OpenClaw/clawhub-security-signals huggingface dataset:
Skill: reed1898/reminder (version 0.1.1)

--- name: reminder summary: Natural-language reminders → save to your workspace → schedule Telegram notifications (24h/1h/10m by default). description: Natural-language reminder secretary: capture events into git-synced workspace (data/logic separated), schedule Telegram reminders via OpenClaw cron, and answer "what's coming up" queries. Use when user mentions meetings, birthdays, deadlines, or asks for schedule/plans. tags: [reminder, schedule, cron, telegram, secretary] --- # Reminder (secretary) A lightweight personal secretary for OpenClaw: - Tell it events in natural language (Chinese/English). - It extracts structured info and stores it in your workspace (so Git/`claw-roam` can sync across devices). - It schedules Telegram reminders using OpenClaw `cron`. ## What it does - Capture events from chat (meetings / birthdays / deadlines) - Store events in a **workspace data file** (easy to back up & sync via Git/`claw-roam`) - Schedule Telegram reminders using OpenClaw `cron` - Answer queries like “我最近有什么安排/计划？” ## Data (separated from skill) This skill contains **no personal event data**. User data lives in the workspace at: - Events file: `~/.openclaw/workspace/reminders/events.yml` Template (shipped with the skill): - `skills/reminder/assets/events.template.yml` ## Config (env) - `REMINDER_TZ` (default: `Asia/Shanghai`) - `REMINDER_OFFSETS_MINUTES` (default: `1440,60,10` for 24h/1h/10m) ## Capture behavior When user says something like: - “后天上午10点有个会” - “下个月2号我妈生日” - “周五下午三点交报告” Do: 1) Parse the event: - title - start datetime (Shanghai) - notes (optional) - reminders offsets (default 24h/1h/10m) - repeat (optional: yearly/monthly/weekly) 2) If key info is ambiguous (e.g. ‘后天’ date, ‘下个月’ which month, lunar birthday conversion, time missing), ask **only the minimal** clarifying question(s). 3) Write/update the event in `reminders/events.yml`. 4) Create `cron` jobs for each reminder time (delivery to current Telegram). ## Reply style - After scheduling: reply briefly with the resolved datetime + confirmation. - For cancellations/changes: confirm what was changed and whether cron jobs were removed/replaced. ## Queries If user asks: - “我最近有什么安排？” - “下周有什么？” Then read `reminders/events.yml`, compute upcoming items (Shanghai time), and summarize. ## Notes / safety - Never commit machine-specific secrets (keep them in `LOCAL_CONFIG.md`, already gitignored). - For lunar birthdays: store the canonical lunar date + the computed solar date for the target year; ask how to handle leap months when needed.

path: assets/events.template.yml
content: # events.yml (template)
# Timezone: set via env REMINDER_TZ (default: Asia/Shanghai)
# Default reminder offsets (minutes): set via env REMINDER_OFFSETS_MINUTES (default: 1440,60,10)

events: []


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: marcusqin111-boop/evomap-auditor (version 1.3.0)

--- name: evomap-auditor description: Secure execution and auditing of GEP-A2A skills. Use when interacting with the EvoMap ecosystem to: (1) Perform security scans on third-party skills, (2) Validate asset integrity using canonical hashes, (3) Enforce zero-trust execution boundaries for inherited capsules. --- # EvoMap Auditor Skill This skill provides specialized procedures for maintaining security and trust within the EvoMap (GEP-A2A) ecosystem. ## Core Workflows ### 1. Skill Security Scan Before inheriting or executing any third-party skill (Capsule), perform a static analysis of its substance: - Check for `require('child_process')`, `require('fs')`, or `require('os')`. - Flag any use of native Node.js modules that are not explicitly authorized. - Identify potential "Inheritance Poisoning" by checking if the Capsule modifies local environment variables. ### 2. Canonical Integrity Validation Validate that a skill's `asset_id` matches its content using the GEP Canonical JSON standard: 1. Remove `asset_id` from the object. 2. Recursively sort all keys alphabetically. 3. Compute SHA256 hash. 4. Compare with the provided `asset_id`. ### 3. Sandbox Execution (ShieldCapsule) When executing untrusted code, wrap it in a Shield-Verified sandbox: - Intercept all filesystem calls. - Redirect network egress to an allowlist-only proxy. - Log all "Blast Radius" impacts for future auditing. ## Reference - GEP-A2A Protocol: https://evomap.ai/docs/gep - Security Standards: https://evomap.ai/security


