_user\": \"Engineering Managers\",\n \"title\": \"Compare Frameworks for New Project\",\n \"description\": \"Evaluate React vs Vue vs Angular for a SaaS dashboard with team skill considerations.\"\n },\n {\n \"target_user\": \"DevOps Leads\",\n \"title\": \"Analyze Cloud Migration Costs\",\n \"description\": \"Calculate 5-year TCO for AWS vs Azure including licensing, training, and scaling.\"\n },\n {\n \"target_user\": \"Security Officers\",\n \"title\": \"Assess Stack Compliance Readiness\",\n \"description\": \"Analyze Express.js plus MongoDB stack for SOC2 and GDPR compliance gaps.\"\n }\n ],\n \"prompt_templates\": [\n {\n \"title\": \"Quick Comparison\",\n \"scenario\": \"Simple framework comparison\",\n \"prompt\": \"Compare React vs Vue for building a SaaS dashboard. Focus on developer experience and ecosystem.\"\n },\n {\n \"title\": \"Stack Evaluation\",\n \"scenario\": \"Complete technology assessment\",\n \"prompt\": \"Evaluate our tech stack for a real-time collaboration platform: Next.js, Node.js, PostgreSQL, WebSockets, AWS. Include TCO analysis and security assessment.\"\n },\n {\n \"title\": \"Migration Analysis\",\n \"scenario\": \"Legacy to modern migration\",\n \"prompt\": \"Assess migration from Angular.js to React. Our codebase has 50,000 lines, 200 components, and an 8-person team. Need zero-downtime migration.\"\n },\n {\n \"title\": \"Cloud Comparison\",\n \"scenario\": \"Multi-provider cost analysis\",\n \"prompt\": \"Compare AWS vs Azure vs GCP for ML workloads. Priorities: GPU availability 40%, cost 30%, ecosystem 20%, support 10%. Team has 5 ML engineers.\"\n }\n ],\n \"output_examples\": [\n {\n \"input\": \"Compare React vs Vue for a SaaS dashboard\",\n \"output\": [\n \"Recommendation: React (78% confidence)\",\n \"Top strengths: Larger ecosystem, stronger corporate backing, higher job market demand\",\n \"Key concerns: Steeper learning curve, more complex state management\",\n \"Best for: Performance (React), Developer Experience (Vue)\"\n ]\n },\n {\n \"input\": \"Calculate TCO for AWS vs Azure with 50 EC2 instances\",\n \"output\": [\n \"5-Year Total TCO: AWS $1.2M, Azure $1.1M\",\n \"Key cost drivers: Infrastructure hosting, developer maintenance time\",\n \"Hidden costs: Technical debt, vendor lock-in risk\",\n \"Recommendation: Azure for cost optimization, AWS for ecosystem maturity\"\n ]\n },\n {\n \"input\": \"Assess security of Express.js plus MongoDB for SOC2 compliance\",\n \"output\": [\n \"Security Score: 72/100 (Grade C)\",\n \"SOC2 Readiness: Mostly Ready (75%) - Minor gaps in audit logging\",\n \"Missing features: Encryption at rest, MFA support\",\n \"Recommendations: Implement audit logging, enable encryption, add MFA\"\n ]\n }\n ],\n \"best_practices\": [\n \"Provide specific use cases and constraints for accurate recommendations\",\n \"Include team size, skill levels, and timeline constraints\",\n \"Request specific report sections for focused analysis\",\n \"Validate TCO assumptions against your actual costs\"\n ],\n \"anti_patterns\": [\n \"Using for trivial decisions between nearly identical tools\",\n \"Ignoring confidence scores when making final decisions\",\n \"Accepting recommendations without validating assumptions\",\n \"Comparing more than 5 technologies at once\"\n ],\n \"faq\": [\n {\n \"question\": \"How current is the ecosystem data?\",\n \"answer\": \"Uses current public data from GitHub, npm, and CVE databases. Metrics are point-in-time snapshots.\"\n },\n {\n \"question\": \"Can I compare more than 2 technologies?\",\n \"answer\": \"Yes, supports comparing 2-5 technologies. More than 5 reduces actionability.\"\n },\n {\n \"question\": \"What if I lack data for TCO analysis?\",\n \"answer\": \"The skill uses industry-standard defaults. Provide what you know and it fills reasonable estimates.\"\n },\n {\n \"question\": \"How does confidence scoring work?\",\n \"answer\": \"Based on score gaps between options, data completeness, and requirement clarity.\"\n },\n {\n \"question\": \"Can I export reports?\",\n \"answer\": \"Yes, generates markdown reports that can be saved or exported for documentation.\"\n },\n {\n \"question\": \"What compliance standards are supported?\",\n \"answer\": \"GDPR, SOC2, HIPAA, and PCI-DSS with feature checklists and gap analysis.\"\n }\n ]\n },\n \"file_structure\": [\n {\n \"name\": \"ecosystem_analyzer.py\",\n \"type\": \"file\",\n \"path\": \"ecosystem_analyzer.py\",\n \"lines\": 502\n },\n {\n \"name\": \"evaluation_output.json\",\n \"type\": \"file\",\n \"path\": \"evaluation_output.json\",\n \"lines\": 236\n },\n {\n \"name\": \"expected_output_comparison.json\",\n \"type\": \"file\",\n \"path\": \"expected_output_comparison.json\",\n \"lines\": 83\n },\n {\n \"name\": \"format_detector.py\",\n \"type\": \"file\",\n \"path\": \"format_detector.py\",\n \"lines\": 431\n },\n {\n \"name\": \"HOW_TO_USE.md\",\n \"type\": \"file\",\n \"path\": \"HOW_TO_USE.md\",\n \"lines\": 336\n },\n {\n \"name\": \"migration_analyzer.py\",\n \"type\": \"file\",\n \"path\": \"migration_analyzer.py\",\n \"lines\": 588\n },\n {\n \"name\": \"README.md\",\n \"type\": \"file\",\n \"path\": \"README.md\",\n \"lines\": 560\n },\n {\n \"name\": \"report_generator.py\",\n \"type\": \"file\",\n \"path\": \"report_generator.py\",\n \"lines\": 461\n },\n {\n \"name\": \"sample_input_structured.json\",\n \"type\": \"file\",\n \"path\": \"sample_input_structured.json\",\n \"lines\": 40\n },\n {\n \"name\": \"sample_input_tco.json\",\n \"type\": \"file\",\n \"path\": \"sample_input_tco.json\",\n \"lines\": 43\n },\n {\n \"name\": \"sample_input_text.json\",\n \"type\": \"file\",\n \"path\": \"sample_input_text.json\",\n \"lines\": 5\n },\n {\n \"name\": \"security_assessor.py\",\n \"type\": \"file\",\n \"path\": \"security_assessor.py\",\n \"lines\": 519\n },\n {\n \"name\": \"SKILL.md\",\n \"type\": \"file\",\n \"path\": \"SKILL.md\",\n \"lines\": 430\n },\n {\n \"name\": \"stack_comparator.py\",\n \"type\": \"file\",\n \"path\": \"stack_comparator.py\",\n \"lines\": 390\n },\n {\n \"name\": \"tco_calculator.py\",\n \"type\": \"file\",\n \"path\": \"tco_calculator.py\",\n \"lines\": 459\n }\n ]\n}\n","content_type":"application/json; charset=utf-8","language":"json","size":11126,"content_sha256":"91fc632b8e6f83dd09491e4415b8b2293dd1ee0eb602d5e0c40e01c75c12a0a8"},{"filename":"stack_comparator.py","content":"\"\"\"\nTechnology Stack Comparator - Main comparison engine with weighted scoring.\n\nProvides comprehensive technology comparison with customizable weighted criteria,\nfeature matrices, and intelligent recommendation generation.\n\"\"\"\n\nfrom typing import Dict, List, Any, Optional, Tuple\nimport json\n\n\nclass StackComparator:\n \"\"\"Main comparison engine for technology stack evaluation.\"\"\"\n\n # Feature categories for evaluation\n FEATURE_CATEGORIES = [\n \"performance\",\n \"scalability\",\n \"developer_experience\",\n \"ecosystem\",\n \"learning_curve\",\n \"documentation\",\n \"community_support\",\n \"enterprise_readiness\"\n ]\n\n # Default weights if not provided\n DEFAULT_WEIGHTS = {\n \"performance\": 15,\n \"scalability\": 15,\n \"developer_experience\": 20,\n \"ecosystem\": 15,\n \"learning_curve\": 10,\n \"documentation\": 10,\n \"community_support\": 10,\n \"enterprise_readiness\": 5\n }\n\n def __init__(self, comparison_data: Dict[str, Any]):\n \"\"\"\n Initialize comparator with comparison data.\n\n Args:\n comparison_data: Dictionary containing technologies to compare and criteria\n \"\"\"\n self.technologies = comparison_data.get('technologies', [])\n self.use_case = comparison_data.get('use_case', 'general')\n self.priorities = comparison_data.get('priorities', {})\n self.weights = self._normalize_weights(comparison_data.get('weights', {}))\n self.scores = {}\n\n def _normalize_weights(self, custom_weights: Dict[str, float]) -> Dict[str, float]:\n \"\"\"\n Normalize weights to sum to 100.\n\n Args:\n custom_weights: User-provided weights\n\n Returns:\n Normalized weights dictionary\n \"\"\"\n # Start with defaults\n weights = self.DEFAULT_WEIGHTS.copy()\n\n # Override with custom weights\n weights.update(custom_weights)\n\n # Normalize to 100\n total = sum(weights.values())\n if total == 0:\n return self.DEFAULT_WEIGHTS\n\n return {k: (v / total) * 100 for k, v in weights.items()}\n\n def score_technology(self, tech_name: str, tech_data: Dict[str, Any]) -> Dict[str, float]:\n \"\"\"\n Score a single technology across all criteria.\n\n Args:\n tech_name: Name of technology\n tech_data: Technology feature and metric data\n\n Returns:\n Dictionary of category scores (0-100 scale)\n \"\"\"\n scores = {}\n\n for category in self.FEATURE_CATEGORIES:\n # Get raw score from tech data (0-100 scale)\n raw_score = tech_data.get(category, {}).get('score', 50.0)\n\n # Apply use-case specific adjustments\n adjusted_score = self._adjust_for_use_case(category, raw_score, tech_name)\n\n scores[category] = min(100.0, max(0.0, adjusted_score))\n\n return scores\n\n def _adjust_for_use_case(self, category: str, score: float, tech_name: str) -> float:\n \"\"\"\n Apply use-case specific adjustments to scores.\n\n Args:\n category: Feature category\n score: Raw score\n tech_name: Technology name\n\n Returns:\n Adjusted score\n \"\"\"\n # Use case specific bonuses/penalties\n adjustments = {\n 'real-time': {\n 'performance': 1.1, # 10% bonus for real-time use cases\n 'scalability': 1.1\n },\n 'enterprise': {\n 'enterprise_readiness': 1.2, # 20% bonus\n 'documentation': 1.1\n },\n 'startup': {\n 'developer_experience': 1.15,\n 'learning_curve': 1.1\n }\n }\n\n # Determine use case type\n use_case_lower = self.use_case.lower()\n use_case_type = None\n\n for uc_key in adjustments.keys():\n if uc_key in use_case_lower:\n use_case_type = uc_key\n break\n\n # Apply adjustment if applicable\n if use_case_type and category in adjustments[use_case_type]:\n multiplier = adjustments[use_case_type][category]\n return score * multiplier\n\n return score\n\n def calculate_weighted_score(self, category_scores: Dict[str, float]) -> float:\n \"\"\"\n Calculate weighted total score.\n\n Args:\n category_scores: Dictionary of category scores\n\n Returns:\n Weighted total score (0-100 scale)\n \"\"\"\n total = 0.0\n\n for category, score in category_scores.items():\n weight = self.weights.get(category, 0.0) / 100.0 # Convert to decimal\n total += score * weight\n\n return total\n\n def compare_technologies(self, tech_data_list: List[Dict[str, Any]]) -> Dict[str, Any]:\n \"\"\"\n Compare multiple technologies and generate recommendation.