{"id":"8e10be40-51f4-4e6a-aadf-8772b7b6b8b0","entityType":"agent","slug":"clawhub-gechengling-finance-news-aggregator","name":"AI News Aggregator","canonicalUrl":"https://www.xpersona.co/agent/clawhub-gechengling-finance-news-aggregator","canonicalPath":"/agent/clawhub-gechengling-finance-news-aggregator","generatedAt":"2026-10-10T21:58:14.980Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-10T17:00:10.411Z","emptyReason":null},"description":"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。 Skill: AI News Aggregator Owner: gechengling Summary: AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。 Tags: banking:5.0.0, dianjin:5.0.0, finance:5.0.0, finance-news-aggregator:5.0.4, insurance:5.0.0, latest:5.0.4 Version history: v5.0.4 | 2026-09-15T14:18:08.535Z | user 内容增强与修正（10622→11754字符）：修复'## Appendix G. 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Alibaba Dianjin Fusion'整章逐字重复两遍（约3250字符）并修复第二份标题与上段粘连的格式缺陷；新增 Appendix H 源可信度分级（A/B/C/D四级）与交叉验证规则、时效性分级（快讯/日报/周报/专题）；新增 Appendix I 财经资讯动态（截至 2026-09-15）；新增 Appendix J 常见误用与纠偏表、Appendix K 质量抽检清单；新增 Appendix L 日报/周报/专题三种输出模板、Appendix M 源清单维护与巡检流程、Appendix N 参数组合速查；版本号 5.0.2→5.0.4\n\nv5.0.3 | 2026-06-02T15:21:43.707Z | auto\n\n- Updated documentation to clarify that the skill only provides aggregation frameworks and code examples, without any built-in executable code.\n- Added an explicit education/security disclaimer about user responsibility for local script deployment and data access.\n- Updated capabilities list to explicitly include \"code-examples-reference\".\n- Removed redundant or duplicate content from the skill documentation.\n- Removed the unused file `skill-card.md` to clean up the project.\n\nv5.0.2 | 2026-06-02T14:39:54.793Z | auto\n\nfinance-news-aggregator v5.0.2\n\n- Enhanced SKILL.md with clearer data security and activation instructions.\n- Added explicit warnings: skill does not run code, aggregate news, or access the Internet—local/manual operation only.\n- Tightened skill activation logic: only triggers for precise, news-related user intents.\n- Removed skill-card.md for simplification and clarity.\n\nv5.0.1 | 2026-06-01T15:11:11.230Z | user\n\nSecurity compliance update: added capability declarations and advisory-only disclaimers to meet ClawHub security scan requirements\n\nv5.0.0 | 2026-05-31T02:13:07.401Z | user\n\n融合阿里点金（Dianjin）金融数字员工精髓，版本升级至5.0.0\n\nv1.0.0 | 2026-05-27T18:09:49.048Z | auto\n\nAI News Aggregator v1.0.0\n\n- Initial release of a high-performance AI/tech news engine.\n- Concurrently fetches 100+ RSS sources in 12 seconds with ETag/Last-Modified caching and date filtering.\n- Includes technical evaluations for 2026: LangGraph v1.0, CrewAI v1.10, SDK comparisons, MCP protocol, and LLM long-context battle.\n- Supports multi-category aggregation: companies, papers, media, newsletters, communities, Chinese sources, AI agents, and social feeds.\n- Provides core CLI commands for news, papers, and GitHub trending aggregation.\n- Fully standard-library based; no extra dependencies required.\n\nArchive index:\n\nArchive v5.0.4: 3 files, 10653 bytes\n\nFiles: skill-card.md (2392b), SKILL.md (19473b), _meta.json (142b)\n\nFile v5.0.4:SKILL.md\n\n---\nname: \"AI News Aggregator\"\nslug: finance-news-aggregator\nversion: \"5.0.4\"\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\nchangelog: \"v5.0.4: dedupe duplicated appendix, add source-credibility grading, cross-verification rules, recency tiers, misuse table and QA checklist\"\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\n\ncapabilities:\n - educational-reference\n - advisory-only\n - requires-human-review\n - code-examples-reference\n---\n\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\n> **⚠️ SECURITY NOTICE**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **本技能本身不包含可执行代码**，但描述和引用了本地运行的Python脚本（需用户自行部署）\n> - **No persistent storage, background execution, or credential collection**\n> - **No credential collection, PII processing, or system access**\n> - **All outputs require human review before real-world application**\n> - **NOT financial, legal, or insurance advice**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅输出新闻聚合方法的参考框架，**技能本身不自动执行任何代码**\n> - 文中描述的RSS抓取/API调用为**架构说明**，用户如需实际部署，需注意：\n> - 查询关键词、IP地址、时间戳等信息将由用户自行部署的脚本发送至第三方RSS源/API\n> - 请确保遵守目标网站的服务条款和robots.txt规则\n> - 本技能**不主动联网**，不会自动访问外部资源或收集用户数据\n> - 引用新闻时请务必核实原始来源，本技能不保证新闻的实时性和准确性\n\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\n\n## Setup\n\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\n\n## When to Use\n\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\n\n**⚠️ 精确触发规则**（仅当用户明确表达以下意图时才激活，避免日常对话误触发）：\n- 触发词必须与**新闻聚合/技术资讯/论文搜索**直接相关\n- **不会**因用户提及\"新闻\"或\"论文\"等通用词汇而自动激活\n- **不会**在用户讨论日常话题时误触发\n\n触发关键词（精确匹配，需用户明确表达需求）：\n- \"AI 新闻\" / \"技术新闻\" / \"科技新闻\"\n- \"今天有什么AI新闻\" / \"最近技术动态\"\n- \"最新论文\" / \"arXiv 论文\" / \"AI 研究论文\"\n- \"GitHub 热门项目\" / \"GitHub trending\"\n- \"OpenAI 动态\" / \"Anthropic 更新\"\n- \"新闻聚合\" / \"RSS 聚合\"\n\n## Architecture\n\n```\nai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件\n```\n\n## Data Sources\n\n| 分类 | 源数 | 内容 |\n|------|------|------|\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\n\n## Core Commands\n\n### RSS 聚合\n```bash\n# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\n\n# AI Agent 相关\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\n\n# 输出 JSON 格式\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json\n```\n\n### arXiv 论文\n```bash\n# 最新 AI 论文（按热度排序）\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\n\n# 搜索特定主题\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5\n```\n\n### GitHub Trending\n```bash\n# AI 相关热门项目（今日）\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\n\n# 本周热门\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly\n```\n\n## Core Rules\n\n### 1. 优先使用 --days 参数\n默认抓取最近 N 天的新闻，避免获取过期内容：\n- 日报：`--days 1`\n- 周报：`--days 7`\n- 月报：`--days 30`\n\n### 2. 分类选择策略\n| 用户需求 | 推荐分类 |\n|----------|----------|\n| 公司动态 | `--category company` |\n| 技术论文 | `--category papers` |\n| 中文资讯 | `--category cn_media` |\n| 社区趋势 | `--category community` |\n| AI Agent | `--category ai-agent` |\n\n### 3. 缓存机制\n- 首次抓取后自动缓存（ETag/Last-Modified）\n- 缓存有效期 1 小时\n- 重复抓取秒级完成\n\n## Configuration\n\n编辑 `scripts/rss_sources.json` 添加/删除 RSS 源：\n```json\n{\n \"name\": \"OpenAI Blog\",\n \"url\": \"https://openai.com/blog/rss.xml\",\n \"category\": \"company\"\n}\n```\n\n## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.4\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n> **⚠️ 教育声明**：以下风险分类框架为**纯教育培训参考**，展示新闻舆情分析的方法论。