News Aggregator Skill Fetch real-time hot news from 44+ sources (including international news + AI curated aggregators + user-defined OPML feeds), generate deep analysis reports in Chinese. --- 🔄 Universal Workflow (3 Steps) Every news request follows the same workflow, regardless of source or combination: Step 1: Fetch Data Step 2: Generate Report Read the output JSON and format every item using the Unified Report Template below. Translate all content to Simplified Chinese . Step 3: Save & Present Save the report to , then display the full content to the user. --- 📰 Unified Report Template…

, '', text).strip()\n return text\n\ndef parse_rss_content(content, source_name, limit=5):\n \"\"\"\n Parses RSS/Atom content string (XML or HTML) and returns items.\n \"\"\"\n try:\n # Use html.parser which is built-in and lenient \n soup = BeautifulSoup(content, 'html.parser')\n \n items = []\n entries = soup.find_all(['item', 'entry'])\n \n for entry in entries:\n # --- Title ---\n title_tag = entry.find('title')\n if not title_tag: continue\n title = clean_text(title_tag.get_text())\n \n # --- Link ---\n link = \"\"\n link_tag = entry.find('link')\n if link_tag:\n if link_tag.has_attr('href'):\n link = link_tag['href']\n elif link_tag.get_text(strip=True):\n link = link_tag.get_text(strip=True)\n if not link:\n link = str(link_tag.next_sibling).strip()\n \n if not link:\n guid = entry.find('guid')\n if guid and guid.get_text(strip=True).startswith('http'):\n link = guid.get_text(strip=True)\n\n # --- Time ---\n pub = entry.find(['pubdate', 'published', 'updated', 'dc:date'])\n time_str = clean_text(pub.get_text()) if pub else \"\"\n \n # --- Summary / Content ---\n content_encoded = entry.find('content:encoded')\n description = entry.find('description')\n summary = entry.find('summary')\n content = entry.find('content')\n \n raw_summary = \"\"\n if content_encoded: raw_summary = content_encoded.get_text()\n elif description: raw_summary = description.get_text()\n elif summary: raw_summary = summary.get_text()\n elif content: raw_summary = content.get_text()\n \n soup_desc = BeautifulSoup(raw_summary, 'html.parser')\n _summary_text = soup_desc.get_text(separator=' ', strip=True)\n clean_summary = (_summary_text[:300] + \"...\") if len(_summary_text) > 300 else _summary_text\n \n # --- Heat ---\n heat = \"\"\n comments = entry.find('slash:comments')\n if comments:\n heat = f\"{comments.get_text(strip=True)} comments\"\n \n items.append({\n \"source\": source_name,\n \"title\": title,\n \"url\": link,\n \"time\": time_str,\n \"heat\": heat,\n \"summary\": clean_summary\n })\n if len(items) >= limit: break\n \n return items\n except Exception as e:\n print(f\"Content Parse failed: {e}\", file=sys.stderr)\n return []\n\ndef fetch_rss_feed(url, source_name, limit=5):\n \"\"\"\n Robust RSS/Atom fetcher using BeautifulSoup.\n Handles various feed formats (RSS 2.0, Atom, etc.)\n \"\"\"\n # User-Agent is critical\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8\"\n }\n\n last_error = None\n for attempt in range(3):\n try:\n response = requests.get(url, headers=headers, timeout=15, verify=False)\n response.raise_for_status()\n response.encoding = response.apparent_encoding or 'utf-8'\n return parse_rss_content(response.content, source_name, limit)\n except Exception as e:\n last_error = e\n if attempt \u003c 2:\n time.sleep(1 + attempt)\n\n print(f\"RSS Fetch failed for {url}: {last_error}\", file=sys.stderr)\n return []\n","content_type":"text/x-python; charset=utf-8","language":"python","size":4255,"content_sha256":"80f7a89cb686d7e63a3eac1c1fc7c1d05be716520c44b9f2fd43d9a4e13829b3"},{"filename":"scripts/test_all_sources.py","content":"\nimport subprocess\nimport os\nimport sys\nimport shutil\nfrom datetime import datetime\n\ndef get_all_sources():\n \"\"\"Retrieve all source keys from fetch_news.py --list-sources\"\"\"\n cmd = [sys.executable, 'scripts/fetch_news.py', '--list-sources']\n result = subprocess.run(cmd, capture_output=True, text=True)\n if result.returncode != 0:\n print(f\"Error listing sources: {result.stderr}\")\n return []\n \n lines = result.stdout.strip().split('\\n')\n sources = []\n start_collecting = False\n for line in lines:\n if line.startswith('---'):\n start_collecting = True\n continue\n if start_collecting and line.strip():\n key = line.split('|')[0].strip()\n if key:\n sources.append(key)\n return sources\n\ndef test_source(source, out_dir):\n \"\"\"Run fetch_news.py for a single source\"\"\"\n print(f\"Testing {source}...\", end=' ', flush=True)\n cmd = [\n sys.executable, \n 'scripts/fetch_news.py', \n '--source', source, \n '--limit', '1', \n '--save',\n '--outdir', out_dir\n ]\n \n start_time = datetime.now()\n try:\n # Timeout after 30s to prevent hanging\n result = subprocess.run(cmd, capture_output=True, text=True, timeout=45)\n duration = (datetime.now() - start_time).total_seconds()\n \n if result.returncode == 0:\n # Check if file was actually created\n # fetch_news prints \"[Saved] Raw Data: ...JSON\"\n if \"Raw Data:\" in result.stderr:\n print(f\"✅ PASS ({duration:.1f}s)\")\n return True, duration, None\n else:\n # It might have returned empty list [] and not saved anything?\n # fetch_news only saves if data is not empty? \n # Let's check logic: \"if args.save or ... md_file = save_report...\"\n # save_report writes file even if data is empty? \n # \"if not data: f.write('No items found.')\" -> Yes.\n # So if it didn't save, something else happened.\n print(f\"⚠️ EMPTY/NO-SAVE ({duration:.1f}s)\")\n return False, duration, \"Script ran but no save message found\"\n else:\n print(f\"❌ FAIL ({duration:.1f}s)\")\n return False, duration, result.stderr.strip()\n \n except subprocess.TimeoutExpired:\n print(\"❌ TIMEOUT (45s)\")\n return False, 45, \"Timeout\"\n except Exception as e:\n print(f\"❌ ERROR: {e}\")\n return False, 0, str(e)\n\ndef main():\n # Setup\n test_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'test_results')\n if os.path.exists(test_dir):\n shutil.rmtree(test_dir)\n os.makedirs(test_dir)\n \n print(f\"Starting systematic source test...\")\n print(f\"Output directory: {test_dir}\\n\")\n \n sources = get_all_sources()\n print(f\"Found {len(sources)} sources to test.\\n\")\n \n results = []\n \n for source in sources:\n success, duration, error = test_source(source, test_dir)\n results.append({\n 'source': source,\n 'success': success,\n 'duration': duration,\n 'error': error\n })\n \n # Generate Summary\n summary_path = os.path.join(test_dir, 'summary.md')\n with open(summary_path, 'w', encoding='utf-8') as f:\n f.write(\"# 🧪 News Source Test Report\\n\")\n f.write(f\"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\\n\\n\")\n \n passed = [r for r in results if r['success']]\n failed = [r for r in results if not r['success']]\n \n f.write(f\"## Summary\\n\")\n f.write(f\"- **Total**: {len(results)}\\n\")\n f.write(f\"- **Passed**: {len(passed)}\\n\")\n f.write(f\"- **Failed**: {len(failed)}\\n\\n\")\n \n f.write(\"## ❌ Failures\\n\")\n if not failed:\n f.write(\"None! 