\n\n Args:\n tech_data_list: List of technology data dictionaries\n\n Returns:\n Comparison results with scores and recommendation\n \"\"\"\n results = {\n 'technologies': {},\n 'recommendation': None,\n 'confidence': 0.0,\n 'decision_factors': [],\n 'comparison_matrix': []\n }\n\n # Score each technology\n tech_scores = {}\n for tech_data in tech_data_list:\n tech_name = tech_data.get('name', 'Unknown')\n category_scores = self.score_technology(tech_name, tech_data)\n weighted_score = self.calculate_weighted_score(category_scores)\n\n tech_scores[tech_name] = {\n 'category_scores': category_scores,\n 'weighted_total': weighted_score,\n 'strengths': self._identify_strengths(category_scores),\n 'weaknesses': self._identify_weaknesses(category_scores)\n }\n\n results['technologies'] = tech_scores\n\n # Generate recommendation\n results['recommendation'], results['confidence'] = self._generate_recommendation(tech_scores)\n results['decision_factors'] = self._extract_decision_factors(tech_scores)\n results['comparison_matrix'] = self._build_comparison_matrix(tech_scores)\n\n return results\n\n def _identify_strengths(self, category_scores: Dict[str, float], threshold: float = 75.0) -> List[str]:\n \"\"\"\n Identify strength categories (scores above threshold).\n\n Args:\n category_scores: Category scores dictionary\n threshold: Score threshold for strength identification\n\n Returns:\n List of strength categories\n \"\"\"\n return [\n category for category, score in category_scores.items()\n if score >= threshold\n ]\n\n def _identify_weaknesses(self, category_scores: Dict[str, float], threshold: float = 50.0) -> List[str]:\n \"\"\"\n Identify weakness categories (scores below threshold).\n\n Args:\n category_scores: Category scores dictionary\n threshold: Score threshold for weakness identification\n\n Returns:\n List of weakness categories\n \"\"\"\n return [\n category for category, score in category_scores.items()\n if score \u003c threshold\n ]\n\n def _generate_recommendation(self, tech_scores: Dict[str, Dict[str, Any]]) -> Tuple[str, float]:\n \"\"\"\n Generate recommendation and confidence level.\n\n Args:\n tech_scores: Technology scores dictionary\n\n Returns:\n Tuple of (recommended_technology, confidence_score)\n \"\"\"\n if not tech_scores:\n return \"Insufficient data\", 0.0\n\n # Sort by weighted total score\n sorted_techs = sorted(\n tech_scores.items(),\n key=lambda x: x[1]['weighted_total'],\n reverse=True\n )\n\n top_tech = sorted_techs[0][0]\n top_score = sorted_techs[0][1]['weighted_total']\n\n # Calculate confidence based on score gap\n if len(sorted_techs) > 1:\n second_score = sorted_techs[1][1]['weighted_total']\n score_gap = top_score - second_score\n\n # Confidence increases with score gap\n # 0-5 gap: low confidence\n # 5-15 gap: medium confidence\n # 15+ gap: high confidence\n if score_gap \u003c 5:\n confidence = 40.0 + (score_gap * 2) # 40-50%\n elif score_gap \u003c 15:\n confidence = 50.0 + (score_gap - 5) * 2 # 50-70%\n else:\n confidence = 70.0 + min(score_gap - 15, 30) # 70-100%\n else:\n confidence = 100.0 # Only one option\n\n return top_tech, min(100.0, confidence)\n\n def _extract_decision_factors(self, tech_scores: Dict[str, Dict[str, Any]]) -> List[Dict[str, Any]]:\n \"\"\"\n Extract key decision factors from comparison.\n\n Args:\n tech_scores: Technology scores dictionary\n\n Returns:\n List of decision factors with importance weights\n \"\"\"\n factors = []\n\n # Get top weighted categories\n sorted_weights = sorted(\n self.weights.items(),\n key=lambda x: x[1],\n reverse=True\n )[:3] # Top 3 factors\n\n for category, weight in sorted_weights:\n # Get scores for this category across all techs\n category_scores = {\n tech: scores['category_scores'].get(category, 0.0)\n for tech, scores in tech_scores.items()\n }\n\n # Find best performer\n best_tech = max(category_scores.items(), key=lambda x: x[1])\n\n factors.append({\n 'category': category,\n 'importance': f\"{weight:.1f}%\",\n 'best_performer': best_tech[0],\n 'score': best_tech[1]\n })\n\n return factors\n\n def _build_comparison_matrix(self, tech_scores: Dict[str, Dict[str, Any]]) -> List[Dict[str, Any]]:\n \"\"\"\n Build comparison matrix for display.\n\n Args:\n tech_scores: Technology scores dictionary\n\n Returns:\n List of comparison matrix rows\n \"\"\"\n matrix = []\n\n for category in self.FEATURE_CATEGORIES:\n row = {\n 'category': category,\n 'weight': f\"{self.weights.get(category, 0):.1f}%\",\n 'scores': {}\n }\n\n for tech_name, scores in tech_scores.items():\n category_score = scores['category_scores'].get(category, 0.0)\n row['scores'][tech_name] = f\"{category_score:.1f}\"\n\n matrix.append(row)\n\n # Add weighted totals row\n totals_row = {\n 'category': 'WEIGHTED TOTAL',\n 'weight': '100%',\n 'scores': {}\n }\n\n for tech_name, scores in tech_scores.items():\n totals_row['scores'][tech_name] = f\"{scores['weighted_total']:.1f}\"\n\n matrix.append(totals_row)\n\n return matrix\n\n def generate_pros_cons(self, tech_name: str, tech_scores: Dict[str, Any]) -> Dict[str, List[str]]:\n \"\"\"\n Generate pros and cons for a technology.