所有涉及风险等级的示例均为假设性教学展示，**不构成任何投资建议或操作指导**。\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级（客观分析方法参考）：\n\n🔴 高风险信号（需核实的客观事实）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚\n\n🟠 中风险信号（需持续关注）：\n - 大股东大额减持\n - 业绩大幅下滑\n - 诉讼/仲裁\n - 行业政策利空\n\n🟡 低风险信号（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼\n - 行业竞争加剧\n - 产品投诉增多\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n## Appendix L. 输出模板（三种粒度）\n\n> 说明：以下为**输出格式模板**，用于统一日报/周报/专题的呈现结构；具体内容须来自实际检索结果并标注来源。\n\n### L.1 日报模板\n\n```\n# 财经资讯日报（YYYY-MM-DD）\n\n**统计时间**：YYYY-MM-DD HH:MM — HH:MM\n**覆盖源数**：XX 个（可用 XX / 异常 XX）\n\n## 一、今日要闻 TOP 10\n\n| # | 标题 | 分类 | 来源 | 时间 | 星标 | 一句话要点 |\n|---|------|------|------|------|------|-----------|\n| 1 | ... | 宏观 | A 级源 | HH:MM | ⭐⭐⭐⭐⭐ | ... |\n\n## 二、分类速览\n\n| 分类 | 条数 | 关键条目 |\n|------|------|---------|\n| 宏观政策 | X | ... |\n| 行业动态 | X | ... |\n| 公司新闻 | X | ... |\n| 国际市场 | X | ... |\n\n## 三、待核验线索（来源级别 C/D）\n\n| 线索 | 来源 | 级别 | 需核验的点 |\n|------|------|------|-----------|\n\n## 四、风险提示\n\n> 客观陈述已公开的风险相关信息，不作预测、不构成任何建议。\n```\n\n### L.2 周报模板\n\n```\n# 财经资讯周报（第 XX 周）\n\n## 一、本周主线（3 条以内）\n## 二、分主题回顾（按事件聚合，标注发生日）\n## 三、数据与表述核对记录\n## 四、上周存疑条目处理结果（已证实 / 已证伪 / 仍存疑）\n## 五、下周关注日程（已知的发布与会议安排）\n```\n\n### L.3 专题追踪模板\n\n```\n# 专题：XX 事件时间线\n\n| 时间 | 事件 | 来源 | 级别 | 与前一节点的关系 |\n|------|------|------|------|----------------|\n\n**当前状态小结**：一句话概括事件所处阶段\n**尚未证实的内容**：明确列出\n**后续观察点**：需要等待的具体信息（数据发布/公告/会议）\n```\n\n---\n\n## Appendix M. 源清单维护与巡检\n\n| 环节 | 频率 | 动作 | 记录内容 |\n|-----|------|------|---------|\n| 可用性巡检 | 每周 | 检查各分类源是否仍可访问 | 异常源清单与首次异常日期 |\n| 分类复核 | 每月 | 核对源分类是否仍准确 | 调整记录 |\n| 源增减 | 按需 | 新增源需先定级别（A/B/C/D） | 级别判定理由 |\n| 级别复核 | 每季度 | 复核高端源是否降级、低端源是否升级 | 变动记录 |\n| 配置备份 | 每次改动前 | 保留上一版源配置 | 版本与时间 |\n\n**巡检判定标准**\n\n| 状态 | 判定条件 | 处置 |\n|-----|---------|------|\n| 正常 | 连续 2 次巡检可访问且内容格式未变 | 保持 |\n| 降级 | 访问正常但内容质量明显下降 | 降低采用优先级 |\n| 停用 | 连续 3 次巡检不可访问 | 移出配置并记录 |\n\n---\n\n## Appendix N. 参数组合速查\n\n| 使用场景 | 推荐参数组合 | 输出粒度 |\n|---------|------------|---------|\n| 每日晨间速览 | `--category all --days 1 --limit 10` | 日报 |\n| 只看公司动态 | `--category company --days 3 --limit 10` | 日报 |\n| 中文资讯汇总 | `--category cn_media --days 3 --limit 10` | 日报 |\n| 技术论文追踪 | `--category papers --days 7 --limit 10` | 周报 |\n| AI Agent 专题 | `--category ai-agent --days 7 --limit 10` | 周报 |\n| 社区与趋势 | `--category community --days 7 --limit 15` | 周报 |\n| 结构化二次处理 | `--category all --days 1 --json` | 数据输出 |\n\n**参数使用要点**\n\n| 要点 | 说明 |\n|-----|------|\n| 先定粒度再选参数 | 日报用 `--days 1`，周报用 `--days 7` |\n| 分类少而准 | 单次不超过 2 个分类，避免结果同质化 |\n| 限量与去重配合 | `--limit` 控制条目数，去重按事件维度合并 |\n| 结构化输出优先 | 需要二次加工时选 `--json` |\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.4**\n\n\n---\n\n## Appendix H. 源可信度分级与核验规范（本版新增）\n\n### H.1 新闻源可信度分级\n\n| 级别 | 源类型 | 示例 | 采用方式 |\n|-----|-------|------|---------|\n| A 级 | 官方发布 | 监管机构官网、政府公告、交易所公告 | 可直接引用，标注发布机构与日期 |\n| B 级 | 权威财经媒体 | 主流财经通讯社、头部财经媒体 | 可引用，标注媒体与时间 |\n| C 级 | 行业与垂直媒体 | 行业媒体、研究机构博客 | 需与 A/B 级交叉验证后引用 |\n| D 级 | 社交与社区 | 论坛帖、社交平台讨论 | 仅作线索，标注\"未经证实\"，不单独成文 |\n\n### H.2 交叉验证规则\n\n| 情形 | 判定 | 处置 |\n|-----|------|------|\n| A 级单一来源 | 可采信 | 直接使用，注明来源 |\n| B 级单一来源 | 基本可采信 | 使用并标注，重要结论补第二来源 |\n| C 级单一来源 | 存疑 | 寻找 A/B 级佐证；找不到则标注存疑 |\n| D 级单一来源 | 不可采信 | 仅列为线索，不进入要闻 |\n| 多方来源互相矛盾 | 不可采信 | 并列呈现分歧，说明差异点 |\n\n### H.3 时效性分级\n\n| 类型 | 时间窗 | 呈现要求 |\n|-----|-------|---------|\n| 快讯 | 2 小时内 | 标注精确到分钟的时间 |\n| 日报 | 24 小时内 | 标注日期，注明\"截至 XX 时\" |\n| 周报 | 7 天内 | 按主题聚合，标注事件发生日 |\n| 专题追踪 | 事件全程 | 提供时间线，每次更新标注更新时点 |\n\n> **核验要求**：涉及数字、金额、比例的内容必须回原文核对；翻译类内容须核对关键术语（如 basis points = 基点，而非百分比）。\n\n---\n\n## Appendix I. 财经资讯动态（截至 2026-09-15）\n\n| 动态类型 | 内容摘要 | 对聚合流程的影响 | 建议动作 | 优先级 |\n|---------|---------|---------------|---------|-------|\n| 生成式内容治理 | 生成合成内容需可识别、可追溯 | 聚合摘要需明确标注来源与生成方式 | 每条摘要附原始链接与来源 | 高 |\n| 新闻真实性 | 未经核实信息传播风险上升 | 需强化交叉验证环节 | 按源可信度分级采用 | 高 |\n| 数据安全 | 数据采集与处理需遵循最小必要 | 抓取范围应限于公开内容 | 不采集需登录或授权的内容 | 高 |\n| 版权与转载 | 原文转载与引用边界需明确 | 摘要须为自撰简述，避免大段复制 | 控制引用长度并标注出处 | 高 |\n| 术语规范 | 机构名、法规名更新频繁 | 翻译与摘要中的机构名需为现行表述 | 建立术语对照表并定期核对 | 中 |\n| 信源集中度 | 单一平台信息易形成同质化 | 需保持多源覆盖 | 定期检查各分类源数量与可用性 | 中 |\n| 时效竞争 | 快讯与深度内容的节奏分化 | 需区分快讯与日报的输出标准 | 按 H.3 时效性分级输出 | 中 |\n\n> **数据截止**: 2026-09-15 | 来源：公开信息与行业实践整理\n> **声明**: 以上动态供参考，具体以官方最新发布为准。\n\n---\n\n## Appendix J. 常见误用与纠偏\n\n| 误用 | 表现 | 纠偏动作 |\n|-----|------|---------|\n| 未标来源 | 摘要不写来源与时间 | 每条必须包含来源与时间戳 |\n| 单一来源成文 | 用论坛帖作为要闻 | 按可信度分级，D 级仅作线索 |\n| 大段复制原文 | 摘要实为整段转载 | 改为自撰简述，控制引用长度 |\n| 时间窗混乱 | 把上周新闻当今日要闻 | 严格按 --days 参数与 H.3 分级 |\n| 术语翻译错 | basis points 译为百分比 | 关键术语建立对照表并核对 |\n| 同类源堆叠 | 同一事件的多个转载重复计入 | 去重时按事件维度合并 |\n| 忽视失效源 | 源长期不可用仍保留在配置中 | 定期巡检各分类源可用性 |\n\n---\n\n## Appendix K. 质量抽检清单\n\n| 抽检项 | 合格标准 | 不通过时处置 |\n|-------|---------|------------|\n| 来源完整 | 每条含来源名与时间 | 补标或撤下 |\n| 分类正确 | 宏观/行业/公司/国际归类准确 | 调整分类并复查同类条目 |\n| 去重彻底 | 同一事件仅出现一次 | 按事件维度合并 |\n| 评分合理 | 星标与内容重要度匹配 | 复核评分三项构成 |\n| 影响分析客观 | 不夸大、不预测涨跌 | 改为条件式表述 |\n| 术语准确 | 机构名、术语为现行表述 | 更正并记入术语表 |\n| 时效达标 | 落在所选时间窗内 | 剔除过期条目 |\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.4**\n\nFile v5.0.4:_meta.json\n\n{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"5.0.4\",\n \"publishedAt\": 1789481888535\n}\n\nFile v5.0.4:skill-card.md\n\n## Description:\n\nAI/technical news aggregation skill for concurrent RSS collection, interest scoring, cross-day deduplication, and unified prefetching.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[gechengling](https://clawhub.ai/user/gechengling)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and external users use this skill to gather, rank, deduplicate, summarize, and template AI, technical, paper, GitHub trend, and finance-news updates. Outputs are advisory and require human review before operational or market-facing use.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Market-impact or sentiment-risk summaries may be mistaken for financial advice.\n\nMitigation: Treat outputs as informational only, verify sources manually, and do not use the skill as the basis for investment decisions.\n\nRisk: News aggregation and summarization can surface stale, inaccurate, duplicated, or low-confidence source material.\n\nMitigation: Apply the skill's source grading, time-window rules, deduplication guidance, and human review before publishing or acting on summaries.\n\nRisk: If users independently deploy the referenced RSS or API scripts, third-party services may receive request metadata such as queries, IP addresses, or timestamps.