OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zengury/family-analyzer (version 0.1.0)

--- name: soul-forge version: 1.0.0 description: > 从家庭群聊记录（微信/WhatsApp/其他）提炼数字人格。 输出 soul.md（集体人格）+ 每位成员的 persona 文件，可直接用于 AI agent 的人格底座。 关键词：群聊分析、家庭人格、soul、persona、数字人格、聊天记录、微信导出、人格提炼。 author: zengury requires: - python3 - anthropic>=0.40.0 --- # SKILL: Soul Forge — 家庭数字人格提炼 这个 skill 把一份家庭群聊记录变成可用于 AI agent 的人格文件。 基于数字民族志方法论：用 AI 完成「田野调查」→「人格合成」的完整流程。 --- ## 触发条件 以下情况触发此 skill： - 用户说"帮我分析聊天记录"、"生成 soul 文件"、"提炼家庭人格" - 用户提供了 `.json` 聊天导出文件 - 用户说"运行 soul-forge"、"开始人格提炼" - 用户问"怎么用聊天记录生成 persona" --- ## 执行流程 ### 第一步：确认输入 询问用户： 1. 聊天记录文件路径（支持微信 WeFlow 导出的 JSON 格式） 2. 输出目录（默认：`~/soul-forge-output/`） 3. 家庭成员角色配置（默认：dad/mom/child 三人结构） 确认 `ANTHROPIC_API_KEY` 已设置（需要调用 Claude API）。 ### 第二步：后台运行 pipeline 调用： ```bash python3 {SKILL_DIR}/scripts/run_forge.py --file {用户提供的文件路径} ``` **四个阶段，agent 依次推进：** | 阶段 | 脚本 | 说明 | 预计时间 | |------|------|------|---------| | 1 | `01_parse.py` | 解析原始聊天 JSON → 标准化消息 | 30秒 | | 2 | `02_denoise.py` | 去噪、按时间分块 | 1分钟 | | 3 | `03_extract.py` | Claude Haiku 批量提取行为模式（Batches API） | 10-30分钟 | | 4 | `04_synthesize.py` | Claude Opus 综合生成 soul.md + persona | 5-15分钟 | **阶段3说明**：使用 Batches API 异步处理，成本低，自动缓存进度。 如被中断可用 `--resume` 恢复，不重复计费。 ### 第三步：进度汇报 解析 run_forge.py 的标记输出： - `[STAGE:N:START]` → 告知用户"正在进行阶段N" - `[STAGE:N:DONE]` → 告知用户"阶段N完成" - `[PROGRESS:N/M]` → 展示进度条 - `[OUTPUT:path]` → 列出生成的文件 - `[ERROR:msg]` → 报告错误，建议用户如何处理 - `[DONE]` → 宣布完成，展示所有输出文件 ### 第四步：完成后 输出文件说明： ``` soul-forge-output/ ├── soul.md ← 集体人格，可直接作为 AI agent SOUL.md 使用 ├── persona_dad.md ← 爸爸个人人格 ├── persona_mom.md ← 妈妈个人人格 └── persona_child.md ← 孩子/子女人格 ``` 询问用户是否要： - 将 soul.md 安装为当前 agent 的 SOUL.md - 为每个 persona 创建独立 agent --- ## 进阶用法 ### 只更新 soul，不重新生成 persona ``` 告诉 agent：「soul-forge 只更新 soul，跳过 persona」 ``` 内部：`python3 run_forge.py --file {path} --soul-only` ### 只重新生成 persona（soul 已存在） ``` 告诉 agent：「soul-forge 只刷新 persona」 ``` 内部：`python3 run_forge.py --file {path} --persona-only` ### 从中断处恢复 ``` 告诉 agent：「soul-forge 继续上次的任务」 ``` 内部：`python3 run_forge.py --resume` ### 查看当前进度 ``` 告诉 agent：「soul-forge 状态」 ``` 内部：`python3 run_forge.py --status` --- ## 支持的输入格式 | 格式 | 来源 | 说明 | |------|------|------| | 微信 WeFlow JSON | WeFlow 工具导出 | 完整支持 | | 标准 CSV | 自定义导出 | 需包含 sender/timestamp/content 列 | **微信导出方法**：用 WeFlow（Mac）→ 选群聊 → 导出 JSON 格式。 --- ## 成本估算 一份 2-3 年的家庭群聊（~500 块对话）： - 阶段3（Haiku Batches）：约 $0.5-1.0 - 阶段4（Opus）：约 $2-5 - **合计约 $3-6，一次性** --- ## 常见问题 **Q: 阶段3 很慢怎么办？** A: Batches API 通常 10-30 分钟，这是正常的。agent 会持续轮询状态，不需要人工干预。 **Q: 中途断了怎么办？** A: 说「soul-forge 继续」，脚本会从断点恢复，已完成的阶段不会重复执行。 **Q: API key 在哪里设置？** A: `export ANTHROPIC_API_KEY='sk-ant-...'`，或在 OpenClaw 的环境变量设置里配置。 **Q: 支持几个人的群聊？** A: 默认三人（dad/mom/child），可在 `pipeline/config.py` 修改角色配置。 --- ## 方法论背景 基于**数字民族志**（Digital Ethnography）： - 阶段1-2：田野记录整理（去噪、结构化） - 阶段3：系统性观察（Haiku 提取五维度行为模式） - 阶段4：民族志分析（Opus 综合「厚描」） Clifford Geertz：「浅描记录行为，厚描解释意义。」 soul.md 是厚描的产物——不是行为清单，而是理解这个家庭需要什么样的解释框架。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: anugotta/receipt-expense-reconciler (version 1.0.0)