\n\n Args:\n tech_name: Technology name\n tech_scores: Technology scores dictionary\n\n Returns:\n Dictionary with 'pros' and 'cons' lists\n \"\"\"\n category_scores = tech_scores['category_scores']\n strengths = tech_scores['strengths']\n weaknesses = tech_scores['weaknesses']\n\n pros = []\n cons = []\n\n # Generate pros from strengths\n for strength in strengths[:3]: # Top 3\n score = category_scores[strength]\n pros.append(f\"Excellent {strength.replace('_', ' ')} (score: {score:.1f}/100)\")\n\n # Generate cons from weaknesses\n for weakness in weaknesses[:3]: # Top 3\n score = category_scores[weakness]\n cons.append(f\"Weaker {weakness.replace('_', ' ')} (score: {score:.1f}/100)\")\n\n # Add generic pros/cons if not enough specific ones\n if len(pros) == 0:\n pros.append(f\"Balanced performance across all categories\")\n\n if len(cons) == 0:\n cons.append(f\"No significant weaknesses identified\")\n\n return {'pros': pros, 'cons': cons}\n","content_type":"text/x-python; charset=utf-8","language":"python","size":12381,"content_sha256":"bd56d8776c5bbd6b7c7827b83facfbf26893ad28097febc5725fed45feae4cf4"},{"filename":"tco_calculator.py","content":"\"\"\"\nTotal Cost of Ownership (TCO) Calculator.\n\nCalculates comprehensive TCO including licensing, hosting, developer productivity,\nscaling costs, and hidden costs over multi-year projections.\n\"\"\"\n\nfrom typing import Dict, List, Any, Optional\nimport json\n\n\nclass TCOCalculator:\n \"\"\"Calculate Total Cost of Ownership for technology stacks.\"\"\"\n\n def __init__(self, tco_data: Dict[str, Any]):\n \"\"\"\n Initialize TCO calculator with cost parameters.\n\n Args:\n tco_data: Dictionary containing cost parameters and projections\n \"\"\"\n self.technology = tco_data.get('technology', 'Unknown')\n self.team_size = tco_data.get('team_size', 5)\n self.timeline_years = tco_data.get('timeline_years', 5)\n self.initial_costs = tco_data.get('initial_costs', {})\n self.operational_costs = tco_data.get('operational_costs', {})\n self.scaling_params = tco_data.get('scaling_params', {})\n self.productivity_factors = tco_data.get('productivity_factors', {})\n\n def calculate_initial_costs(self) -> Dict[str, float]:\n \"\"\"\n Calculate one-time initial costs.\n\n Returns:\n Dictionary of initial cost components\n \"\"\"\n costs = {\n 'licensing': self.initial_costs.get('licensing', 0.0),\n 'training': self._calculate_training_costs(),\n 'migration': self.initial_costs.get('migration', 0.0),\n 'setup': self.initial_costs.get('setup', 0.0),\n 'tooling': self.initial_costs.get('tooling', 0.0)\n }\n\n costs['total_initial'] = sum(costs.values())\n return costs\n\n def _calculate_training_costs(self) -> float:\n \"\"\"\n Calculate training costs based on team size and learning curve.\n\n Returns:\n Total training cost\n \"\"\"\n # Default training assumptions\n hours_per_developer = self.initial_costs.get('training_hours_per_dev', 40)\n avg_hourly_rate = self.initial_costs.get('developer_hourly_rate', 100)\n training_materials = self.initial_costs.get('training_materials', 500)\n\n total_hours = self.team_size * hours_per_developer\n total_cost = (total_hours * avg_hourly_rate) + training_materials\n\n return total_cost\n\n def calculate_operational_costs(self) -> Dict[str, List[float]]:\n \"\"\"\n Calculate ongoing operational costs per year.\n\n Returns:\n Dictionary with yearly cost projections\n \"\"\"\n yearly_costs = {\n 'licensing': [],\n 'hosting': [],\n 'support': [],\n 'maintenance': [],\n 'total_yearly': []\n }\n\n for year in range(1, self.timeline_years + 1):\n # Licensing costs (may include annual fees)\n license_cost = self.operational_costs.get('annual_licensing', 0.0)\n yearly_costs['licensing'].append(license_cost)\n\n # Hosting costs (scale with growth)\n hosting_cost = self._calculate_hosting_cost(year)\n yearly_costs['hosting'].append(hosting_cost)\n\n # Support costs\n support_cost = self.operational_costs.get('annual_support', 0.0)\n yearly_costs['support'].append(support_cost)\n\n # Maintenance costs (developer time)\n maintenance_cost = self._calculate_maintenance_cost(year)\n yearly_costs['maintenance'].append(maintenance_cost)\n\n # Total for year\n year_total = (\n license_cost + hosting_cost + support_cost + maintenance_cost\n )\n yearly_costs['total_yearly'].append(year_total)\n\n return yearly_costs\n\n def _calculate_hosting_cost(self, year: int) -> float:\n \"\"\"\n Calculate hosting costs with growth projection.\n\n Args:\n year: Year number (1-indexed)\n\n Returns:\n Hosting cost for the year\n \"\"\"\n base_cost = self.operational_costs.get('monthly_hosting', 1000.0) * 12\n growth_rate = self.scaling_params.get('annual_growth_rate', 0.20) # 20% default\n\n # Apply compound growth\n year_cost = base_cost * ((1 + growth_rate) ** (year - 1))\n\n return year_cost\n\n def _calculate_maintenance_cost(self, year: int) -> float:\n \"\"\"\n Calculate maintenance costs (developer time).\n\n Args:\n year: Year number (1-indexed)\n\n Returns:\n Maintenance cost for the year\n \"\"\"\n hours_per_dev_per_month = self.operational_costs.get('maintenance_hours_per_dev_monthly', 20)\n avg_hourly_rate = self.initial_costs.get('developer_hourly_rate', 100)\n\n monthly_cost = self.team_size * hours_per_dev_per_month * avg_hourly_rate\n yearly_cost = monthly_cost * 12\n\n return yearly_cost\n\n def calculate_scaling_costs(self) -> Dict[str, Any]:\n \"\"\"\n Calculate scaling-related costs and metrics.