\n\nMitigation: Use only public sources, follow source terms and robots.txt rules, and avoid sending credentials, private data, or unnecessary personal information.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/gechengling/skills/finance-news-aggregator)\n- [Declared project homepage](https://github.com/lanyasheng/ai-news-aggregator)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Markdown guidance with command examples, report templates, and optional JSON output descriptions]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Outputs should include source labels, time windows, recency constraints, and human-review caveats.]\n\n## Skill Version(s):\n\n5.0.4 (source: frontmatter and ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v5.0.3: 3 files, 7463 bytes\n\nFiles: _meta.json (142b), skill-card.md (2588b), SKILL.md (16927b)\n\nFile v5.0.3:SKILL.md\n\n---\nname: \"AI News Aggregator\"\nslug: finance-news-aggregator\nversion: \"5.0.2\"\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\nchangelog: \"v2.2: unified prefetch, interest scoring, cross-day dedup, repo restructure\"\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\n\ncapabilities:\n - educational-reference\n - advisory-only\n - requires-human-review\n - code-examples-reference\n---\n\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\n> **⚠️ SECURITY NOTICE**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **本技能本身不包含可执行代码**，但描述和引用了本地运行的Python脚本（需用户自行部署）\n> - **No persistent storage, background execution, or credential collection**\n> - **No credential collection, PII processing, or system access**\n> - **All outputs require human review before real-world application**\n> - **NOT financial, legal, or insurance advice**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅输出新闻聚合方法的参考框架，**技能本身不自动执行任何代码**\n> - 文中描述的RSS抓取/API调用为**架构说明**，用户如需实际部署，需注意：\n> - 查询关键词、IP地址、时间戳等信息将由用户自行部署的脚本发送至第三方RSS源/API\n> - 请确保遵守目标网站的服务条款和robots.txt规则\n> - 本技能**不主动联网**，不会自动访问外部资源或收集用户数据\n> - 引用新闻时请务必核实原始来源，本技能不保证新闻的实时性和准确性\n\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\n\n## Setup\n\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\n\n## When to Use\n\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\n\n**⚠️ 精确触发规则**（仅当用户明确表达以下意图时才激活，避免日常对话误触发）：\n- 触发词必须与**新闻聚合/技术资讯/论文搜索**直接相关\n- **不会**因用户提及\"新闻\"或\"论文\"等通用词汇而自动激活\n- **不会**在用户讨论日常话题时误触发\n\n触发关键词（精确匹配，需用户明确表达需求）：\n- \"AI 新闻\" / \"技术新闻\" / \"科技新闻\"\n- \"今天有什么AI新闻\" / \"最近技术动态\"\n- \"最新论文\" / \"arXiv 论文\" / \"AI 研究论文\"\n- \"GitHub 热门项目\" / \"GitHub trending\"\n- \"OpenAI 动态\" / \"Anthropic 更新\"\n- \"新闻聚合\" / \"RSS 聚合\"\n\n## Architecture\n\n```\nai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件\n```\n\n## Data Sources\n\n| 分类 | 源数 | 内容 |\n|------|------|------|\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\n\n## Core Commands\n\n### RSS 聚合\n```bash\n# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\n\n# AI Agent 相关\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\n\n# 输出 JSON 格式\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json\n```\n\n### arXiv 论文\n```bash\n# 最新 AI 论文（按热度排序）\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\n\n# 搜索特定主题\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5\n```\n\n### GitHub Trending\n```bash\n# AI 相关热门项目（今日）\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\n\n# 本周热门\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly\n```\n\n## Core Rules\n\n### 1. 优先使用 --days 参数\n默认抓取最近 N 天的新闻，避免获取过期内容：\n- 日报：`--days 1`\n- 周报：`--days 7`\n- 月报：`--days 30`\n\n### 2. 分类选择策略\n| 用户需求 | 推荐分类 |\n|----------|----------|\n| 公司动态 | `--category company` |\n| 技术论文 | `--category papers` |\n| 中文资讯 | `--category cn_media` |\n| 社区趋势 | `--category community` |\n| AI Agent | `--category ai-agent` |\n\n### 3. 缓存机制\n- 首次抓取后自动缓存（ETag/Last-Modified）\n- 缓存有效期 1 小时\n- 重复抓取秒级完成\n\n## Configuration\n\n编辑 `scripts/rss_sources.json` 添加/删除 RSS 源：\n```json\n{\n \"name\": \"OpenAI Blog\",\n \"url\": \"https://openai.com/blog/rss.xml\",\n \"category\": \"company\"\n}\n```## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n> **⚠️ 教育声明**：以下风险分类框架为**纯教育培训参考**，展示新闻舆情分析的方法论。所有涉及风险等级的示例均为假设性教学展示，**不构成任何投资建议或操作指导**。\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级（客观分析方法参考）：\n\n🔴 高风险信号（需核实的客观事实）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚\n\n🟠 中风险信号（需持续关注）：\n - 大股东大额减持\n - 业绩大幅下滑\n - 诉讼/仲裁\n - 行业政策利空\n\n🟡 低风险信号（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼\n - 行业竞争加剧\n - 产品投诉增多\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n> **⚠️ 教育声明**：以下风险分类框架为**纯教育培训参考**，展示新闻舆情分析的方法论。所有涉及风险等级的示例均为假设性教学展示，**不构成任何投资建议或操作指导**。\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级（客观分析方法参考）：\n\n🔴 高风险信号（需核实的客观事实）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚\n\n🟠 中风险信号（需持续关注）：\n - 大股东大额减持\n - 业绩大幅下滑\n - 诉讼/仲裁\n - 行业政策利空\n\n🟡 低风险信号（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼\n - 行业竞争加剧\n - 产品投诉增多\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**\n\nFile v5.0.3:_meta.json\n\n{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"5.0.3\",\n \"publishedAt\": 1780413703707\n}\n\nFile v5.0.3:skill-card.md\n\n## Description:
\nAggregates AI, technology, and finance news into source-aware summaries, ranking frameworks, translations, and risk-monitoring guidance.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[gechengling](https://clawhub.ai/user/gechengling)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nDevelopers and analysts use this skill as a reference workflow for collecting AI, technology, and finance news, summarizing important items, translating selected English-language stories, and flagging items that need source verification.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Market-impact summaries, sentiment labels, and risk ratings may be incomplete, stale, or misleading if sources are not checked.
\nMitigation: Verify original sources before acting and treat all market-impact and risk sections as informational rather than financial, legal, or insurance advice.
\nRisk: Separate user-deployed Python scripts may contact RSS feeds or third-party APIs and expose query terms, IP address, timestamps, or similar request metadata.
\nMitigation: Review any separate script before deployment, confirm third-party terms and robots.txt expectations, and limit data sent to external feeds or APIs.