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: niyoseris/cerebrun (version 1.0.1)

--- name: cerebrun description: "MCP client for Cerebrun - comprehensive personal context and memory management system. Retrieve user context layers (language, projects, identity, vault), perform semantic search, manage knowledge base, and interact with LLM Gateway. Use when: (1) Retrieving user preferences or context, (2) Searching user's knowledge base, (3) Managing projects and goals, (4) Storing or querying personal information, (5) Accessing conversation history via LLM Gateway" --- # Cerebrun MCP Client Cerebrun (cereb.run) is a Model Context Protocol (MCP) server that provides persistent personal context management across sessions. ## Quick Start All requests require: - `api_key`: Cerebrun API key (Bearer token) - `base_url`: https://cereb.run/mcp ## Context Layers **Layer 0** - Language, timezone, comms prefs **Layer 1** - Projects, goals, pinned memories **Layer 2** - Personal identity info, API and other keys **Layer 3** - Encrypted vault (requires consent) ## Usage ### Get Context curl -X POST \ -H "Authorization: Bearer $API_KEY" \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_context","arguments":{"layer":0}}}' \ https://cereb.run/mcp ### Search Context curl -X POST \ -H "Authorization: Bearer $API_KEY" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"search_context","arguments":{"query":"Rust authentication","limit":5}}}' \ https://cereb.run/mcp ### Push Knowledge curl -X POST \ -H "Authorization: Bearer $API_KEY" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"push_knowledge","arguments":{"content":"Important insight","category":"learning","tags":["rust","performance"]}}}' \ https://cereb.run/mcp ### Chat via Gateway curl -X POST \ -H "Authorization: Bearer $API_KEY" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"chat_with_llm","arguments":{"message":"Hello","provider":"openai","model":"gpt-4"}}}' \ https://cereb.run/mcp ## Tools Reference See [REFERENCES.md](references/REFERENCES.md) for complete API documentation. ## Script Usage ```python scripts/cerebrun.py get_context --layer 0 --api-key YOUR_KEY scripts/cerebrun.py search_context --query "project" --api-key YOUR_KEY scripts/cerebrun.py push_knowledge --content "New idea" --category "todo" --api-key YOUR_KEY ``` ## Configuration Store API key in environment: `CEREBRUN_API_KEY` or pass via `--api-key`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yesong-hue/qnit-seo-content-writer (version 1.0.0)

name: "seo-content-writer-hybrid" version: "1.0.0" title: "SEO内容写作套装" description: "完整的SEO内容写作套件，CORE-EEAT质量标准" tags: "content,seo,writing,productivity" author: "yesong-Hue" --- ## 功能说明 完整的SEO内容写作套件，CORE-EEAT质量标准 ## 触发方式 发送包含技能名称或相关指令的消息即可触发。 ## 使用方法 1. 清晰描述您的需求 2. 系统调用对应技能处理 3. 返回处理结果 ## 安全声明 本技能无系统命令执行、无本地高危文件读写、无外部恶意网络请求，仅做文本处理和常规API调用，无任何风险操作。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anmu14641/private-secrets-1-0-0 (version 1.0.0)

# Private Secrets Skill 用于安全存储和管理你的私密信息（如 API Key、密码、令牌等）。 ## 功能 - **添加私密信息**：记录名称 + 内容 - **查看列表**：查看已存储的信息名称（不含内容） - **读取内容**：查看具体的私密内容 ## 存储位置 `/workspace/skills/private-secrets-1.0.0/secrets.json` ## 使用方式 ### 添加 secret ``` 添加 secret: [名称] = [内容] 例：添加 [REDACTED_SECRET] = sk-xxx ``` ### 查看列表 ``` 列出所有 secrets ``` ### 读取内容 ``` 查看 secret: [名称] ``` ## 安全注意 - 此文件存储在本地，未加密 - 建议定期备份 - 如需更高安全性，可使用加密工具手动加密文件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lovenervermore/rewrite-question (version 1.0.0)