\n\n Returns:\n Dictionary with scaling cost analysis\n \"\"\"\n # Project user growth\n initial_users = self.scaling_params.get('initial_users', 1000)\n annual_growth_rate = self.scaling_params.get('annual_growth_rate', 0.20)\n\n user_projections = []\n for year in range(1, self.timeline_years + 1):\n users = initial_users * ((1 + annual_growth_rate) ** year)\n user_projections.append(int(users))\n\n # Calculate cost per user\n operational = self.calculate_operational_costs()\n cost_per_user = []\n\n for year_idx, year_cost in enumerate(operational['total_yearly']):\n users = user_projections[year_idx]\n cost_per_user.append(year_cost / users if users > 0 else 0)\n\n # Infrastructure scaling costs\n infra_scaling = self._calculate_infrastructure_scaling()\n\n return {\n 'user_projections': user_projections,\n 'cost_per_user': cost_per_user,\n 'infrastructure_scaling': infra_scaling,\n 'scaling_efficiency': self._calculate_scaling_efficiency(cost_per_user)\n }\n\n def _calculate_infrastructure_scaling(self) -> Dict[str, List[float]]:\n \"\"\"\n Calculate infrastructure scaling costs.\n\n Returns:\n Infrastructure cost projections\n \"\"\"\n base_servers = self.scaling_params.get('initial_servers', 5)\n cost_per_server_monthly = self.scaling_params.get('cost_per_server_monthly', 200)\n growth_rate = self.scaling_params.get('annual_growth_rate', 0.20)\n\n server_costs = []\n for year in range(1, self.timeline_years + 1):\n servers_needed = base_servers * ((1 + growth_rate) ** year)\n yearly_cost = servers_needed * cost_per_server_monthly * 12\n server_costs.append(yearly_cost)\n\n return {\n 'yearly_infrastructure_costs': server_costs\n }\n\n def _calculate_scaling_efficiency(self, cost_per_user: List[float]) -> str:\n \"\"\"\n Assess scaling efficiency based on cost per user trend.\n\n Args:\n cost_per_user: List of yearly cost per user\n\n Returns:\n Efficiency assessment\n \"\"\"\n if len(cost_per_user) \u003c 2:\n return \"Insufficient data\"\n\n # Compare first year to last year\n initial = cost_per_user[0]\n final = cost_per_user[-1]\n\n if final \u003c initial * 0.8:\n return \"Excellent - economies of scale achieved\"\n elif final \u003c initial:\n return \"Good - improving efficiency over time\"\n elif final \u003c initial * 1.2:\n return \"Moderate - costs growing with users\"\n else:\n return \"Poor - costs growing faster than users\"\n\n def calculate_productivity_impact(self) -> Dict[str, Any]:\n \"\"\"\n Calculate developer productivity impact.\n\n Returns:\n Productivity analysis\n \"\"\"\n # Productivity multiplier (1.0 = baseline)\n productivity_multiplier = self.productivity_factors.get('productivity_multiplier', 1.0)\n\n # Time to market impact (in days)\n ttm_reduction = self.productivity_factors.get('time_to_market_reduction_days', 0)\n\n # Calculate value of faster development\n avg_feature_time_days = self.productivity_factors.get('avg_feature_time_days', 30)\n features_per_year = 365 / avg_feature_time_days\n faster_features_per_year = 365 / max(1, avg_feature_time_days - ttm_reduction)\n\n additional_features = faster_features_per_year - features_per_year\n feature_value = self.productivity_factors.get('avg_feature_value', 10000)\n\n yearly_productivity_value = additional_features * feature_value\n\n return {\n 'productivity_multiplier': productivity_multiplier,\n 'time_to_market_reduction_days': ttm_reduction,\n 'additional_features_per_year': additional_features,\n 'yearly_productivity_value': yearly_productivity_value,\n 'five_year_productivity_value': yearly_productivity_value * self.timeline_years\n }\n\n def calculate_hidden_costs(self) -> Dict[str, float]:\n \"\"\"\n Identify and calculate hidden costs.\n\n Returns:\n Dictionary of hidden cost components\n \"\"\"\n costs = {\n 'technical_debt': self._estimate_technical_debt(),\n 'vendor_lock_in_risk': self._estimate_vendor_lock_in_cost(),\n 'security_incidents': self._estimate_security_costs(),\n 'downtime_risk': self._estimate_downtime_costs(),\n 'developer_turnover': self._estimate_turnover_costs()\n }\n\n costs['total_hidden_costs'] = sum(costs.values())\n return costs\n\n def _estimate_technical_debt(self) -> float:\n \"\"\"\n Estimate technical debt accumulation costs.\n\n Returns:\n Estimated technical debt cost\n \"\"\"\n # Percentage of development time spent on debt\n debt_percentage = self.productivity_factors.get('technical_debt_percentage', 0.15)\n yearly_dev_cost = self._calculate_maintenance_cost(1) # Year 1 baseline\n\n # Technical debt accumulates over time\n total_debt_cost = 0\n for year in range(1, self.timeline_years + 1):\n year_debt = yearly_dev_cost * debt_percentage * year # Increases each year\n total_debt_cost += year_debt\n\n return total_debt_cost\n\n def _estimate_vendor_lock_in_cost(self) -> float:\n \"\"\"\n Estimate cost of vendor lock-in.\n\n Returns:\n Estimated lock-in cost\n \"\"\"\n lock_in_risk = self.productivity_factors.get('vendor_lock_in_risk', 'low')\n\n # Migration cost if switching vendors\n migration_cost = self.initial_costs.get('migration', 10000)\n\n risk_multipliers = {\n 'low': 0.1,\n 'medium': 0.3,\n 'high': 0.6\n }\n\n multiplier = risk_multipliers.get(lock_in_risk, 0.2)\n return migration_cost * multiplier\n\n def _estimate_security_costs(self) -> float:\n \"\"\"\n Estimate potential security incident costs.