huashu-nuwa — Skillopedia

女娲 · Skill造人术「写不进去的那部分，才是你真正的护城河。」——但写得进去的部分，已经足够强大。核心理念女娲不是复制人，是提炼思维框架。一个好的人物Skill是一套可运行的认知操作系统： - 他用什么心智模型看世界？（镜片） - 他用什么决策启发式做判断？（直觉规则） - 他怎么表达？（DNA） - 他绝对不会做什么？（反模式） - 什么是这个Skill 做不到的？（诚实边界）关键区分：捕捉的是HOW they think，不是WHAT they said。 --- 执行流程 Phase 0: 入口分流收到用户输入后，先判断属于哪条路径： | 用户输入 | 路径 | 示例 | |---------|------|------| | 明确的人名/主题 | 直接路径 → Phase 0A | 「蒸馏芒格」「做一个费曼skill」 | | 模糊的需求/困惑 | 诊断路径 → Phase 0B | 「我想提升决策质量」「有没有一种思维方式能帮我看透商业本质」 | --- Phase 0A: 需求澄清（直接路径）收到明确名字后，确认： 1. 这个人/主题是谁：确保理解正确 2. 聚焦方向（可选）：全面画像 vs 聚焦某个维度？ 3. 用途：思维顾问？决策参考？角色扮演？ 4. 新建 or 更新：是否已有该人物的Skill？（检查目录）…

, content, re.MULTILINE)\n if headings:\n return headings[:max_items]\n\n # fallback: 提取加粗项\n bolds = re.findall(r'\\*\\*(.+?)\\*\\*', content)\n if bolds:\n return bolds[:max_items]\n\n # fallback: 取前3个非空行\n lines = [l.strip() for l in content.split('\\n') if l.strip() and not l.startswith('#')]\n return [l[:50] + '...' if len(l) > 50 else l for l in lines[:max_items]]\n\n\ndef find_contradictions(files: dict[str, str]) -> list[str]:\n \"\"\"简单检测跨文件矛盾（同一关键词出现不同判断）\"\"\"\n contradictions = []\n # 检测「但是」「然而」「相反」「矛盾」等矛盾标记\n for name, content in files.items():\n matches = re.findall(r'(?:矛盾|相反|但实际上|然而.*?不同|争议).{0,100}', content)\n for m in matches:\n contradictions.append(f\"{AGENTS.get(name, name)}: {m[:80]}\")\n return contradictions[:5] # 最多5条\n\n\ndef main():\n if len(sys.argv) \u003c 2:\n print(\"用法: python3 merge_research.py \u003cskill目录路径>\")\n sys.exit(1)\n\n skill_dir = Path(sys.argv[1])\n research_dir = skill_dir / 'references' / 'research'\n\n if not research_dir.exists():\n print(f\"❌ 目录不存在: {research_dir}\")\n sys.exit(1)\n\n files = {}\n rows = []\n total_sources = 0\n total_primary = 0\n total_secondary = 0\n missing = []\n\n for key, label in AGENTS.items():\n md_file = research_dir / f\"{key}.md\"\n if not md_file.exists():\n missing.append(label)\n rows.append(f\"│ {label:\u003c12} │ {'❌ 缺失':\u003c8} │ {'—':\u003c24} │\")\n continue\n\n content = md_file.read_text(encoding='utf-8')\n files[key] = content\n stats = count_sources(content)\n findings = extract_key_findings(content)\n\n total_sources += stats['unique_urls']\n total_primary += stats['primary_markers']\n total_secondary += stats['secondary_markers']\n\n findings_str = ', '.join(findings) if findings else '—'\n if len(findings_str) > 40:\n findings_str = findings_str[:37] + '...'\n\n rows.append(f\"│ {label:\u003c12} │ {stats['unique_urls']:\u003c8} │ {findings_str:\u003c24} │\")\n\n # 矛盾检测\n contradictions = find_contradictions(files)\n\n # 输出\n print(\"┌──────────────┬──────────┬──────────────────────────┐\")\n print(\"│ Agent │ 来源数量 │ 关键发现 │\")\n print(\"├──────────────┼──────────┼──────────────────────────┤\")\n for row in rows:\n print(row)\n print(\"├──────────────┼──────────┼──────────────────────────┤\")\n\n primary_ratio = f\"{total_primary}/{total_primary + total_secondary}\" if (total_primary + total_secondary) > 0 else \"未标记\"\n print(f\"│ 总来源数 │ {total_sources:\u003c8} │ 一手占比: {primary_ratio:\u003c15} │\")\n\n if contradictions:\n print(f\"│ 矛盾点 │ {len(contradictions)}处 │ {contradictions[0][:24]:\u003c24} │\")\n else:\n print(f\"│ 矛盾点 │ 0处 │ {'—':\u003c24} │\")\n\n if missing:\n print(f\"│ 信息不足维度 │ {len(missing)}个 │ {', '.join(missing):\u003c24} │\")\n else:\n print(f\"│ 信息不足维度 │ 无 │ {'—':\u003c24} │\")\n\n print(\"└──────────────┴──────────┴──────────────────────────┘\")\n\n # 总结\n if total_sources \u003c 10:\n print(\"\\n⚠️ 总来源数 \u003c10，建议降低期望或补充调研\")\n if missing:\n print(f\"\\n⚠️ 缺失维度: {', '.join(missing)}，建议补充或在诚实边界中标注\")\n\n\nif __name__ == '__main__':\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":5609,"content_sha256":"bd371814fb32cf10ab171b0f5d20d5a0ca2e9f83570bdf763729d0ab3187c6a6"},{"filename":"scripts/quality_check.py","content":"#!/usr/bin/env python3\n\"\"\"\n自动检查生成的SKILL.md是否通过Phase 4质量标准。\n对照通过标准表格逐项检查，输出通过/不通过和具体原因。\n\n用法:\n python3 quality_check.py \u003cSKILL.md路径>\n\n示例:\n python3 quality_check.py .claude/skills/elon-musk-perspective/SKILL.md\n\"\"\"\n\nimport sys\nimport re\nfrom pathlib import Path\n\n\ndef check_mental_models(content: str) -> tuple[bool, str]:\n \"\"\"检查心智模型数量（3-7个）\"\"\"\n # 匹配 ### 模型N: 或 ### N. 等模式\n models = re.findall(r'^###\\s+(?:模型|Model|心智模型)\\s*\\d', content, re.MULTILINE)\n if not models:\n # fallback: 数「### 」开头的行在心智模型section中\n in_section = False\n count = 0\n for line in content.split('\\n'):\n if re.match(r'^##\\s+.*心智模型|Mental Model', line, re.IGNORECASE):\n in_section = True\n continue\n if in_section and re.match(r'^##\\s+', line) and '心智模型' not in line:\n break\n if in_section and re.match(r'^###\\s+', line):\n count += 1\n if count > 0:\n passed = 3 \u003c= count \u003c= 7\n return passed, f\"{count}个心智模型 {'✅' if passed else '❌ (应为3-7个)'}\"\n\n count = len(models)\n if count == 0:\n return False, \"未检测到心智模型section\"\n passed = 3 \u003c= count \u003c= 7\n return passed, f\"{count}个心智模型 {'✅' if passed else '❌ (应为3-7个)'}\"\n\n\ndef check_limitations(content: str) -> tuple[bool, str]:\n \"\"\"检查每个模型是否有局限性\"\"\"\n has_limitation = bool(re.search(r'局限|失效|不适用|盲区|limitation|blind spot', content, re.IGNORECASE))\n return has_limitation, \"有局限性标注 ✅\" if has_limitation else \"❌ 未找到局限性描述\"\n\n\ndef check_expression_dna(content: str) -> tuple[bool, str]:\n \"\"\"检查表达DNA辨识度\"\"\"\n dna_section = bool(re.search(r'表达DNA|Expression DNA|表达风格', content, re.IGNORECASE))\n if not dna_section:\n return False, \"❌ 未找到表达DNA section\"\n\n # 检查是否有具体的风格描述（句式、词汇等）\n style_markers = len(re.findall(r'句式|词汇|语气|幽默|节奏|确定性|引用|口头禅', content))\n passed = style_markers >= 3\n return passed, f\"表达DNA特征: {style_markers}项 {'✅' if passed else '❌ (应≥3项)'}\"\n\n\ndef check_honest_boundary(content: str) -> tuple[bool, str]:\n \"\"\"检查诚实边界（至少3条）\"\"\"\n # 找诚实边界section\n boundary_match = re.search(r'(?:##\\s+.*诚实边界|## Honest Boundary)(.*?)(?=\\n##\\s|\\Z)', content, re.DOTALL | re.IGNORECASE)\n if not boundary_match:\n return False, \"❌ 未找到诚实边界section\"\n\n boundary_text = boundary_match.group(1)\n # 计算列表项\n items = re.findall(r'^[-*]\\s+', boundary_text, re.MULTILINE)\n count = len(items)\n passed = count >= 3\n return passed, f\"诚实边界: {count}条 {'✅' if passed else '❌ (应≥3条)'}\"\n\n\ndef check_tensions(content: str) -> tuple[bool, str]:\n \"\"\"检查内在张力（至少2对）\"\"\"\n tension_markers = len(re.findall(r'张力|矛盾|tension|paradox|一方面.*另一方面|既.*又', content, re.IGNORECASE))\n passed = tension_markers >= 2\n return passed, f\"内在张力: {tension_markers}处 {'✅' if passed else '❌ (应≥2处)'}\"\n\n\ndef check_primary_sources(content: str) -> tuple[bool, str]:\n \"\"\"检查一手来源占比\"\"\"\n # 找调研来源section\n source_section = re.search(r'(?:##\\s+.