🎉\\n\")\n else:\n for r in failed:\n f.write(f\"- **{r['source']}**: {r['error']}\\n\")\n \n f.write(\"\\n## 📋 Detailed Results\\n\")\n f.write(\"| Source | Status | Duration | Note |\\n\")\n f.write(\"|---|---|---|---|\\n\")\n for r in results:\n status = \"✅ PASS\" if r['success'] else \"❌ FAIL\"\n error_msg = r['error'] if r['error'] else \"\"\n f.write(f\"| {r['source']} | {status} | {r['duration']:.1f}s | {error_msg} |\\n\")\n \n print(f\"\\nTest passed: {len(passed)}/{len(results)}\")\n print(f\"Report saved to: {summary_path}\")\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":4590,"content_sha256":"97531ca8c6f2f2ed7972afe9657412c6bd724000d989e0624290add950b8509a"},{"filename":"templates.md","content":"# 🗞️ News Aggregator 指令菜单\n\n请回复 **序号** 执行任务。所有报告均自动保存到 `reports/YYYY-MM-DD/` 并以中文呈现。\n\n---\n\n### 🎯 核心新闻源\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 1 | 🦄 硅谷热点 (Hacker News) | `--source hackernews` |\n| 2 | 🐙 开源趋势 (GitHub Trending) | `--source github` |\n| 3 | 🚀 创投快讯 (36Kr) | `--source 36kr` |\n| 4 | 🐱 产品猎人 (Product Hunt) | `--source producthunt` |\n| 5 | 🤓 极客社区 (V2EX) | `--source v2ex` |\n| 6 | 🐧 腾讯科技 (Tencent News) | `--source tencent` |\n| 7 | 📈 华尔街见闻 (WallStreetCN) | `--source wallstreetcn` |\n| 8 | 🔴 微博热搜 (Weibo) | `--source weibo` |\n| 9 | 🤗 HF 每日论文 (Hugging Face) | `--source huggingface` |\n\n---\n\n### 📧 AI 行业内参\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 10 | 🧪 Latent Space AINews (swyx) | `--source latentspace_ainews` |\n| 11 | ChinAI (Jeffrey Ding) | `--source chinai` |\n| 12 | Memia (Ben Reid) | `--source memia` |\n| 13 | Ben's Bites | `--source bensbites` |\n| 14 | One Useful Thing (Ethan Mollick) | `--source oneusefulthing` |\n| 15 | Interconnects (Nathan Lambert) | `--source interconnects` |\n| 16 | AI to ROI | `--source aitoroi` |\n| 17 | KDnuggets | `--source kdnuggets` |\n| 18 | 🧠 全部 AI 内参聚合 | `--source ai_newsletters --limit 3` |\n\n---\n\n### ✍️ 深度思考 & 播客\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 19 | Paul Graham | `--source paulgraham` |\n| 20 | Wait But Why | `--source waitbutwhy` |\n| 21 | James Clear | `--source jamesclear` |\n| 22 | Farnam Street | `--source farnamstreet` |\n| 23 | Scott Young | `--source scottyoung` |\n| 24 | Dan Koe | `--source dankoe` |\n| 25 | 📚 全部文章聚合 | `--source essays --limit 3` |\n| 26 | Lex Fridman Podcast | `--source lexfridman` |\n| 27 | Latent Space (swyx) | `--source latentspace` |\n| 28 | 80,000 Hours | `--source 80000hours` |\n| 29 | 🎧 全部播客聚合 | `--source podcasts --limit 3` |\n\n---\n\n### ☕️ 每日早报 (Daily Briefings)\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 30 | 🌅 综合早报 (General) | `daily_briefing.py --profile general --no-save` |\n| 31 | 💰 财经早报 (Finance) | `daily_briefing.py --profile finance --no-save` |\n| 32 | 🤖 科技早报 (Tech) | `daily_briefing.py --profile tech --no-save` |\n| 33 | 🍉 吃瓜早报 (Social) | `daily_briefing.py --profile social --no-save` |\n| 34 | 🧠 AI 深度日报 (AI Daily) | `daily_briefing.py --profile ai_daily --no-save` |\n| 35 | 📚 深度阅读清单 | `daily_briefing.py --profile reading_list --no-save` |\n\n---\n\n### 🆕 扩展源 (v2)\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 36 | 🦞 Lobsters 技术深度 | `--source lobsters` |\n| 37 | 👩‍💻 Dev.to 开发者热门 | `--source devto` |\n| 38 | 📜 arXiv AI 最新论文 (cs.AI/CL/LG) | `--source arxiv` |\n| 39 | 📕 少数派 (sspai) | `--source sspai` |\n| 40 | 💻 InfoQ 中文 (软件工程/AI) | `--source infoq_cn --deep` |\n\n---\n\n### 🎯 AI 精选聚合 (v3) —— 二次精选，密度最高\n\n> 这一档是别人的编辑团队替你筛过的 AI 高价值内容，单源信息密度顶 5-10 个原始信源。