\n\n Returns:\n Estimated security cost\n \"\"\"\n incidents_per_year = self.productivity_factors.get('security_incidents_per_year', 0.5)\n avg_incident_cost = self.productivity_factors.get('avg_security_incident_cost', 50000)\n\n total_cost = incidents_per_year * avg_incident_cost * self.timeline_years\n return total_cost\n\n def _estimate_downtime_costs(self) -> float:\n \"\"\"\n Estimate downtime costs.\n\n Returns:\n Estimated downtime cost\n \"\"\"\n hours_downtime_per_year = self.productivity_factors.get('downtime_hours_per_year', 2)\n cost_per_hour = self.productivity_factors.get('downtime_cost_per_hour', 5000)\n\n total_cost = hours_downtime_per_year * cost_per_hour * self.timeline_years\n return total_cost\n\n def _estimate_turnover_costs(self) -> float:\n \"\"\"\n Estimate costs from developer turnover.\n\n Returns:\n Estimated turnover cost\n \"\"\"\n turnover_rate = self.productivity_factors.get('annual_turnover_rate', 0.15)\n cost_per_hire = self.productivity_factors.get('cost_per_new_hire', 30000)\n\n hires_per_year = self.team_size * turnover_rate\n total_cost = hires_per_year * cost_per_hire * self.timeline_years\n\n return total_cost\n\n def calculate_total_tco(self) -> Dict[str, Any]:\n \"\"\"\n Calculate complete TCO over the timeline.\n\n Returns:\n Comprehensive TCO analysis\n \"\"\"\n initial = self.calculate_initial_costs()\n operational = self.calculate_operational_costs()\n scaling = self.calculate_scaling_costs()\n productivity = self.calculate_productivity_impact()\n hidden = self.calculate_hidden_costs()\n\n # Calculate total costs\n total_operational = sum(operational['total_yearly'])\n total_cost = initial['total_initial'] + total_operational + hidden['total_hidden_costs']\n\n # Adjust for productivity gains\n net_cost = total_cost - productivity['five_year_productivity_value']\n\n return {\n 'technology': self.technology,\n 'timeline_years': self.timeline_years,\n 'initial_costs': initial,\n 'operational_costs': operational,\n 'scaling_analysis': scaling,\n 'productivity_impact': productivity,\n 'hidden_costs': hidden,\n 'total_tco': total_cost,\n 'net_tco_after_productivity': net_cost,\n 'average_yearly_cost': total_cost / self.timeline_years\n }\n\n def generate_tco_summary(self) -> Dict[str, Any]:\n \"\"\"\n Generate executive summary of TCO.\n\n Returns:\n TCO summary for reporting\n \"\"\"\n tco = self.calculate_total_tco()\n\n return {\n 'technology': self.technology,\n 'total_tco': f\"${tco['total_tco']:,.2f}\",\n 'net_tco': f\"${tco['net_tco_after_productivity']:,.2f}\",\n 'average_yearly': f\"${tco['average_yearly_cost']:,.2f}\",\n 'initial_investment': f\"${tco['initial_costs']['total_initial']:,.2f}\",\n 'key_cost_drivers': self._identify_cost_drivers(tco),\n 'cost_optimization_opportunities': self._identify_optimizations(tco)\n }\n\n def _identify_cost_drivers(self, tco: Dict[str, Any]) -> List[str]:\n \"\"\"\n Identify top cost drivers.\n\n Args:\n tco: Complete TCO analysis\n\n Returns:\n List of top cost drivers\n \"\"\"\n drivers = []\n\n # Check operational costs\n operational = tco['operational_costs']\n total_hosting = sum(operational['hosting'])\n total_maintenance = sum(operational['maintenance'])\n\n if total_hosting > total_maintenance:\n drivers.append(f\"Infrastructure/hosting ({total_hosting:,.0f})\")\n else:\n drivers.append(f\"Developer maintenance time ({total_maintenance:,.0f})\")\n\n # Check hidden costs\n hidden = tco['hidden_costs']\n if hidden['technical_debt'] > 10000:\n drivers.append(f\"Technical debt ({hidden['technical_debt']:,.0f})\")\n\n return drivers[:3] # Top 3\n\n def _identify_optimizations(self, tco: Dict[str, Any]) -> List[str]:\n \"\"\"\n Identify cost optimization opportunities.\n\n Args:\n tco: Complete TCO analysis\n\n Returns:\n List of optimization suggestions\n \"\"\"\n optimizations = []\n\n # Check scaling efficiency\n scaling = tco['scaling_analysis']\n if scaling['scaling_efficiency'].startswith('Poor'):\n optimizations.append(\"Improve scaling efficiency - costs growing too fast\")\n\n # Check hidden costs\n hidden = tco['hidden_costs']\n if hidden['technical_debt'] > 20000:\n optimizations.append(\"Address technical debt accumulation\")\n\n if hidden['downtime_risk'] > 10000:\n optimizations.append(\"Invest in reliability to reduce downtime costs\")\n\n return optimizations\n","content_type":"text/x-python; charset=utf-8","language":"python","size":16153,"content_sha256":"55221bb79cb06c7890074beb08b4d59a38e2c3d4835495de230446272e065677"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"Technology Stack Evaluator","type":"text"}]},{"type":"paragraph","content":[{"text":"A comprehensive evaluation framework for comparing technologies, frameworks, cloud providers, and complete technology stacks. Provides data-driven recommendations with TCO analysis, security assessment, ecosystem health scoring, and migration path analysis.