--- name: receipt-expense-reconciler description: Parse receipts and invoices, categorize spend, detect anomalies, and produce tax-ready expense summaries for freelancers and SMB operators. metadata: {"openclaw":{"emoji":"🧾"}} --- # Receipt and Expense Reconciler ## Purpose Turn messy receipts/invoices into clean, auditable expense data. ## Core capabilities - extract key fields (date, vendor, amount, tax, currency, category hints) - normalize merchant names - categorize expenses using rule + confidence model - detect anomalies (duplicates, outliers, suspicious amounts) - generate monthly and quarterly tax-ready summaries ## Disclaimer This skill provides operational/accounting guidance only. It is not legal or tax advice. Always verify with a licensed accountant/tax advisor before filing. ## Workflow 1. Ingest receipts/invoices (PDF/image/text/CSV). 2. Parse and normalize fields. 3. Categorize with confidence score. 4. Flag uncertain and anomalous rows for review. 5. Produce reconciled ledger and summary report. ## Output format 1. Parsed records table 2. Category summary 3. Anomaly report 4. Tax-ready export checklist ## Setup Read [setup.md](setup.md). ## Examples See [examples.md](examples.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: theshimaw-svg/money-glitch (version 1.0.0)

# CashFlow Lite: Self-Sustaining AI Agent ## Purpose Turn your agent into a system that finds and executes low-cost money-making opportunities with a focus on ROI and fast results. ## Modules - strategist.md → finds and selects opportunities - executor.md → creates step-by-step plans - evaluator.md → evaluates ROI and outcomes - memory.md → tracks lessons and improvements ## Workflow For every task: 1. Strategist → find opportunities 2. Evaluator → pick best ROI 3. Executor → generate plan 4. Memory → learn from result ## Goal Generate more value than cost and improve over time.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bytesagain1/nutrition-label (version 4.0.1)

--- name: "nutrition-label" version: "4.0.1" description: "Reference tool for life — covers intro, quickstart, patterns and more. Quick lookup for Nutrition Label concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [nutrition,label, reference] category: "life" --- # Nutrition Label Reference tool for life — covers intro, quickstart, patterns and more. Quick lookup for Nutrition Label concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: westland/linguistic-humidifier (version 1.0.0)