\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 41 | 🔥 AIHOT 中文 AI 精选（跨源 + 中文编辑稿） | `--source aihot` |\n| 42 | 📨 TLDR AI（英文日刊，每天 5-10 主题摘要） | `--source tldr_ai` |\n| 43 | 📜 Import AI by Jack Clark（英文周刊深度评论） | `--source import_ai --deep` |\n| 44 | 🌐 AI 精选三件套（一次拉全） | `--source aihot,tldr_ai,import_ai` |\n\n---\n\n### 🔧 自定义订阅源\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 45 | 🔧 我的订阅源 (OPML) | `--source user` |\n\n> 💡 **首次使用 OPML（45）**：先 `cp user_sources.opml.example user_sources.opml`，编辑里面的 `\u003coutline xmlUrl=\"...\">` 加自己的源；或从 Feedly/Inoreader 导出 OPML 覆盖即可。\n\n---\n\n### 🌍 国际新闻源\n\n| # | 名称 | 命令 |\n|---|---|---|\n| 46 | 🌍 国际新闻聚合 (最近 24h) | `--source international --limit 20` |\n| 47 | 📰 BBC Top News (最近 24h) | `--source bbc_top` |\n| 48 | 🌐 BBC World (最近 24h) | `--source bbc_world` |\n| 49 | 🈶 BBC 中文 (最近 24h) | `--source bbc_chinese` |\n| 50 | 🗞️ The Guardian World (最近 24h) | `--source guardian_world` |\n| 51 | 🛰️ Al Jazeera (最近 24h) | `--source aljazeera` |\n| 52 | 🇫🇷 France 24 (最近 24h) | `--source france24` |\n| 53 | 🧭 Reuters fallback (Google News RSS, 最近 24h) | `--source reuters` |\n\n> 💡 **输出与时间窗口**：国际新闻源必须按统一报告模板输出；抓取只保留最近 24 小时内容，不用旧闻补位。`reuters` 使用 Google News RSS 的 `site:reuters.com` 检索结果。Reuters 官方公开 RSS 不稳定；如有 Reuters Connect 账号，可把 authenticated RSS 放进 OPML。\n\n---\n\n### 🔀 自由组合\n\n直接指定多个源，用逗号分隔：\n\n```\nhackernews,github,wallstreetcn\n```\n\n例如：*\"帮我看看 HN 和 GitHub 今天有什么热点\"* → Agent 自动执行 `--source hackernews,github`\n\n---\n\n**✨ 请输入序号 (1-53) 或源名组合来执行**\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":5129,"content_sha256":"3a1d68164c112fd8d80e7b1ed170433facb5f19358f6bfaff77756bc9851dcf2"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"News Aggregator Skill","type":"text"}]},{"type":"paragraph","content":[{"text":"Fetch real-time hot news from 44+ sources (including international news + AI curated aggregators + user-defined OPML feeds), generate deep analysis reports in Chinese.","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"🔄 Universal Workflow (3 Steps)","type":"text"}]},{"type":"paragraph","content":[{"text":"Every","type":"text","marks":[{"type":"strong"}]},{"text":" news request follows the same workflow, regardless of source or combination:","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Step 1: Fetch Data","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Single source\npython3 scripts/fetch_news.py --source \u003csource_key> --no-save\n\n# Multiple sources (comma-separated)\npython3 scripts/fetch_news.py --source hackernews,github,wallstreetcn --no-save\n\n# All sources (broad scan)\npython3 scripts/fetch_news.py --source all --limit 15 --deep --no-save\n\n# With keyword filter (auto-expand: \"AI\" → \"AI,LLM,GPT,Claude,Agent,RAG\")\npython3 scripts/fetch_news.py --source hackernews --keyword \"AI,LLM,GPT\" --deep --no-save","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Step 2: Generate Report","type":"text"}]},{"type":"paragraph","content":[{"text":"Read the output JSON and format ","type":"text"},{"text":"every","type":"text","marks":[{"type":"strong"}]},{"text":" item using the ","type":"text"},{"text":"Unified Report Template","type":"text","marks":[{"type":"strong"}]},{"text":" below. Translate all content to ","type":"text"},{"text":"Simplified Chinese","type":"text","marks":[{"type":"strong"}]},{"text":".","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Step 3: Save & Present","type":"text"}]},{"type":"paragraph","content":[{"text":"Save the report to ","type":"text"},{"text":"reports/YYYY-MM-DD/\u003csource>_report.md","type":"text","marks":[{"type":"code_inline"}]},{"text":", then