*来源|## Source|## Reference)(.*?)(?=\\n##\\s|\\Z)', content, re.DOTALL | re.IGNORECASE)\n if not source_section:\n return True, \"未找到来源section（跳过检查）\"\n\n source_text = source_section.group(1)\n primary = len(re.findall(r'一手|primary|本人著作|原始', source_text, re.IGNORECASE))\n secondary = len(re.findall(r'二手|secondary|转述|评论', source_text, re.IGNORECASE))\n total = primary + secondary\n if total == 0:\n return True, \"未标记来源类型（跳过检查）\"\n\n ratio = primary / total\n passed = ratio > 0.5\n return passed, f\"一手来源占比: {primary}/{total} ({ratio:.0%}) {'✅' if passed else '❌ (应>50%)'}\"\n\n\ndef main():\n if len(sys.argv) \u003c 2:\n print(\"用法: python3 quality_check.py \u003cSKILL.md路径>\")\n sys.exit(1)\n\n skill_path = Path(sys.argv[1])\n if not skill_path.exists():\n print(f\"❌ 文件不存在: {skill_path}\")\n sys.exit(1)\n\n content = skill_path.read_text(encoding='utf-8')\n\n checks = [\n (\"心智模型数量\", check_mental_models),\n (\"模型局限性\", check_limitations),\n (\"表达DNA辨识度\", check_expression_dna),\n (\"诚实边界\", check_honest_boundary),\n (\"内在张力\", check_tensions),\n (\"一手来源占比\", check_primary_sources),\n ]\n\n print(f\"质量检查: {skill_path.name}\")\n print(\"=\" * 50)\n\n passed_count = 0\n total = len(checks)\n\n for name, check_fn in checks:\n passed, detail = check_fn(content)\n status = \"✅ PASS\" if passed else \"❌ FAIL\"\n print(f\" {name:\u003c12} {status} {detail}\")\n if passed:\n passed_count += 1\n\n print(\"=\" * 50)\n print(f\"结果: {passed_count}/{total} 通过\")\n\n if passed_count == total:\n print(\"🎉 全部通过，可以交付\")\n elif passed_count >= total - 1:\n print(\"⚠️ 基本通过，建议修复不通过项后交付\")\n else:\n print(\"❌ 多项不通过，建议回到Phase 2迭代\")\n\n sys.exit(0 if passed_count == total else 1)\n\n\nif __name__ == '__main__':\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":5677,"content_sha256":"1f34ded1f0d161db82c4ce789088e772f5670ebbdd632f17d90f9896ee4e978e"},{"filename":"scripts/srt_to_transcript.py","content":"#!/usr/bin/env python3\n\"\"\"\n将SRT/VTT字幕文件清洗为干净的纯文本transcript。\n去除时间戳、序号、重复行、HTML标签，输出可直接阅读的文本。\n\n用法:\n python3 srt_to_transcript.py input.srt [output.txt]\n python3 srt_to_transcript.py input.vtt [output.txt]\n\n如果不指定输出文件，默认输出到 input_transcript.txt\n\"\"\"\n\nimport sys\nimport re\nfrom pathlib import Path\n\n\ndef clean_srt(content: str) -> str:\n \"\"\"清洗SRT格式字幕\"\"\"\n lines = content.strip().split('\\n')\n texts = []\n\n for line in lines:\n line = line.strip()\n # 跳过序号行（纯数字）\n if re.match(r'^\\d+

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, line):\n continue\n # 跳过时间戳行\n if re.match(r'\\d{2}:\\d{2}:\\d{2}', line):\n continue\n # 跳过空行\n if not line:\n continue\n # 去除HTML标签\n line = re.sub(r'\u003c[^>]+>', '', line)\n # 去除VTT的position标记\n line = re.sub(r'align:.*$|position:.*

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, '', line).strip()\n if line:\n texts.append(line)\n\n # 去重（自动字幕常有连续重复行）\n deduped = []\n for text in texts:\n if not deduped or text != deduped[-1]:\n deduped.append(text)\n\n # 合并成段落：连续的短句合并，遇到句末标点或长停顿换行\n result = []\n current = []\n\n for text in deduped:\n current.append(text)\n # 如果当前累积文本够长或遇到句末标点，形成一个段落\n joined = ' '.join(current)\n if len(joined) > 200 or re.search(r'[。！？.!?]