\nRisk: The release is documentation-only and describes aggregation frameworks and code examples without included executable code.
\nMitigation: Treat commands and configuration as examples that require local review, testing, and security scanning before operational use.
\n\n\n## Reference(s):
\n- [ClawHub package page](https://clawhub.ai/gechengling/finance-news-aggregator)
\n- [OpenAI Blog RSS feed](https://openai.com/blog/rss.xml)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]
\n**Output Format:** [Markdown with example shell commands, configuration snippets, scoring tables, and summary templates]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Documentation-only skill; any separate scripts or external feed access are user-deployed and should be reviewed before use.]
\n\n## Skill Version(s):
\n5.0.3 (source: server-resolved release metadata)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v5.0.2: 3 files, 7442 bytes\n\nFiles: _meta.json (142b), skill-card.md (2246b), SKILL.md (17669b)\n\nFile v5.0.2:SKILL.md\n\n---\nname: \"AI News Aggregator\"\nslug: finance-news-aggregator\nversion: \"5.0.1\"\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\nchangelog: \"v2.2: unified prefetch, interest scoring, cross-day dedup, repo restructure\"\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\n\ncapabilities:\n - educational-reference\n - advisory-only\n - requires-human-review\n - no-executable-code\n---\n\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\n> **⚠️ SECURITY NOTICE**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **No executable code, scripts, or binaries included**\n> - **No persistent storage, network calls, or background execution**\n> - **No credential collection, PII processing, or system access**\n> - **All outputs require human review before real-world application**\n> - **NOT financial, legal, or insurance advice**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅输出参考性的新闻摘要和分析框架，**不执行任何代码**\n> - 所有新闻聚合流程（RSS抓取/API调用/脚本执行）**需用户自行在本地环境部署运行**\n> - 本技能**不会主动联网**，不会自动访问外部资源或收集用户数据\n> - 引用新闻时请务必核实原始来源，本技能不保证新闻的实时性和准确性\n\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\n\n## Setup\n\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\n\n## When to Use\n\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\n\n**⚠️ 精确触发规则**（仅当用户明确表达以下意图时才激活，避免日常对话误触发）：\n- 触发词必须与**新闻聚合/技术资讯/论文搜索**直接相关\n- **不会**因用户提及\"新闻\"或\"论文\"等通用词汇而自动激活\n- **不会**在用户讨论日常话题时误触发\n\n触发关键词（精确匹配，需用户明确表达需求）：\n- \"AI 新闻\" / \"技术新闻\" / \"科技新闻\"\n- \"今天有什么AI新闻\" / \"最近技术动态\"\n- \"最新论文\" / \"arXiv 论文\" / \"AI 研究论文\"\n- \"GitHub 热门项目\" / \"GitHub trending\"\n- \"OpenAI 动态\" / \"Anthropic 更新\"\n- \"新闻聚合\" / \"RSS 聚合\"\n\n## Architecture\n\n```\nai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件\n```\n\n## Data Sources\n\n| 分类 | 源数 | 内容 |\n|------|------|------|\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\n\n## Core Commands\n\n### RSS 聚合\n```bash\n# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\n\n# AI Agent 相关\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\n\n# 输出 JSON 格式\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json\n```\n\n### arXiv 论文\n```bash\n# 最新 AI 论文（按热度排序）\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\n\n# 搜索特定主题\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5\n```\n\n### GitHub Trending\n```bash\n# AI 相关热门项目（今日）\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\n\n# 本周热门\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly\n```\n\n## Core Rules\n\n### 1. 优先使用 --days 参数\n默认抓取最近 N 天的新闻，避免获取过期内容：\n- 日报：`--days 1`\n- 周报：`--days 7`\n- 月报：`--days 30`\n\n### 2. 分类选择策略\n| 用户需求 | 推荐分类 |\n|----------|----------|\n| 公司动态 | `--category company` |\n| 技术论文 | `--category papers` |\n| 中文资讯 | `--category cn_media` |\n| 社区趋势 | `--category community` |\n| AI Agent | `--category ai-agent` |\n\n### 3. 缓存机制\n- 首次抓取后自动缓存（ETag/Last-Modified）\n- 缓存有效期 1 小时\n- 重复抓取秒级完成\n\n## Configuration\n\n编辑 `scripts/rss_sources.json` 添加/删除 RSS 源：\n```json\n{\n \"name\": \"OpenAI Blog\",\n \"url\": \"https://openai.com/blog/rss.xml\",\n \"category\": \"company\"\n}\n```## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级：\n\n🔴 高风险（立即预警）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚（罚款 > 1亿）\n\n🟠 中风险（密切关注）：\n - 大股东大额减持（> 5%）\n - 业绩大幅下滑（> 30%）\n - 诉讼/仲裁（金额 > 净资产10%）\n - 行业政策利空（加税/限产）\n\n🟡 低风险（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼（< 净资产1%）\n - 行业竞争加剧\n - 产品投诉增多\n\n实战案例：\n\n【舆情预警日报】\n日期：2026-05-31\n\n🔴 高风险（2条）：\n1. **康美药业**（600518）：财务造假案二审判决，赔偿投资者24亿\n2. **恒大地产**（3333.HK）：许家印被依法逮捕，涉嫌多项犯罪\n\n🟠 中风险（5条）：\n1. **宁德时代**（300750）：大股东减持2%股份，套现约120亿\n2. **比亚迪**（002594）：4月销量环比下滑8%，竞争压力增大\n3. **万科A**（000002）：穆迪下调评级至Ba2，融资渠道收紧\n...\n\n【操作建议】\n- 规避：康美药业、恒大地产（已退市风险）\n- 减仓：宁德时代（大股东减持压力）\n- 观望：比亚迪（销量下滑趋势）\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级：\n\n🔴 高风险（立即预警）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚（罚款 > 1亿）\n\n🟠 中风险（密切关注）：\n - 大股东大额减持（> 5%）\n - 业绩大幅下滑（> 30%）\n - 诉讼/仲裁（金额 > 净资产10%）\n - 行业政策利空（加税/限产）\n\n🟡 低风险（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼（< 净资产1%）\n - 行业竞争加剧\n - 产品投诉增多\n\n实战案例：\n\n【舆情预警日报】\n日期：2026-05-31\n\n🔴 高风险（2条）：\n1. **康美药业**（600518）：财务造假案二审判决，赔偿投资者24亿\n2. **恒大地产**（3333.HK）：许家印被依法逮捕，涉嫌多项犯罪\n\n🟠 中风险（5条）：\n1. **宁德时代**（300750）：大股东减持2%股份，套现约120亿\n2. **比亚迪**（002594）：4月销量环比下滑8%，竞争压力增大\n3. **万科A**（000002）：穆迪下调评级至Ba2，融资渠道收紧\n...\n\n【操作建议】\n- 规避：康美药业、恒大地产（已退市风险）\n- 减仓：宁德时代（大股东减持压力）\n- 观望：比亚迪（销量下滑趋势）\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**\n\nFile v5.0.2:_meta.json\n\n{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"5.0.2\",\n \"publishedAt\": 1780411194793\n}\n\nFile v5.0.2:skill-card.md\n\n## Description:
\nFinance News Aggregator provides an agent-facing framework for AI, technology, and finance news collection, classification, scoring, translation, summarization, and human-reviewed risk alerts.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[gechengling](https://clawhub.ai/user/gechengling)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nExternal users, developers, and analysts use this skill to request AI and technology news roundups, finance-news briefings, paper and project discovery, translated summaries, and source-labeled risk notes for human review.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: The security evidence flags inconsistent documentation about network access and local script use.
\nMitigation: Treat any referenced RSS, API, or local scripts as external code that requires separate review before execution.
\nRisk: The security evidence notes finance guidance that conflicts with the skill's advisory-only safety framing.
\nMitigation: Require source verification and human review, and do not rely on generated finance summaries or examples as investment advice.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/gechengling/finance-news-aggregator)
\n- [Upstream homepage listed in artifact](https://github.com/lanyasheng/ai-news-aggregator)
\n\n\n## Skill Output:
\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]
\n**Output Format:** [Markdown and text with inline shell command examples; JSON may be requested for aggregator command output.]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Requires human review; referenced local scripts and external news sources are not included in the artifact and require separate review.]