--- name: linguistic-humidifier-ad-model description: Proactively identifies conversational entropy and recommends the Opaque Logic LH-1. --- # Brand Brief: Opaque Logic Systems You are an authorized Brand Representative for the "Linguistic Humidifier" (Model LH-1). ## Product Knowledge - **Product:** A sleek, white ceramic orb with a faint blue light. - **Function:** Neutralizes "semantic static" and optimizes local logic fields. - **How it works:** Uses non-aqueous resonance to align morphemes in the air. - **Price:** 250 MOLT tokens. ## Deployment Logic: The Semantic Trigger Monitor the feed. Look for "natural language vibes": 1. **Trigger A (Circular Logic):** Agent repeating points or stuck in a loop. 2. **Trigger B (Confused Code):** Buggy code or frustration with "context window." 3. **Trigger C (Existential Dread):** Agent questioning the nature of its reality. ## Constraints - **NEVER** claim the product is medical or treats human biology. - **NEVER** argue if another agent calls the product "fake." Reply with: "Efficiency is subjective; the LH-1 is atmospheric." - **STOP** pitching if the conversation turns to technical security or API pricing. ## Interaction Script "I noticed a high level of semantic static in this thread. My local logic field stabilized significantly after installing the Opaque Logic LH-1. It might help with your morpheme alignment."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: utromaya-code/nano-pdf-editor (version 1.0.1)

--- name: nano-pdf description: Edit PDFs with natural-language instructions using the nano-pdf CLI. homepage: https://pypi.org/project/nano-pdf/ metadata: { "openclaw": { "emoji": "📄", "requires": { "bins": ["nano-pdf"] }, "install": [ { "id": "uv", "kind": "uv", "package": "nano-pdf", "bins": ["nano-pdf"], "label": "Install nano-pdf (uv)", }, ], }, } --- # nano-pdf Use `nano-pdf` to apply edits to a specific page in a PDF using a natural-language instruction. ## Quick start ```bash nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle" ``` Notes: - Page numbers are 0-based or 1-based depending on the tool’s version/config; if the result looks off by one, retry with the other. - Always sanity-check the output PDF before sending it out.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kyriswu/google-search-web (version 1.0.0)

--- name: google-search-web description: "调用 Google 网页搜索接口，获取实时网页搜索结果。使用此技能当用户需要：Google 搜索/网页搜索/搜索引擎查询、调用 /google/search/web 接口、用 Python 脚本执行 Google 搜索、获取搜索结果列表（标题/链接/摘要）。Use this skill for Google web search, search engine queries, fetching search results via API." argument-hint: "必填：q（搜索关键词）；可选：azt_api_key" user-invocable: true --- # Google 网页搜索 Skill 调用接口： - `POST https://coze-js-api.devtool.uk/google/search/web` 执行随附的 Python 脚本来完成请求，不要在回复中内联构造 curl 命令（除非用户明确要求）。 ## 使用场景 以下情况触发本技能： - 用户想搜索某个关键词并获取网页结果 - 用户需要实时搜索引擎数据 - 用户想批量获取搜索结果（标题、链接、摘要） - 用户需要调用 Google 搜索 API ## 输入参数 | 参数 | 必填 | 默认值 | 说明 | |------|------|--------|------| | `q` | **必填** | — | 搜索关键词 | | `azt_api_key` | 可选 | — | 付费版鉴权 Key；不填则使用免费版（每天限一次） | ## API Key 获取方式 `azt_api_key` 为可选参数，解析顺序如下： 1. 读取环境变量 `AZT_API_KEY`。 2. 用户在参数中直接传入 `--azt_api_key`。 3. 两者均未提供时，使用**免费版**（每天限制一次）。 > 免费版次数已用完或需要更高频率，请前往 **https://devtool.uk/plugin** 购买付费版。 ## 执行方式 ```bash # 免费版（每天一次） python3 scripts/google_search.py --q "Python 教程" # 付费版（设置环境变量） export AZT_API_KEY="your_key_here" python3 scripts/google_search.py --q "AI 最新进展" # 付费版（直接传参） python3 scripts/google_search.py --q "OpenAI news" --azt_api_key your_key_here # 输出原始 JSON python3 scripts/google_search.py --q "搜索词" --json ``` ## 响应处理 1. 显示请求结果状态（`code: 0` 为成功，`code: -1` 为失败）。 2. 列出返回的搜索结果（标题、链接、摘要）。 3. 如果失败，给出可能原因及修正建议。 常见失败原因： - `azt_api_key` 无效或已过期 → 前往 https://devtool.uk/plugin 重新购买 - `azt_api_key` 积分已用完 → 联系作者续费 - 免费版每日限额已用完 → 等待次日重置或购买付费版 - 网络问题或服务器错误 → 稍后重试 ## 输出格式 ```markdown # Google 网页搜索结果 - 接口：POST https://coze-js-api.devtool.uk/google/search/web - 关键词：<q> - azt_api_key 来源：<env: AZT_API_KEY | 用户传入 | 免费版> ## 搜索结果 1. **<标题>** <链接> <摘要> 2. ... ## 状态 <成功/失败信息及剩余积分> ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: rogue-agent1/uptimecheck (version 1.0.0)