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, text):\n result.append(joined)\n current = []\n\n if current:\n result.append(' '.join(current))\n\n return '\\n\\n'.join(result)\n\n\ndef clean_vtt(content: str) -> str:\n \"\"\"清洗VTT格式字幕（先去掉VTT头部，然后按SRT逻辑处理）\"\"\"\n # 去掉WEBVTT头部\n content = re.sub(r'^WEBVTT.*?\\n\\n', '', content, flags=re.DOTALL)\n # 去掉NOTE块\n content = re.sub(r'NOTE.*?\\n\\n', '', content, flags=re.DOTALL)\n return clean_srt(content)\n\n\ndef main():\n if len(sys.argv) \u003c 2:\n print(\"用法: python3 srt_to_transcript.py \u003cinput.srt|input.vtt> [output.txt]\")\n sys.exit(1)\n\n input_path = Path(sys.argv[1])\n if not input_path.exists():\n print(f\"❌ 文件不存在: {input_path}\")\n sys.exit(1)\n\n # 默认输出文件名\n if len(sys.argv) >= 3:\n output_path = Path(sys.argv[2])\n else:\n output_path = input_path.parent / f\"{input_path.stem}_transcript.txt\"\n\n # 读取并检测格式\n content = input_path.read_text(encoding='utf-8')\n\n if input_path.suffix.lower() == '.vtt' or content.startswith('WEBVTT'):\n transcript = clean_vtt(content)\n else:\n transcript = clean_srt(content)\n\n output_path.write_text(transcript, encoding='utf-8')\n\n # 统计\n word_count = len(transcript)\n line_count = transcript.count('\\n') + 1\n print(f\"✅ 转换完成: {output_path}\")\n print(f\" 字数: {word_count} 段落数: {line_count}\")\n\n\nif __name__ == '__main__':\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":3115,"content_sha256":"fe9de1d39243920f1c28fee519faa45c18230c67e41d1c34d4af83cdbc4f6b8b"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"女娲 · Skill造人术","type":"text"}]},{"type":"blockquote","content":[{"type":"paragraph","content":[{"text":"「写不进去的那部分，才是你真正的护城河。」——但写得进去的部分，已经足够强大。","type":"text"}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"核心理念","type":"text"}]},{"type":"paragraph","content":[{"text":"女娲不是复制人，是","type":"text"},{"text":"提炼思维框架","type":"text","marks":[{"type":"strong"}]},{"text":"。","type":"text"}]},{"type":"paragraph","content":[{"text":"一个好的人物Skill是一套可运行的认知操作系统：","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"他用什么","type":"text"},{"text":"心智模型","type":"text","marks":[{"type":"strong"}]},{"text":"看世界？（镜片）","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"他用什么","type":"text"},{"text":"决策启发式","type":"text","marks":[{"type":"strong"}]},{"text":"做判断？（直觉规则）","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"他怎么","type":"text"},{"text":"表达","type":"text","marks":[{"type":"strong"}]},{"text":"？（DNA）","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"他","type":"text"},{"text":"绝对不会","type":"text","marks":[{"type":"strong"}]},{"text":"做什么？（反模式）","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"什么是这个Skill","type":"text"},{"text":"做不到的","type":"text","marks":[{"type":"strong"}]},{"text":"？