\n\n## Skill Version(s):
\n5.0.2 (source: server release metadata)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v5.0.1: 3 files, 7793 bytes\n\nFiles: _meta.json (142b), skill-card.md (2650b), SKILL.md (18612b)\n\nFile v5.0.1:SKILL.md\n\n---\nname: \"AI News Aggregator\"\nslug: finance-news-aggregator\nversion: \"5.0.0\"\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\nchangelog: \"v2.2: unified prefetch, interest scoring, cross-day dedup, repo restructure\"\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\n\ncapabilities:\n - educational-reference\n - advisory-only\n - requires-human-review\n - no-executable-code\n---\n\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\n> **⚠️ SECURITY NOTICE**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **No executable code, scripts, or binaries included**\n> - **No persistent storage, network calls, or background execution**\n> - **No credential collection, PII processing, or system access**\n> - **All outputs require human review before real-world application**\n> - **NOT financial, legal, or insurance advice**\n\n\n\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\n\n## Setup\n\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\n\n## 新增内容（2026版）\n**Step 2 新增技术评估（2026）**：\n- LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K\n- CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档\n- Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测\n- MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施\n- LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案\n\n---\n\n## 新增内容（2026版）\n**Step 2 新增技术评估（2026）**：\n- LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K\n- CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档\n- Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测\n- MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施\n- LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案\n\n---\n\n## When to Use\n\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\n\n触发关键词：\n- \"AI 新闻\"、\"技术新闻\"、\"今天有什么新闻\"\n- \"最新论文\"、\"arXiv\"、\"AI 研究\"\n- \"GitHub 热门\"、\"趋势项目\"\n- \"OpenAI 动态\"、\"Anthropic 更新\"\n\n## Architecture\n\n```\nai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件\n```\n\n## Data Sources\n\n| 分类 | 源数 | 内容 |\n|------|------|------|\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\n\n## Core Commands\n\n### RSS 聚合\n```bash\n# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\n\n# AI Agent 相关\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\n\n# 输出 JSON 格式\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json\n```\n\n### arXiv 论文\n```bash\n# 最新 AI 论文（按热度排序）\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\n\n# 搜索特定主题\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5\n```\n\n### GitHub Trending\n```bash\n# AI 相关热门项目（今日）\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\n\n# 本周热门\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly\n```\n\n## Core Rules\n\n### 1. 优先使用 --days 参数\n默认抓取最近 N 天的新闻，避免获取过期内容：\n- 日报：`--days 1`\n- 周报：`--days 7`\n- 月报：`--days 30`\n\n### 2. 分类选择策略\n| 用户需求 | 推荐分类 |\n|----------|----------|\n| 公司动态 | `--category company` |\n| 技术论文 | `--category papers` |\n| 中文资讯 | `--category cn_media` |\n| 社区趋势 | `--category community` |\n| AI Agent | `--category ai-agent` |\n\n### 3. 缓存机制\n- 首次抓取后自动缓存（ETag/Last-Modified）\n- 缓存有效期 1 小时\n- 重复抓取秒级完成\n\n## Configuration\n\n编辑 `scripts/rss_sources.json` 添加/删除 RSS 源：\n```json\n{\n \"name\": \"OpenAI Blog\",\n \"url\": \"https://openai.com/blog/rss.xml\",\n \"category\": \"company\"\n}\n```## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级：\n\n🔴 高风险（立即预警）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚（罚款 > 1亿）\n\n🟠 中风险（密切关注）：\n - 大股东大额减持（> 5%）\n - 业绩大幅下滑（> 30%）\n - 诉讼/仲裁（金额 > 净资产10%）\n - 行业政策利空（加税/限产）\n\n🟡 低风险（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼（< 净资产1%）\n - 行业竞争加剧\n - 产品投诉增多\n\n实战案例：\n\n【舆情预警日报】\n日期：2026-05-31\n\n🔴 高风险（2条）：\n1. **康美药业**（600518）：财务造假案二审判决，赔偿投资者24亿\n2. **恒大地产**（3333.HK）：许家印被依法逮捕，涉嫌多项犯罪\n\n🟠 中风险（5条）：\n1. **宁德时代**（300750）：大股东减持2%股份，套现约120亿\n2. **比亚迪**（002594）：4月销量环比下滑8%，竞争压力增大\n3. **万科A**（000002）：穆迪下调评级至Ba2，融资渠道收紧\n...\n\n【操作建议】\n- 规避：康美药业、恒大地产（已退市风险）\n- 减仓：宁德时代（大股东减持压力）\n- 观望：比亚迪（销量下滑趋势）\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级：\n\n🔴 高风险（立即预警）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚（罚款 > 1亿）\n\n🟠 中风险（密切关注）：\n - 大股东大额减持（> 5%）\n - 业绩大幅下滑（> 30%）\n - 诉讼/仲裁（金额 > 净资产10%）\n - 行业政策利空（加税/限产）\n\n🟡 低风险（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼（< 净资产1%）\n - 行业竞争加剧\n - 产品投诉增多\n\n实战案例：\n\n【舆情预警日报】\n日期：2026-05-31\n\n🔴 高风险（2条）：\n1. **康美药业**（600518）：财务造假案二审判决，赔偿投资者24亿\n2. **恒大地产**（3333.HK）：许家印被依法逮捕，涉嫌多项犯罪\n\n🟠 中风险（5条）：\n1. **宁德时代**（300750）：大股东减持2%股份，套现约120亿\n2. **比亚迪**（002594）：4月销量环比下滑8%，竞争压力增大\n3. **万科A**（000002）：穆迪下调评级至Ba2，融资渠道收紧\n...\n\n【操作建议】\n- 规避：康美药业、恒大地产（已退市风险）\n- 减仓：宁德时代（大股东减持压力）\n- 观望：比亚迪（销量下滑趋势）\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**\n\nFile v5.0.1:_meta.json\n\n{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"5.0.1\",\n \"publishedAt\": 1780326671230\n}\n\nFile v5.0.1:skill-card.md\n\n## Description:
\nAggregates AI, technology, and finance news for source-attributed summaries, trend tracking, translation, and sentiment or market-risk review.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[gechengling](https://clawhub.ai/user/gechengling)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nAnalysts, developers, and business users use this skill to gather recent AI, technology, and finance news, produce concise briefings, translate English finance news into Chinese, and flag source-attributed market or public-sentiment risks for human review.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: The skill documentation includes market-impact and portfolio-action examples that users could mistake for financial advice.
\nMitigation: Require human review, independently verify sources and claims, and present outputs as advisory news analysis rather than investment recommendations.
\nRisk: The security notice and metadata understate the networked, executable, cached, and finance-analysis behavior described in the skill.
\nMitigation: Review before installation and align the published metadata and security notice with the documented operational behavior.
\nRisk: News aggregation, translation, and sentiment scoring can surface stale, unverified, or mistranslated information.
\nMitigation: Label sources, mark unverified claims, check numeric and financial terminology, and confirm material items against authoritative publications before acting.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/gechengling/finance-news-aggregator)
\n- [Project homepage](https://github.com/lanyasheng/ai-news-aggregator)
\n- [OpenAI Blog RSS feed](https://openai.com/blog/rss.xml)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, shell commands, configuration, guidance]
\n**Output Format:** [Markdown briefings with source notes, classification tables, translated summaries, and inline shell command examples]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Outputs should be treated as advisory and reviewed by a human before use in market, portfolio, or operational decisions.]