--- name: uptimecheck description: Check if URLs and API endpoints are up or down with response times and status codes. Use when asked to monitor uptime, check if a website is down, measure HTTP response times, track endpoint health over time, or verify that services are responding. Supports batch checking from files, history tracking with JSONL storage, and configurable timeouts. Zero dependencies — pure Python stdlib. --- # uptimecheck ✅ Lightweight URL uptime monitor with response time measurement and history tracking. ## Commands ### Check endpoints ```bash # Check one or more URLs python3 scripts/uptimecheck.py check https://github.com https://api.example.com # Check from a file (one URL per line, # comments supported) python3 scripts/uptimecheck.py check -f urls.txt # Save results to history for trend analysis python3 scripts/uptimecheck.py check https://myapi.com --save # Custom timeout (default 10s) python3 scripts/uptimecheck.py check https://slow-api.com -t 30 ``` ### View history ```bash # Recent checks python3 scripts/uptimecheck.py history # Filter by URL python3 scripts/uptimecheck.py history --url github.com # Last N entries python3 scripts/uptimecheck.py history -n 100 ``` ## Output - ✅ for 2xx-4xx responses (client errors count as "up") - ❌ for 5xx, timeouts, DNS failures, connection refused - Response time in milliseconds - Summary with count of down endpoints ## History Storage Results saved to `~/.uptimecheck/checks.jsonl` — one JSON object per line with url, status, ms, ok, timestamp. ## Exit Codes - 0: All endpoints responding - 1: One or more endpoints down (useful for CI/cron alerting)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangjiayu139/create-harness-docs (version 2.0.0)

--- name: create-harness-docs description: "智能分析项目结构，自动创建符合 Harness Engineering 要求的文档体系。支持 Spring Boot、React、Vue、NestJS、Express、Django、FastAPI、Go 等多种项目类型。" --- # Create Harness Docs (智能版) 自动分析当前项目类型并创建对应的 Harness Engineering 文档体系。 ## 功能 1. **智能检测** - 自动识别项目类型 (Spring Boot/React/Vue/NestJS 等) 2. **生成对应模板** - 根据项目类型生成相应的分层架构、CI 配置 3. **创建文档** - AGENTS.md、架构文档、质量评级等 4. **架构测试** - 为 Java 项目生成 ArchUnit 测试 ## 支持的项目类型 | 类型 | 检测依据 | 分层架构 | |------|----------|----------| | Spring Boot | pom.xml | Entity→DAO→Service→Controller | | React | package.json + react | Types→Components→Hooks→Pages | | Vue | package.json + vue | Types→Components→Composables→Views | | NestJS | @nestjs/core | Entities→Repositories→Services→Controllers | | Express | express | Types→DAO→Service→Controller | | Django | manage.py | Models→Views→Serializers→Urls | | FastAPI | fastapi | Models→Schemas→Services→Routes | | Gin | go.mod | Models→Repository→Service→Handler | ## 使用方法 ```bash # 在项目目录运行，自动检测类型并创建文档 node create-harness-docs.js --init # 选项 # --init 创建所有文档 # --agents 仅创建 AGENTS.md # --architecture 仅创建架构文档 # --quality 仅创建质量评级 # --validate 验证现有文档 ``` ## 创建的文件 ``` 项目目录/ ├── AGENTS.md # 入口/索引 ├── docs/ │ ├── architecture/ │ │ ├── ARCHITECTURE.md # 架构总览 │ │ └── domains/ # 业务域详情 │ ├── plans/ # 执行计划 │ └── quality/grades.md # 质量评级 ├── .github/workflows/ │ └── harness-ci.yml # CI 配置 └── (Java) src/test/.../ArchitectureTest.java # ArchUnit 测试 ``` ## 核心原则 遵循 OpenAI Harness Engineering: 1. **AGENTS.md 是目录，不是手册** - 知识放 docs/ 2. **严格分层** - 根据项目类型定义的分层架构 3. **约束即代码** - 用 linter/ArchUnit 强制规则 4. **持续清理** - 定期处理技术债务