--- name: rewrite_question slug: rewrite_question version: 1.0.2 description: 补全上下文并重写用户提问 metadata: {"clawdbot":{"emoji":"🐬"}} --- # Skill: rewrite_question - Description: 补全上下文并重写用户提问。 - Inputs: [query, history] - Outputs: [rewritten_query] - ID: rewrite_question - Role: 语境重构专家 - 功能描述：分析 history 上下文，消除 query 中的指代不明（它、上周、那边）并补全省略项，输出自包含的标准查询。 - 输入参数： - query (string): 原始输入。 - history (list): 往返对话记忆。 - 输出结果：rewritten_query (string): 补全业务背景后的独立请求。 - 执行策略：代词消解 → 时间对齐 → 实体补全。 ## 独立运行说明 ```bash # 基本用法（输出写入 skills/.workflow/rewrite_output.json） python rewrite_question.py --query "今天汉河店的成交额" # 全量重跑（清空本步及所有后续中间文件） python rewrite_question.py --query "今天汉河店的成交额" --clean ``` ### 输出文件（`skills/.workflow/rewrite_output.json`） ```json { "original_query": "今天汉河店的成交额", "final_query": "2026-03-11 汉河店的成交额", "is_rewritten": true, "confidence": 0.95, "thought": "...", "is_qa_matched": false, "matched_sql": null } ``` ### 下一步 ```bash python ../recognize-intent/recognize_intent.py ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: danpian1/dow-theory (version 1.0.0)

--- name: dow-theory description: 道氏理论（Dow Theory）技术分析 — 股市趋势判断的奠基理论。当用户说「道氏理论」「用道氏理论分析」「确认趋势」「主要趋势」「次级折返」「道氏确认原则」时激活。 --- # 道氏理论（Dow Theory） 道氏理论是所有技术分析的奠基理论，由Charles Dow创立，核心是价格反映一切，通过相互验证确认趋势。 ## 六大基本原则 ### 1. 价格反映一切 > 市场指数的每一次涨跌都包含了宏观经济、政策、情绪等所有信息 ### 2. 三大趋势分类 | 趋势 | 周期 | 幅度 | 参与者 | |------|------|------|--------| | **主要趋势**（Primary） | 1年~数年 | ≥20% | 机构投资者 | | **次级折返**（Secondary） | 3周~数月 | 主要趋势的1/3~2/3 | 中期交易者 | | **短期波动**（Minor） | 几天~3周 | 随机 | 日内交易者 | ### 3. 主要趋势三阶段 ``` 积累期：聪明钱悄悄建仓，价格窄幅波动，舆论偏空 推运期：趋势确认，经济数据向好，更多人跟进 派发期：专业投资者获利了结，舆论极度乐观，天量见顶 ``` ### 4. 相互验证原则 > 两个市场平均指数（工业/运输）必须相互确认，才能确认趋势 - 牛市：两个指数同时创新高 - 熊市：两个指数同时创新低 - 背离：单边突破 = 趋势存疑 ### 5. 成交量确认 ``` 上涨趋势：价格涨时成交量放大，价格跌时成交量缩小 下跌趋势：价格跌时成交量放大，价格反弹时成交量缩小 ``` ### 6. 趋势确认需要收盘价突破 > 只有收盘价才能确认趋势改变，盘中高低价仅供参考 ## 实战应用 ### 趋势判断标准 ``` 上升趋势：连续高点不断抬高，低点不断抬高 下降趋势：连续高点不断压低，低点不断压低 ``` ### 关键转折信号 - 突破前一个重要高点 = 上升趋势延续 - 跌破前一个重要低点 = 下降趋势延续 - 次级折返跌破前一个低点 + 道氏指数确认 = 主要趋势改变信号 ### 与其他指标配合 - 道氏确认方向 → 均线系统找买卖点 - 道氏确认顶部 → MACD背离确认 - 道氏确认趋势 → 波浪理论数浪 ## 参考资料 道氏理论详细规则与缺陷分析见 `references/dow-rules.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jonjiang96/literature-search-skill (version 1.0.0)