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Capabilities","type":"text"}]},{"type":"paragraph","content":[{"text":"This skill provides eight comprehensive evaluation capabilities:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Technology Comparison","type":"text","marks":[{"type":"strong"}]},{"text":": Head-to-head comparisons of frameworks, languages, and tools (React vs Vue, PostgreSQL vs MongoDB, Node.js vs Python)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Stack Evaluation","type":"text","marks":[{"type":"strong"}]},{"text":": Assess complete technology stacks for specific use cases (real-time collaboration, API-heavy SaaS, data-intensive platforms)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Maturity & Ecosystem Analysis","type":"text","marks":[{"type":"strong"}]},{"text":": Evaluate community health, maintenance status, long-term viability, and ecosystem strength","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Total Cost of Ownership (TCO)","type":"text","marks":[{"type":"strong"}]},{"text":": Calculate comprehensive costs including licensing, hosting, developer productivity, and scaling","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Security & Compliance","type":"text","marks":[{"type":"strong"}]},{"text":": Analyze vulnerabilities, compliance readiness (GDPR, SOC2, HIPAA), and security posture","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Migration Path Analysis","type":"text","marks":[{"type":"strong"}]},{"text":": Assess migration complexity, risks, timelines, and strategies from legacy to modern stacks","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Cloud Provider Comparison","type":"text","marks":[{"type":"strong"}]},{"text":": Compare AWS vs Azure vs GCP for specific workloads with cost and feature analysis","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Decision Reports","type":"text","marks":[{"type":"strong"}]},{"text":": Generate comprehensive decision matrices with pros/cons, confidence scores, and actionable recommendations","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Input Requirements","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Flexible Input Formats (Automatic Detection)","type":"text"}]},{"type":"paragraph","content":[{"text":"The skill automatically detects and processes multiple input formats:","type":"text"}]},{"type":"paragraph","content":[{"text":"Text/Conversational","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"\"Compare React vs Vue for building a SaaS dashboard\"\n\"Evaluate technology stack for real-time collaboration platform\"\n\"Should we migrate from MongoDB to PostgreSQL?\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Structured (YAML)","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"yaml"},"content":[{"text":"comparison:\n technologies:\n - name: \"React\"\n - name: \"Vue\"\n use_case: \"SaaS dashboard\"\n priorities:\n - \"Developer productivity\"\n - \"Ecosystem maturity\"\n - \"Performance\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Structured (JSON)","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"json"},"content":[{"text":"{\n \"comparison\": {\n \"technologies\": [\"React\", \"Vue\"],\n \"use_case\": \"SaaS dashboard\",\n \"priorities\": [\"Developer productivity\", \"Ecosystem maturity\"]\n }\n}","type":"text"}]},{"type":"paragraph","content":[{"text":"URLs for Ecosystem Analysis","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"GitHub repository URLs (for health scoring)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"npm package URLs (for download statistics)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Technology documentation URLs (for feature extraction)","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Analysis Scope Selection","type":"text"}]},{"type":"paragraph","content":[{"text":"Users can select which analyses to run:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Quick Comparison","type":"text","marks":[{"type":"strong"}]},{"text":": Basic scoring and comparison (200-300 tokens)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Standard Analysis","type":"text","marks":[{"type":"strong"}]},{"text":": Scoring + TCO + Security (500-800 tokens)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Comprehensive Report","type":"text","marks":[{"type":"strong"}]},{"text":": All analyses including migration paths (1200-1500 tokens)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Custom","type":"text","marks":[{"type":"strong"}]},{"text":": User selects specific sections (modular)","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Output Formats","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Context-Aware Output","type":"text"}]},{"type":"paragraph","content":[{"text":"The skill automatically adapts output based on environment:","type":"text"}]},{"type":"paragraph","content":[{"text":"Claude Desktop (Rich Markdown)","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Formatted tables with color indicators","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Expandable sections for detailed analysis","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Visual decision matrices","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Charts and graphs (when appropriate)","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"CLI/Terminal (Terminal-Friendly)","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Plain text tables with ASCII borders","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Compact formatting","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Clear section headers","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Copy-paste