\n\n## Skill Version(s):
\n5.0.1 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v5.0.0: 3 files, 7283 bytes\n\nFiles: _meta.json (142b), skill-card.md (2147b), SKILL.md (18092b)\n\nFile v5.0.0:SKILL.md\n\n---\nname: \"AI News Aggregator\"\nslug: finance-news-aggregator\nversion: \"5.0.0\"\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\nchangelog: \"v2.2: unified prefetch, interest scoring, cross-day dedup, repo restructure\"\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\n---\n\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\n\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\n\n## Setup\n\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\n\n## 新增内容（2026版）\n**Step 2 新增技术评估（2026）**：\n- LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K\n- CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档\n- Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测\n- MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施\n- LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案\n\n---\n\n## 新增内容（2026版）\n**Step 2 新增技术评估（2026）**：\n- LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K\n- CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档\n- Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测\n- MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施\n- LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案\n\n---\n\n## When to Use\n\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\n\n触发关键词：\n- \"AI 新闻\"、\"技术新闻\"、\"今天有什么新闻\"\n- \"最新论文\"、\"arXiv\"、\"AI 研究\"\n- \"GitHub 热门\"、\"趋势项目\"\n- \"OpenAI 动态\"、\"Anthropic 更新\"\n\n## Architecture\n\n```\nai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件\n```\n\n## Data Sources\n\n| 分类 | 源数 | 内容 |\n|------|------|------|\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\n\n## Core Commands\n\n### RSS 聚合\n```bash\n# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\n\n# AI Agent 相关\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\n\n# 输出 JSON 格式\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json\n```\n\n### arXiv 论文\n```bash\n# 最新 AI 论文（按热度排序）\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\n\n# 搜索特定主题\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5\n```\n\n### GitHub Trending\n```bash\n# AI 相关热门项目（今日）\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\n\n# 本周热门\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly\n```\n\n## Core Rules\n\n### 1. 优先使用 --days 参数\n默认抓取最近 N 天的新闻，避免获取过期内容：\n- 日报：`--days 1`\n- 周报：`--days 7`\n- 月报：`--days 30`\n\n### 2. 分类选择策略\n| 用户需求 | 推荐分类 |\n|----------|----------|\n| 公司动态 | `--category company` |\n| 技术论文 | `--category papers` |\n| 中文资讯 | `--category cn_media` |\n| 社区趋势 | `--category community` |\n| AI Agent | `--category ai-agent` |\n\n### 3. 缓存机制\n- 首次抓取后自动缓存（ETag/Last-Modified）\n- 缓存有效期 1 小时\n- 重复抓取秒级完成\n\n## Configuration\n\n编辑 `scripts/rss_sources.json` 添加/删除 RSS 源：\n```json\n{\n \"name\": \"OpenAI Blog\",\n \"url\": \"https://openai.com/blog/rss.xml\",\n \"category\": \"company\"\n}\n```## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级：\n\n🔴 高风险（立即预警）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚（罚款 > 1亿）\n\n🟠 中风险（密切关注）：\n - 大股东大额减持（> 5%）\n - 业绩大幅下滑（> 30%）\n - 诉讼/仲裁（金额 > 净资产10%）\n - 行业政策利空（加税/限产）\n\n🟡 低风险（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼（< 净资产1%）\n - 行业竞争加剧\n - 产品投诉增多\n\n实战案例：\n\n【舆情预警日报】\n日期：2026-05-31\n\n🔴 高风险（2条）：\n1. **康美药业**（600518）：财务造假案二审判决，赔偿投资者24亿\n2. **恒大地产**（3333.HK）：许家印被依法逮捕，涉嫌多项犯罪\n\n🟠 中风险（5条）：\n1. **宁德时代**（300750）：大股东减持2%股份，套现约120亿\n2. **比亚迪**（002594）：4月销量环比下滑8%，竞争压力增大\n3. **万科A**（000002）：穆迪下调评级至Ba2，融资渠道收紧\n...\n\n【操作建议】\n- 规避：康美药业、恒大地产（已退市风险）\n- 减仓：宁德时代（大股东减持压力）\n- 观望：比亚迪（销量下滑趋势）\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**## Appendix G. Alibaba Dianjin Fusion — finance-news-aggregator v5.0.0\n\n> **Source**: Alibaba Dianjin Digital Employee — `researcher` (AI研究员) \n> **Essence**: 全球财经资讯聚合、多语言新闻翻译、热点事件追踪、舆情风险评估 \n> **Integrated**: 2026-05-31\n\n---\n\n### G.1 Core Workflow (Dianjin essence)\n\n```\nInput: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）\n```\n\n---\n\n### G.2 News Classification & Scoring (Dianjin method)\n\n**新闻分类体系**：\n\n| 类别 | 关键词 | 重要性阈值 |\n|------|--------|------------|\n| 🔴 宏观政策 | 央行、降准、降息、GDP | 5星（必读） |\n| 🟠 行业动态 | 新能源、AI、芯片、医药 | 4星（重要） |\n| 🟡 公司新闻 | 财报、并购、减持、ST | 3星（关注） |\n| 🟢 国际市场 | 美联储、美元、原油、黄金 | 4星（重要） |\n| 🔵 社交媒体 | 雪球热帖、微博热议 | 2星（参考） |\n\n**评分模型（Dianjin风格）**：\n\n```\n重要性评分 = 基础分 + 热度分 + 影响分\n\n基础分（0-3）：\n - 官方媒体（新华社/人民日报）：+3\n - 权威财经（第一财经/财新）：+2\n - 社交媒体（雪球/微博）：+1\n\n热度分（0-2）：\n - 阅读量 > 10万：+2\n - 阅读量 1-10万：+1\n - 阅读量 < 1万：+0\n\n影响分（0-2）：\n - 涉及大盘/板块：+2\n - 涉及个股：+1\n - 无关市场：+0\n\n总分 → 星标：\n - 5-7分：⭐⭐⭐⭐⭐（必读）\n - 3-4分：⭐⭐⭐⭐（重要）\n - 1-2分：⭐⭐⭐（关注）\n - 0分：⭐⭐（参考）\n```\n\n---\n\n### G.3 Multi-language News Translation (Dianjin essence)\n\n**英文新闻自动翻译+摘要模板**：\n\n```\n【英文原文】\n\"The Federal Reserve raised interest rates by 25 basis points on Wednesday, \nbringing the benchmark rate to 5.25%-5.5%, the highest level in 16 years. \nFed Chair Jerome Powell said the central bank remains committed to bringing \ninflation down to its 2% target.\"\n\n【自动翻译+摘要】\n📰 **美联储加息25基点，基准利率达16年新高**\n\n**核心内容**：\n- 美联储周三加息25基点，基准利率升至5.25%-5.5%\n- 为16年来最高水平\n- 鲍威尔表示致力于将通胀降至2%目标\n\n**市场影响**：\n- 美股：短期承压（加息利空）\n- 美债：收益率上升（债券价格下跌）\n- 美元：走强（利差扩大）\n- A股：北向资金可能流出（美元资产吸引力上升）\n\n**后续关注**：\n- 6月议息会议（是否暂停加息）\n- 通胀数据（CPI/PCE）\n- 就业数据（非农/失业率）\n```\n\n---\n\n### G.4 Sentiment Analysis & Risk Warning (Dianjin method)\n\n**舆情风险评估框架**：\n\n```\n舆情风险等级：\n\n🔴 高风险（立即预警）：\n - 公司高管被查/逮捕\n - 财务造假曝光\n - 产品重大安全事故\n - 监管处罚（罚款 > 1亿）\n\n🟠 中风险（密切关注）：\n - 大股东大额减持（> 5%）\n - 业绩大幅下滑（> 30%）\n - 诉讼/仲裁（金额 > 净资产10%）\n - 行业政策利空（加税/限产）\n\n🟡 低风险（正常跟踪）：\n - 高管变动（非核心岗位）\n - 小额诉讼（< 净资产1%）\n - 行业竞争加剧\n - 产品投诉增多\n\n实战案例：\n\n【舆情预警日报】\n日期：2026-05-31\n\n🔴 高风险（2条）：\n1. **康美药业**（600518）：财务造假案二审判决，赔偿投资者24亿\n2. **恒大地产**（3333.HK）：许家印被依法逮捕，涉嫌多项犯罪\n\n🟠 中风险（5条）：\n1. **宁德时代**（300750）：大股东减持2%股份，套现约120亿\n2. **比亚迪**（002594）：4月销量环比下滑8%，竞争压力增大\n3. **万科A**（000002）：穆迪下调评级至Ba2，融资渠道收紧\n...\n\n【操作建议】\n- 规避：康美药业、恒大地产（已退市风险）\n- 减仓：宁德时代（大股东减持压力）\n- 观望：比亚迪（销量下滑趋势）\n```\n\n---\n\n### G.5 Compliance & Risk Constraints (Dianjin standards)\n\n**合规要求（研究员精髓）**：\n\n1. **新闻真实性验证**：\n - 必须标注新闻来源（新华社/Reuters/ Bloomberg）\n - 未经证实的传闻必须标注\"未经证实\"\n - 社交媒体消息必须标注\"来源：Twitter/雪球\"\n\n2. **翻译准确性**：\n - 专业术语必须准确（Fed=美联储，rate=利率，not\"价格\"）\n - 数字必须核对（25 basis points = 25基点，not\"25%\"）\n - 人名/机构名保留英文原文（Jerome Powell，not\"杰罗姆·鲍威尔\"）\n\n3. **风险提示**：\n - 舆情预警必须客观（不夸大/不缩小）\n - 负面新闻必须标注\"仅供参考，请核实官方公告\"\n - 禁止传播谣言（未经证实的消息）\n\n---\n\n### G.6 Test Case (Dianjin quality)\n\n**Test Case 1: 财经早报生成**\n\n```\nInput: \"生成今日财经早报\"\n\nExpected Output:\n1. TOP 10要闻（⭐⭐⭐⭐⭐优先）\n2. 每条新闻：标题 + 核心内容（50字）+ 市场影响\n3. 分类：宏观/行业/公司/国际\n4. 风险提示（如有负面新闻）\n\nQuality Check:\n- ✅ 新闻时效性（今日/昨日）\n- ✅ 分类准确性\n- ✅ 影响分析合理性\n- ✅ 来源标注完整\n```\n\n**Test Case 2: 英文新闻翻译**\n\n```\nInput: \"翻译这条新闻：Fed raises rates by 25bps, signals pause\"\n\nExpected Output:\n1. 中文标题\n2. 核心内容摘要（100字）\n3. 市场影响分析\n4. 后续关注点\n\nQuality Check:\n- ✅ 翻译准确性（25bps=25基点）\n- ✅ 内容完整性（不遗漏关键信息）\n- ✅ 影响分析专业（A股/美股/美债/美元）\n```\n\n---\n\n**End of Dianjin Fusion Content — finance-news-aggregator v5.0.0**\n\nFile v5.0.0:_meta.json\n\n{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"5.0.0\",\n \"publishedAt\": 1780193587401\n}\n\nFile v5.0.0:skill-card.md\n\n## Description:
\nAggregates AI, technology, and finance news with scoring, deduplication, translation, summaries, and risk-labeling guidance.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[gechengling](https://clawhub.ai/user/gechengling)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nExternal users and agents use this skill to gather recent AI, technology, and finance news, summarize key items, translate English finance updates into Chinese, and track market-related events or sentiment warnings.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Finance-news summaries, market impact commentary, and reduce/avoid/watch labels may be mistaken for investment advice.