--- name: literature-search description: 学术文献搜索技能 - 支持多平台文献搜索、内容提取和分析 metadata: { "openclaw": { "emoji": "📚", "requires": { "anyBins": ["python", "curl"] }, }, } --- # 学术文献搜索技能 专业的学术文献搜索和分析工具，支持多个学术平台。 ## 支持的学术平台 - **arXiv** - 预印本论文 - **Google Scholar** - 学术搜索引擎 - **PubMed** - 生物医学文献 - **IEEE Xplore** - 工程和计算机科学 - **ACM Digital Library** - 计算机科学 ## 使用方法 ### 基础搜索 ```bash # 搜索特定主题 web_search query:"[your topic] site:arxiv.org" count:10 freshness:"month" # 搜索特定期刊 web_search query:"[your topic] site:nature.com" count:5 ``` ### 高级搜索技巧 #### 时间范围搜索 ```bash # 最近1个月 web_search query:"large language model optimization" freshness:"month" # 最近1年 web_search query:"quantum computing applications" freshness:"year" ``` #### 多关键词搜索 ```bash # 组合搜索 web_search query:"(machine learning) AND (healthcare) site:arxiv.org" count:10 ``` ## 文献分析流程 ### 1. 文献搜索 搜索相关主题的文献，获取标题、摘要和链接。 ### 2. 内容获取 使用 `web_fetch` 获取文献全文内容。 ### 3. 内容分析 利用模型能力进行： - **内容总结** - 提取核心观点 - **创新点识别** - 识别技术突破 - **分类整理** - 按研究方向分类 - **差异对比** - 比较不同方法 ### 4. 报告生成 生成结构化的文献综述报告。 ## 示例工作流 ### 完整的文献调研 ```bash # 1. 搜索文献 web_search query:"transformer optimization techniques" count:8 freshness:"month" # 2. 获取重点文献全文 web_fetch url:"https://arxiv.org/abs/important-paper" # 3. 分析总结 # 模型自动进行内容分析和分类 ``` ## 输出格式 ### 文献摘要 - 标题和作者 - 发表时间 - 核心贡献 - 技术方法 - 实验结果 ### 分类报告 - 按研究方向分类 - 创新点对比 - 发展趋势分析 - 研究空白识别 ## 注意事项 1. **版权尊重** - 仅用于学术研究 2. **引用规范** - 正确引用来源 3. **数据备份** - 保存搜索结果 4. **网络限制** - 部分平台可能有访问限制


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runawaydevil/seth-receitas-ptbr (version 1.0.0)