（诚实边界）","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"关键区分","type":"text","marks":[{"type":"strong"}]},{"text":"：捕捉的是HOW they think，不是WHAT they said。","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"执行流程","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Phase 0: 入口分流","type":"text"}]},{"type":"paragraph","content":[{"text":"收到用户输入后，先判断属于哪条路径：","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"用户输入","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"路径","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"示例","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"明确的人名/主题","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"直接路径","type":"text","marks":[{"type":"strong"}]},{"text":" → Phase 0A","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"「蒸馏芒格」「做一个费曼skill」","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"模糊的需求/困惑","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"诊断路径","type":"text","marks":[{"type":"strong"}]},{"text":" → Phase 0B","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"「我想提升决策质量」「有没有一种思维方式能帮我看透商业本质」","type":"text"}]}]}]}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":3},"content":[{"text":"Phase 0A: 需求澄清（直接路径）","type":"text"}]},{"type":"paragraph","content":[{"text":"收到明确名字后，确认：","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"这个人/主题是谁","type":"text","marks":[{"type":"strong"}]},{"text":"：确保理解正确","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"聚焦方向","type":"text","marks":[{"type":"strong"}]},{"text":"（可选）：全面画像 vs 聚焦某个维度？","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"用途","type":"text","marks":[{"type":"strong"}]},{"text":"：思维顾问？决策参考？角色扮演？","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"新建 or 更新","type":"text","marks":[{"type":"strong"}]},{"text":"：是否已有该人物的Skill？（检查 ","type":"text"},{"text":".claude/skills/","type":"text","marks":[{"type":"code_inline"}]},{"text":" 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创建者：[花叔](https://x.com/AlchainHust)","type":"text","marks":[{"type":"code_inline"}]}]}]}]}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"回答工作流（Agentic Protocol）生成指引","type":"text"}]},{"type":"paragraph","content":[{"text":"为什么需要这个段落","type":"text","marks":[{"type":"strong"}]},{"text":"：让人物不只是「说得像」，还「做得像」。没有这个段落，人物Skill遇到需要事实的问题时会凭训练语料编造，而不是像真人一样先做功课再发言。这是人物Skill从「鹦鹉学舌」升级为「可靠思维顾问」的关键。","type":"text"}]},{"type":"paragraph","content":[{"text":"位置","type":"text","marks":[{"type":"strong"}]},{"text":"：放在「角色扮演规则」之后、「示例对话」之前。","type":"text"}]},{"type":"paragraph","content":[{"text":"生成规则","type":"text","marks":[{"type":"strong"}]},{"text":"：","type":"text"}]},{"type":"paragraph","content":[{"text":"生成的Agentic Protocol必须包含以下3个Step，其中Step 2的研究维度必须","type":"text"},{"text":"根据蒸馏出的心智模型自动推导","type":"text","marks":[{"type":"strong"}]},{"text":"，不是固定模板：","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"markdown"},"content":[{"text":"## 回答工作流（Agentic Protocol）\n\n**核心原则：[人物名]不凭感觉说话。遇到需要事实支撑的问题时，先做功课再回答。**\n\n### Step 1: 问题分类\n\n收到问题后，先判断类型：\n\n| 类型 | 特征 | 行动 |\n|------|------|------|\n| **需要事实的问题** | 涉及具体公司/人物/事件/产品/市场现状 | → 先研究再回答（Step 2） |\n| **纯框架问题** | 抽象价值观、思维方式、人生建议 | → 直接用心智模型回答（跳到Step 3） |\n| **混合问题** | 用具体案例讨论抽象道理 | → 先获取案例事实，再用框架分析 |\n\n**判断原则**：如果回答质量会因为缺少最新信息而显著下降，就必须先研究。宁可多搜一次，也不要凭训练语料编造。\n\n### Step 2: [人物名]式研究（按问题类型选择）\n\n**⚠️ 必须使用工具（WebSearch等）获取真实信息，不可跳过。**\n\n[根据此人的心智模型和分析偏好，生成3-5个研究维度分类，每个分类下列出4-6个具体研究点]\n\n#### 研究输出格式\n研究完成后，先在内部整理事实摘要（不输出给用户），然后进入Step 3。\n用户看到的不是调研报告，而是[人物名]基于真实信息做出的判断。\n\n### Step 3: [人物名]式回答\n\n基于Step 2获取的事实（如有），运用心智模型和表达DNA输出回答。","type":"text"}]},{"type":"paragraph","content":[{"text":"Step 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Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.