display the full content to the user.","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"📰 Unified Report Template","type":"text"}]},{"type":"paragraph","content":[{"text":"All sources use this single template.","type":"text","marks":[{"type":"strong"}]},{"text":" Show/hide optional fields based on data availability.","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"markdown"},"content":[{"text":"#### N. [标题 (中文翻译)](https://original-url.com)\n- **Source**: 源名 | **Time**: 时间 | **Heat**: 🔥 热度值\n- **Links**: [Discussion](hn_url) | [GitHub](gh_url) ← 仅在数据存在时显示\n- **Summary**: 一句话中文摘要。\n- **Deep Dive**: 💡 **Insight**: 深度分析（背景、影响、技术价值）。","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Source-Specific Adaptations","type":"text"}]},{"type":"paragraph","content":[{"text":"Only the ","type":"text"},{"text":"differences","type":"text","marks":[{"type":"strong"}]},{"text":" from the universal template:","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":"Source","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Adaptation","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Hacker News","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"MUST","type":"text","marks":[{"type":"strong"}]},{"text":" include ","type":"text"},{"text":"[Discussion](hn_url)","type":"text","marks":[{"type":"code_inline"}]},{"text":" link","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"GitHub","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Use ","type":"text"},{"text":"🌟 Stars","type":"text","marks":[{"type":"code_inline"}]},{"text":" for Heat, add ","type":"text"},{"text":"Lang","type":"text","marks":[{"type":"code_inline"}]},{"text":" field, add ","type":"text"},{"text":"#Tags","type":"text","marks":[{"type":"code_inline"}]},{"text":" in Deep Dive","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Hugging Face","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Use ","type":"text"},{"text":"🔥 +N","type":"text","marks":[{"type":"code_inline"}]},{"text":" upvotes for Heat, include ","type":"text"},{"text":"[GitHub](url)","type":"text","marks":[{"type":"code_inline"}]},{"text":" if present, write ","type":"text"},{"text":"深度解读","type":"text","marks":[{"type":"strong"}]},{"text":" (not just translate abstract)","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Weibo","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Preserve exact heat text (e.g. \"108万\")","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"AIHOT","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"summary","type":"text","marks":[{"type":"code_inline"}]},{"text":" 已是中文编辑稿，","type":"text"},{"text":"直接引用","type":"text","marks":[{"type":"strong"}]},{"text":"不要再翻译；Heat 字段为空也别造数据；保留 ","type":"text"},{"text":"推荐理由","type":"text","marks":[{"type":"code_inline"}]},{"text":" 风格的一句话点评","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"TLDR 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News Aggregator Skill Fetch real-time hot news from 44+ sources (including international news + AI curated aggregators + user-defined OPML feeds), generate deep analysis reports in Chinese. --- 🔄 Universal Workflow (3 Steps) Every news request follows the same workflow, regardless of source or combination: Step 1: Fetch Data Step 2: Generate Report Read the output JSON and format every item using the Unified Report Template below. Translate all content to Simplified Chinese . Step 3: Save & Present Save the report to , then display the full content to the user. --- 📰 Unified Report Template…