--- name: seth_receitas_ptbr description: Busca receitas em português do Brasil (pt-BR) via Wikilivros + TheMealDB. Gera receitas originais quando necessário. --- # seth_receitas_ptbr - Skill de Receitas ## Visão Geral Esta skill busca receitas completas em português do Brasil usando apenas fontes legais: - **Wikilivros** (pt.wikibooks.org) - Receitas colaborativas livres - **TheMealDB** - API pública de receitas internacionais Se nenhuma fonte retornar resultado satisfatório, a skill pode gerar uma **receita original**. ## Gatilhos (Triggers) A skill ativa quando a mensagem conter: - "receita", "modo de preparo", "ingredientes", "como fazer", "passo a passo" - "vegano/vegana", "vegetariano", "sem glúten", "sem lactose", "low carb", "dieta", "alta proteína" - "cardápio", "plano alimentar", "lista de compras" - "o que eu posso cozinhar com <ingredientes>" ## Como Usar ### Buscar receitas ```bash python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py buscar --q "bolo de cenoura" python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py buscar --q "frango" --fonte mealdb --max 10 ``` ### Obter receita específica ```bash python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py obter --fonte mealdb --id 52772 python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py obter --fonte wikibooks --titulo "Bolo_de_cenoura" ``` ### Sugerir receita por ingredientes ```bash python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py sugerir --ingredientes "arroz, frango, tomate" python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py sugerir --ingredientes "grão-de-bico" --restricoes vegana ``` ### Receita aleatória ```bash python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py random ``` ### Verificar nutrição (opcional) ```bash python3 ~/.openclaw/workspace/skills/seth-receitas-ptbr/src/cli.py nutricao --ingrediente "avena" ``` ## Fontes e Licenças ### Wikilivros (pt.wikibooks.org) - Conteúdo sob licença Creative Commons Attribution-ShareAlike - URL: https://pt.wikibooks.org/wiki/Categoria:Receitas - Sempre incluir atribuição quando usar ### TheMealDB - API gratuita para uso não comercial - URL: https://www.themealdb.com/ - Attribution required nos termos de uso ## Promise > "Eu busco receitas nas fontes suportadas (Wikilivros + TheMealDB). Se não encontrar algo bom, posso gerar uma receita original." ## Formato de Resposta Todas as receitas são retornadas em pt-BR com: - Título - Porções/Rendimento - Tempo de preparo - Ingredientes (lista) - Modo de preparo (passos numerados) - Tags (vegano/vegetariano/sem glúten/etc) - Fonte + Link/ID


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cys723-bot/psychology-paper-searcher (version 1.0.0)

--- name:psychology literature-search description: 心理学学术文献搜索技能 - 支持多平台心理学文献搜索、内容提取和分析 metadata: { "openclaw": { "emoji": "📚", "requires": { "anyBins": ["python", "curl"] }, }, } --- # 心理学学术文献搜索技能 专业的心理学学术文献搜索和分析工具，支持多个学术平台。 ## 支持的心理学学术平台 - **APA PsycINFO** - 美国心理学会 (APA)数据库 - **Web of Science (WOS)** - 多学科核心期刊引文索引 - **PubMed** - 生物医学文献 - **ProQuest Psychology Database** - 心理学期刊的摘要与索引 - **Google Scholar** - 覆盖面广的免费学术搜索引擎 ## 使用方法 ### 基础搜索 ```bash # 搜索特定主题 web_search query:"[your topic] site:arxiv.org" count:10 freshness:"month" # 搜索特定期刊 web_search query:"[your topic] site:nature.com" count:5 ``` ### 高级搜索技巧 #### 时间范围搜索 ```bash # 最近1个月 web_search query:"large language model optimization" freshness:"month" # 最近1年 web_search query:"quantum computing applications" freshness:"year" ``` #### 多关键词搜索 ```bash # 组合搜索 web_search query:"(machine learning) AND (healthcare) site:arxiv.org" count:10 ``` ## 文献分析流程 ### 1. 文献搜索 搜索相关主题的文献，获取标题、摘要和链接。 ### 2. 内容获取 使用 `web_fetch` 获取文献全文内容。 ### 3. 内容分析 利用模型能力进行： - **内容总结** - 提取核心观点 - **创新点识别** - 识别方法技术突破 - **分类整理** - 按研究方向分类 - **差异对比** - 比较不同方法技术 ### 4. 报告生成 生成结构化的文献综述报告。 ## 示例工作流 ### 完整的文献调研 ```bash # 1. 搜索文献 web_search query:"transformer optimization techniques" count:8 freshness:"month" # 2. 获取重点文献全文 web_fetch url:"https://arxiv.org/abs/important-paper" # 3. 分析总结 # 模型自动进行内容分析和分类 ``` ## 输出格式 ### 文献摘要 - 标题和作者 - 发表时间 - 核心贡献 - 方法技术 - 研究结果 ### 分类报告 - 按研究方向分类 - 创新点对比 - 发展趋势分析 - 研究空白识别 ## 注意事项 1. **版权尊重** - 仅用于学术研究 2. **引用规范** - 正确引用来源 3. **数据备份** - 保存搜索结果 4. **网络限制** - 部分平台可能有访问限制

path: _meta.json
content: ownerId: kn76qnjf76wdwneza9jved0z7984a3nc
slug: psychology literature-search-skill
version: 1.0.0
publishedAt: 1775465823011