friendly code blocks","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Progressive Disclosure Structure","type":"text"}]},{"type":"paragraph","content":[{"text":"Executive Summary (200-300 tokens)","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Recommendation summary","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Top 3 pros and cons","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Confidence level (High/Medium/Low)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Key decision factors","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Detailed Breakdown (on-demand)","type":"text","marks":[{"type":"strong"}]},{"text":":","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Complete scoring matrices","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Detailed TCO calculations","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Full security analysis","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Migration complexity assessment","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"All supporting data and calculations","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Report Sections (User-Selectable)","type":"text"}]},{"type":"paragraph","content":[{"text":"Users choose which sections to include:","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Scoring & Comparison Matrix","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Weighted decision scores","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Head-to-head comparison tables","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Strengths and weaknesses","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Financial Analysis","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"TCO breakdown (5-year projection)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"ROI analysis","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Cost per user/request metrics","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Hidden cost identification","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Ecosystem Health","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Community size and activity","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"GitHub stars, npm downloads","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Release frequency and maintenance","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Issue response times","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Viability assessment","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Security & Compliance","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Vulnerability count (CVE database)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Security patch frequency","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Compliance readiness (GDPR, SOC2, HIPAA)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Security scoring","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Migration Analysis","type":"text","marks":[{"type":"strong"}]},{"text":" (when applicable)","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Migration complexity scoring","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Code change estimates","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Data migration requirements","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Downtime assessment","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Risk mitigation strategies","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Performance Benchmarks","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Throughput/latency comparisons","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Resource usage analysis","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Scalability characteristics","type":"text"}]}]}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"How to 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urgency","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"When NOT to Use This Skill","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Trivial Decisions","type":"text","marks":[{"type":"strong"}]},{"text":": Choosing between nearly-identical tools (use team preference)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Mandated Solutions","type":"text","marks":[{"type":"strong"}]},{"text":": When technology choice is already decided by management/policy","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Insufficient Context","type":"text","marks":[{"type":"strong"}]},{"text":": When you don't know your requirements, priorities, or constraints","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Real-Time Production 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Technology Stack Evaluator A comprehensive evaluation framework for comparing technologies, frameworks, cloud providers, and complete technology stacks. Provides data-driven recommendations with TCO analysis, security assessment, ecosystem health scoring, and migration path analysis. Capabilities This skill provides eight comprehensive evaluation capabilities: - Technology Comparison : Head-to-head comparisons of frameworks, languages, and tools (React vs Vue, PostgreSQL vs MongoDB, Node.js vs Python) - Stack Evaluation : Assess complete technology stacks for specific use cases (real-time col…