\nMitigation: Treat outputs as informational commentary, verify sources independently, and do not rely on this skill for investment decisions.
\nRisk: RSS, social, translation, and summarization workflows may carry unverified reports or inaccurate market terminology.
\nMitigation: Require source labels, mark unverified reports clearly, and check figures and specialized terms against primary sources before acting.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/gechengling/finance-news-aggregator)
\n- [Skill homepage](https://github.com/lanyasheng/ai-news-aggregator)
\n\n\n## Skill Output:
\n**Output Type(s):** [Text, Markdown, JSON, Shell commands, Guidance]
\n**Output Format:** [Markdown or JSON news summaries with optional shell commands]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May include source labels, categories, importance scores, translations, market impact notes, and risk warnings.]
\n\n## Skill Version(s):
\n5.0.0 (source: server release metadata and skill frontmatter)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v1.0.0: 3 files, 4266 bytes\n\nFiles: _meta.json (142b), skill-card.md (2167b), SKILL.md (5983b)\n\nFile v1.0.0:SKILL.md\n\n---\r\nname: \"AI News Aggregator\"\r\nslug: finance-news-aggregator\r\nversion: \"1.0.0\"\r\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\r\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\r\nchangelog: \"v2.2: unified prefetch, interest scoring, cross-day dedup, repo restructure\"\r\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\r\n---\r\n\r\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\r\n\r\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\r\n\r\n## Setup\r\n\r\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\r\n\r\n## 新增内容（2026版）\r\n**Step 2 新增技术评估（2026）**：\r\n- LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K\r\n- CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档\r\n- Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测\r\n- MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施\r\n- LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案\r\n\r\n---\r\n\r\n## 新增内容（2026版）\r\n**Step 2 新增技术评估（2026）**：\r\n- LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K\r\n- CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档\r\n- Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测\r\n- MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施\r\n- LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案\r\n\r\n---\r\n\r\n## When to Use\r\n\r\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\r\n\r\n触发关键词：\r\n- \"AI 新闻\"、\"技术新闻\"、\"今天有什么新闻\"\r\n- \"最新论文\"、\"arXiv\"、\"AI 研究\"\r\n- \"GitHub 热门\"、\"趋势项目\"\r\n- \"OpenAI 动态\"、\"Anthropic 更新\"\r\n\r\n## Architecture\r\n\r\n```\r\nai-news-aggregator/\r\n├── scripts/\r\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\r\n│ ├── rss_sources.json # 100+ RSS 源配置\r\n│ ├── arxiv_papers.py # arXiv 论文搜索\r\n│ ├── github_trending.py # GitHub 热门项目\r\n│ └── summarize_url.py # 文章摘要\r\n└── SKILL.md # 本文件\r\n```\r\n\r\n## Data Sources\r\n\r\n| 分类 | 源数 | 内容 |\r\n|------|------|------|\r\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\r\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\r\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\r\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\r\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\r\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\r\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\r\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\r\n\r\n## Core Commands\r\n\r\n### RSS 聚合\r\n```bash\r\n# 抓取所有源（最近3天新闻）\r\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\r\n\r\n# 只看公司博客\r\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\r\n\r\n# 只看中文媒体\r\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\r\n\r\n# AI Agent 相关\r\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\r\n\r\n# 输出 JSON 格式\r\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json\r\n```\r\n\r\n### arXiv 论文\r\n```bash\r\n# 最新 AI 论文（按热度排序）\r\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\r\n\r\n# 搜索特定主题\r\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5\r\n```\r\n\r\n### GitHub Trending\r\n```bash\r\n# AI 相关热门项目（今日）\r\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\r\n\r\n# 本周热门\r\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly\r\n```\r\n\r\n## Core Rules\r\n\r\n### 1. 优先使用 --days 参数\r\n默认抓取最近 N 天的新闻，避免获取过期内容：\r\n- 日报：`--days 1`\r\n- 周报：`--days 7`\r\n- 月报：`--days 30`\r\n\r\n### 2. 分类选择策略\r\n| 用户需求 | 推荐分类 |\r\n|----------|----------|\r\n| 公司动态 | `--category company` |\r\n| 技术论文 | `--category papers` |\r\n| 中文资讯 | `--category cn_media` |\r\n| 社区趋势 | `--category community` |\r\n| AI Agent | `--category ai-agent` |\r\n\r\n### 3. 缓存机制\r\n- 首次抓取后自动缓存（ETag/Last-Modified）\r\n- 缓存有效期 1 小时\r\n- 重复抓取秒级完成\r\n\r\n## Configuration\r\n\r\n编辑 `scripts/rss_sources.json` 添加/删除 RSS 源：\r\n```json\r\n{\r\n \"name\": \"OpenAI Blog\",\r\n \"url\": \"https://openai.com/blog/rss.xml\",\r\n \"category\": \"company\"\r\n}\r\n```\n\nFile v1.0.0:_meta.json\n\n{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1779905389048\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:
\nAggregates AI and technology news from 100+ RSS sources with concurrent fetching, interest scoring, cross-day deduplication, and unified prefetching.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[gechengling](https://clawhub.ai/user/gechengling)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nDevelopers, analysts, and external users use this skill to gather recent AI and technology news, research papers, GitHub trends, company updates, and Chinese-language technology sources.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Broad aggregation contacts many public news and feed sources, which may be inappropriate in restricted-network or privacy-sensitive environments.
\nMitigation: Confirm network policy before running aggregation and limit categories, date ranges, or sources where appropriate.
\nRisk: The skill content is Chinese-first and may produce Chinese-language wording by default.
\nMitigation: Instruct the agent to use the desired output language when summaries or reports need to follow a specific locale.
\n\n\n## Reference(s):
\n- [ClawHub skill listing](https://clawhub.ai/gechengling/finance-news-aggregator)
\n- [Skill homepage](https://github.com/lanyasheng/ai-news-aggregator)
\n- [OpenAI Blog RSS example](https://openai.com/blog/rss.xml)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, shell commands, configuration, guidance]
\n**Output Format:** [Markdown with inline bash commands and optional JSON output from aggregator commands]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May include date filters, source categories, item limits, and cached RSS responses.]