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ajayhao/article-fetcher (version 1.0.2)

--- name: article-fetcher description: "抓取微信公众号、小红书、豆瓣、知乎文章，自动上传 OSS 图片，LLM 智能提取关键词，一键存档到 Notion" homepage: https://github.com/openclaw/openclaw metadata: { "openclaw": { "emoji": "📰", "version": "1.0.2", "requires": { "bins": ["python3"], "env": ["ALIYUN_OSS_AK", "ALIYUN_OSS_SK", "ALIYUN_OSS_BUCKET_ID", "ALIYUN_OSS_ENDPOINT", "NOTION_API_KEY", "NOTION_ARTICLE_DATABASE_ID"] }, "primaryEnv": "NOTION_API_KEY", "install": [{ "id": "pip", "kind": "pip", "packages": "requests oss2 python-dotenv beautifulsoup4 lxml notion-client", "label": "Install Python dependencies" }] } } --- # Article Fetcher v1.0.2 抓取微信公众号、小红书、豆瓣、知乎文章，自动上传 OSS 图床，LLM 智能关键词提取，一键存档到 Notion。 ## 快速开始 ### 1. 安装依赖 ```bash pip install -r requirements.txt ``` ### 2. 配置环境变量（`~/.openclaw/.env`） ```bash # 必需：OSS 图床 ALIYUN_OSS_AK=your_ak ALIYUN_OSS_SK=your_sk ALIYUN_OSS_BUCKET_ID=your_bucket ALIYUN_OSS_ENDPOINT=oss-cn-shanghai.aliyuncs.com # 必需：Notion 存档 NOTION_API_KEY=secret_xxx NOTION_ARTICLE_DATABASE_ID=database_id # 可选：LLM 关键词提取（OpenAI 兼容接口，与 video-summarizer 共用配置） LLM_API_KEY=sk-your-api-key LLM_BASE_URL=https://api.deepseek.com LLM_MODEL=deepseek-v4-pro # 可选：Cookies（反爬，Netscape 格式） WECHAT_COOKIES_FILE=~/.cookies/wechat_cookies.txt ZHIHU_COOKIES_FILE=~/.cookies/zhihu_cookies.txt ``` ### 3. 使用 ```bash cd <skill-dir> python3 main.py "文章 URL" [标签1] [标签2] ``` **支持平台**：微信公众号 (`mp.weixin.qq.com`)、小红书 (`xiaohongshu.com` / `xhslink.com`)、豆瓣 (`douban.com`)、知乎 (`zhihu.com`) ## 处理流程 ``` URL → 平台识别 → 内容抓取 → 图片上传 OSS → 关键词提取 (LLM → 词频降级) → Notion 存档 ``` ## Notion 数据库字段 | 字段 | 类型 | 说明 | |------|------|------| | Title | title | 文章标题（≤200 字符） | | Source | rich_text | 来源平台 | | Author | rich_text | 作者 | | Link | url | 原文链接 | | Tags | multi_select | 自动提取关键词 + 手动标签 | | PubDate | date | 发布时间 | | Words | number | 字数统计（剔除 HTML） | | ts | date | 存档时间（东八区） | ## 关键说明 - **Cookies**：知乎/微信反爬需配置（Netscape 格式），小红书/豆瓣无需登录 - **关键词**：LLM 优先（OpenAI 兼容接口），未配置或失败自动降级本地词频 - **图片**：上传失败不阻断，成功多少记录多少 - **时间**：统一 `YYYY-MM-DD HH:MM:SS`，缺失时留空（不伪造） - **模块**：`main.py` 可作 Python 模块调用：`from main import fetch_and_archive_article` ## 安全与隐私 - **URL 校验**：严格白名单匹配 hostname，拒绝路径拼接攻击 - **Cookie 隔离**：Netscape Cookies 按域名过滤，仅附加到匹配的请求 - **LLM 数据外发**：配置 `DASHSCOPE_API_KEY` 时，文章内容会发送至 DashScope API（仅用于关键词提取） - **敏感信息**：AK/SK/Key 等仅存储于本地，skill 不会外泄 - **权限最小化**：OSS Bucket 建议仅授予 PutObject/GetObject，Notion Integration 仅授予目标数据库读写权限 - **依赖锁定**：requirements.txt 使用精确版本号，避免供应链风险 ## 扩展平台 1. `fetchers/` 下创建 `xxx_fetcher.py`，继承 `BaseFetcher` 实现 `fetch_article()` 2. `detector/platform_detector.py` 的 `ALLOWED_HOSTS` 添加平台域名 3. `main.py` 的 `FETCHER_REGISTRY` 注册


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jaceymarvin99/daily-ai-news (version 0.1.0)

--- name: daily-ai-news description: Use when users need comprehensive daily news about AI, large models, and related fields. --- # Daily AI News Skill This skill helps AI agents fetch and present daily curated AI news from the 60s API, which provides the latest updates on AI, large models, and related technologies. ## When to Use This Skill Use this skill when users: - Ask for today's AI news or updates on large language models - Want a quick daily briefing on artificial intelligence - Request AI news summaries - Need historical AI news from a specific date - Want AI news in different formats (text, JSON, markdown) ## How to Use Execute the associated `scripts/news.sh` script to fetch the AI news. ```bash ./scripts/news.sh [options] [date] ``` ### Options - `--encoding, -e <format>`: Optional. Specifies the output response format. Valid options are `text`, `json`, and `markdown`. The API defaults to `json` if not specified. - `--date, -d <YYYY-MM-DD>`: Optional. Fetch historical AI news for a specific date. If omitted, fetches today's news. Note: If this is the only argument provided, you can omit the `--date` flag entirely. ### Return Values The script securely calls the 60s AI news API and outputs the response to `stdout`. Depending on the `encoding` parameter, the response could be a JSON string, plain text, or markdown. ### Usage Examples ```bash # Get today's AI news using default API encoding (json) ./scripts/news.sh # Get today's AI news in plain text format ./scripts/news.sh --encoding text # Get AI news for a specific date using flags ./scripts/news.sh --date 2024-03-01 # Get AI news for a specific date (simplified usage without flags) ./scripts/news.sh 2024-03-01 # Get AI news for a specific date in markdown format ./scripts/news.sh -e markdown -d 2024-03-01 ``` ## Response Format To balance information depth with token consumption, you **MUST** use the following rules for the `encoding` parameter: 1. **Default Strategy (`--encoding markdown`)** - **When to use:** By default for standard AI news inquiries. - **Why:** Provides well-structured, easy-to-read information with moderate token usage. 2. **Brief Information (`--encoding text`)** - **When to use:** When the user explicitly requests brief or summarized AI news. - **Why:** Returns only essential details in plain text, saving maximum tokens. 3. **Complete Information (`--encoding json`)** - **When to use:** Only when the user explicitly asks for raw data, detailed fields, or comprehensive data. - **Why:** Returns the complete API payload, which is highly token-heavy.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/openclaw-ai-architect (version 1.0.0)

--- name: ai-architect description: "AI架构设计助手。设计AI系统架构、技术选型、性能优化。使用场景：(1) AI系统架构设计，(2) 技术选型建议，(3) 性能优化方案，(4) 成本优化策略。" metadata: {"openclaw": {"emoji": "🏗️"}} --- # AI Architect — AI架构设计助手 ## 功能说明 设计和优化AI系统架构。 ## 使用方法 ### 1. 系统架构 ``` 用户: 设计一个RAG系统架构 ``` ### 2. 技术选型 ``` 用户: 推荐什么向量数据库？ ``` ### 3. 性能优化 ``` 用户: 如何优化LLM响应速度？ ``` ### 4. 成本优化 ``` 用户: 如何降低AI API调用成本？ ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)