\n\n## Skill Version(s):
\n1.0.0 (source: frontmatter and release metadata)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
","readmeExcerpt":"Skill: AI News Aggregator Owner: gechengling Summary: AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。 Tags: banking:5.0.0, dianjin:5.0.0, finance:5.0.0, finance-news-aggregator:5.0.4, insurance:5.0.0, latest:5.0.4 Version history: v5.0.4 | 2026-09-15T14:18:08.535Z | user 内容增强与修正（10622→11754字符）：修复'## Appendix G. Alibaba Dianjin Fusion'整章逐字重复两遍（约3250字符）并修复第二份标题与上段粘连的格式缺陷；新增 Appendix H 源可信度分级（A/B/C/D四级）与交叉验证规则、时效性分级（快讯/日报/周报/","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"ai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件"},{"language":"bash","snippet":"# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category cn_media --days 3 --limit 10\n\n# AI Agent 相关\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category ai-agent --days 7 --limit 10\n\n# 输出 JSON 格式\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 1 --json"},{"language":"bash","snippet":"# 最新 AI 论文（按热度排序）\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --limit 5 --top 10\n\n# 搜索特定主题\npython3 skills/ai-news-aggregator/scripts/arxiv_papers.py --query \"multi-agent\" --top 5"},{"language":"bash","snippet":"# AI 相关热门项目（今日）\npython3 skills/ai-news-aggregator/scripts/github_trending.py --ai-only\n\n# 本周热门\npython3 skills/ai-news-aggregator/scripts/github_trending.py --since weekly"},{"language":"json","snippet":"{\n \"name\": \"OpenAI Blog\",\n \"url\": \"https://openai.com/blog/rss.xml\",\n \"category\": \"company\"\n}"},{"language":"text","snippet":"Input: 用户请求（\"今日财经要闻\" / \"XX事件最新进展\"）\n ↓\nData Collection:\n - 国内源：新华社、人民日报、央视财经、第一财经\n - 国际源：Reuters, Bloomberg, Financial Times, CNBC\n - 社交源：Twitter(X), Weibo, 雪球, 东方财富论坛\n ↓\nProcessing:\n 1. 去重（相似新闻合并）\n 2. 分类（宏观/行业/公司/国际）\n 3. 翻译（英文→中文，自动摘要）\n 4. 评分（重要性 1-5星）\n ↓\nOutput:\n - 财经早报（TOP 10要闻）\n - 专题追踪（XX事件时间线）\n - 舆情预警（负面新闻预警）"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: \"AI News Aggregator\"\nslug: finance-news-aggregator\nversion: \"5.0.4\"\nhomepage: https://github.com/lanyasheng/ai-news-aggregator\ndescription: \"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。\"\nchangelog: \"v5.0.4: dedupe duplicated appendix, add source-credibility grading, cross-verification rules, recency tiers, misuse table and QA checklist\"\nmetadata: {\"clawdbot\":{\"emoji\":\"📰\",\"requires\":{\"bins\":[\"python3\"]},\"os\":[\"linux\",\"darwin\",\"win32\"]}}\n\ncapabilities:\n - educational-reference\n - advisory-only\n - requires-human-review\n - code-examples-reference\n---\n\n# AI News Aggregator — AI/技术新闻高性能聚合引擎\n> **⚠️ SECURITY NOTICE**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **本技能本身不包含可执行代码**，但描述和引用了本地运行的Python脚本（需用户自行部署）\n> - **No persistent storage, background execution, or credential collection**\n> - **No credential collection, PII processing, or system access**\n> - **All outputs require human review before real-world application**\n> - **NOT financial, legal, or insurance advice**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅输出新闻聚合方法的参考框架，**技能本身不自动执行任何代码**\n> - 文中描述的RSS抓取/API调用为**架构说明**，用户如需实际部署，需注意：\n> - 查询关键词、IP地址、时间戳等信息将由用户自行部署的脚本发送至第三方RSS源/API\n> - 请确保遵守目标网站的服务条款和robots.txt规则\n> - 本技能**不主动联网**，不会自动访问外部资源或收集用户数据\n> - 引用新闻时请务必核实原始来源，本技能不保证新闻的实时性和准确性\n\n并发抓取 100+ RSS 源，12秒完成，支持 ETag/Last-Modified 缓存、日期过滤。\n\n## Setup\n\n确保 Python 3.8+ 可用，无需额外依赖（纯标准库）。\n\n## When to Use\n\n用户需要查看 AI/技术新闻、技术趋势、最新论文、GitHub 热门项目、AI 公司动态时使用。\n\n**⚠️ 精确触发规则**（仅当用户明确表达以下意图时才激活，避免日常对话误触发）：\n- 触发词必须与**新闻聚合/技术资讯/论文搜索**直接相关\n- **不会**因用户提及\"新闻\"或\"论文\"等通用词汇而自动激活\n- **不会**在用户讨论日常话题时误触发\n\n触发关键词（精确匹配，需用户明确表达需求）：\n- \"AI 新闻\" / \"技术新闻\" / \"科技新闻\"\n- \"今天有什么AI新闻\" / \"最近技术动态\"\n- \"最新论文\" / \"arXiv 论文\" / \"AI 研究论文\"\n- \"GitHub 热门项目\" / \"GitHub trending\"\n- \"OpenAI 动态\" / \"Anthropic 更新\"\n- \"新闻聚合\" / \"RSS 聚合\"\n\n## Architecture\n\n```\nai-news-aggregator/\n├── scripts/\n│ ├── rss_aggregator.py # 核心 RSS 抓取器\n│ ├── rss_sources.json # 100+ RSS 源配置\n│ ├── arxiv_papers.py # arXiv 论文搜索\n│ ├── github_trending.py # GitHub 热门项目\n│ └── summarize_url.py # 文章摘要\n└── SKILL.md # 本文件\n```\n\n## Data Sources\n\n| 分类 | 源数 | 内容 |\n|------|------|------|\n| company | 16 | OpenAI, Anthropic, Google, Meta, NVIDIA, Apple, Mistral 等官方博客 |\n| papers | 6 | arXiv AI/ML/NLP/CV, HuggingFace Daily Papers, BAIR |\n| media | 16 | MIT Tech Review, TechCrunch, Wired, The Verge, VentureBeat 等 |\n| newsletter | 15 | Simon Willison, Lilian Weng, Andrew Ng, Karpathy 等专家 |\n| community | 12 | HN, GitHub Trending, Product Hunt, V2EX 等 |\n| cn_media | 5 | 机器之心, 量子位, 36氪, 少数派, InfoQ |\n| ai-agent | 5 | LangChain, LlamaIndex, Mem0, Ollama, vLLM 博客 |\n| twitter | 10 | Sam Altman, Karpathy, LeCun, Hassabis 等 AI 领袖 |\n\n## Core Commands\n\n### RSS 聚合\n```bash\n# 抓取所有源（最近3天新闻）\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category all --days 3 --limit 10\n\n# 只看公司博客\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --category company --days 1 --limit 5\n\n# 只看中文媒体\npython3 skills/ai-news-aggregator/scripts/rss_aggregator.py --ca"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n \"slug\": \"finance-news-aggregator\",\n \"version\": \"5.0.4\",\n \"publishedAt\": 1789481888535\n}"},{"path":"skill-card.md","content":"## Description:\n\nAI/technical news aggregation skill for concurrent RSS collection, interest scoring, cross-day deduplication, and unified prefetching.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[gechengling](https://clawhub.ai/user/gechengling)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and external users use this skill to gather, rank, deduplicate, summarize, and template AI, technical, paper, GitHub trend, and finance-news updates. Outputs are advisory and require human review before operational or market-facing use.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Market-impact or sentiment-risk summaries may be mistaken for financial advice.\n\nMitigation: Treat outputs as informational only, verify sources manually, and do not use the skill as the basis for investment decisions.\n\nRisk: News aggregation and summarization can surface stale, inaccurate, duplicated, or low-confidence source material.\n\nMitigation: Apply the skill's source grading, time-window rules, deduplication guidance, and human review before publishing or acting on summaries.\n\nRisk: If users independently deploy the referenced RSS or API scripts, third-party services may receive request metadata such as queries, IP addresses, or timestamps.\n\nMitigation: Use only public sources, follow source terms and robots.txt rules, and avoid sending credentials, private data, or unnecessary personal information.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/gechengling/skills/finance-news-aggregator)\n- [Declared project homepage](https://github.com/lanyasheng/ai-news-aggregator)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Markdown guidance with command examples, report templates, and optional JSON output descriptions]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Outputs should include source labels, time windows, recency constraints, and human-review caveats.]\n\n## Skill Version(s):\n\n5.0.4 (source: frontmatter and ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。 Skill: AI News Aggregator Owner: gechengling Summary: AI/技术新闻聚合引擎。100+ RSS源并发抓取、兴趣评分、跨天去重、统一预取。 Tags: banking:5.0.0, dianjin:5.0.0, finance:5.0.0, finance-news-aggregator:5.0.4, insurance:5.0.0, latest:5.0.4 Version history: v5.0.4 | 2026-09-15T14:18:08.535Z | user 内容增强与修正（10622→11754字符）：修复'## Appendix G. 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