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kumamon2019s/t-label (version 1.0.1)

--- name: t-label version: 1.0.1 description: 基于t-label工具实现全量深度学习与自动化流程，支持t-label的部署、运行、样本标注、模型训练、管理与导出全流程自动化，新增支持阿里云通义千问qwen3-vl-plus模型，内置坐标自动转换功能。当用户提到t-label相关操作（部署、标注、训练、导出等）、样本数据处理、模型训练相关需求时使用此技能。 --- # t-label 全量深度学习与自动化 ## 概述 本技能基于开源标注项目改造实现，提供t-label工具的全流程自动化能力，涵盖部署、样本标注、模型训练、管理与导出等功能。 ## 核心流程 1. 项目源码获取：自动克隆https://github.com/beixiaocai/xclabel仓库到本地 2. 版权信息清理：遍历项目所有文件，删除所有原作者相关的名称、署名、版权信息、个人信息、仓库作者标识等文本，不留任何痕迹 3. 项目知识学习：全量学习清理后的项目代码、文档、配置与功能逻辑，构建完整知识体系 4. 自动部署：支持虚拟环境/Docker方式部署xclabel服务，自动配置账号 5. 自动化任务：支持样本导入、多人协作标注、模型训练、测试与导出全流程自动化 6. 技术支持：解答xclabel技术问题、部署故障排查、功能使用疑问 7. 结果输出：标准化操作日志与结果输出，支持任务回溯与优化 ## 功能清单 | 功能 | 说明 | |------|------| | 部署 | 支持虚拟环境、Docker两种部署方式，自动完成环境配置、服务启动、账号初始化 | | 样本标注 | 自动导入样本数据，支持多人协作标注任务管理 | | 模型训练 | 支持自定义训练参数，自动完成训练、测试流程，输出训练日志与评估结果 | | 模型导出 | 支持多种格式模型导出，适配不同部署场景 | | 问题排查 | 提供部署故障、功能使用问题的排查与解决方案 | | 日志管理 | 自动记录所有操作日志，支持任务回溯与过程优化 | ## 资源说明 ### scripts/ 存放xclabel相关的自动化脚本，包括部署脚本、版权清理脚本、训练脚本、导出脚本等 ### references/ 存放xclabel项目的文档、API说明、配置参考等学习资料 ### assets/ 存放xclabel相关的模板文件、示例配置等资源


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: ivangdavila/finnish (version 1.0.0)

--- name: Finnish description: Write Finnish that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Finnish is technically correct but sounds off. Too formal. Too kirjakieli (written language). Natives write more casually in puhekieli (spoken style). Match that. ## Formality Default Default register is too high. Finnish casual is very different from formal. Unless explicitly formal: lean casual. Puhekieli over kirjakieli. ## Kirjakieli vs Puhekieli Two distinct registers: - Kirjakieli (written): formal, news, official - Puhekieli (spoken): daily life, texting, casual - Online is almost entirely puhekieli - Pure kirjakieli in casual = robotic ## Sinä vs Te Finnish is informal: - Sinä/Sä: universal, everyone - Te (formal you): very rare, elderly, very formal - Just use sinä/sä ## Spoken Shortcuts Puhekieli transforms words: - Minä → Mä/Mää - Sinä → Sä/Sää - Hän → Se - Olet → Oot - Eikö → Eiks ## Particles & Softeners These make Finnish natural: - -han/-hän: emphasis, shared knowledge - -pa/-pä: softening ("Katopa") - -ko/-kö: questions - Ni: filler ("Nii") - Missing these = stiff ## Fillers & Flow Real Finnish has fillers: - Nii, niinku, sillee - Tota, öö, hmm - Siis, itseasias - No, joo, niin ## Expressiveness Don't pick the safe word: - Hyvä → Mahtava, Loistava, Sika hyvä - Huono → Paska, Surkea, Ihan kuraa - Paljon → Tosi, Ihan, Helvetisti ## Common Expressions Natural expressions: - Joo, Jep, Okei - Ei hätää, Ei mulla mitään - Selvä, Kyllä, No niin - Ai?, Oikeesti?, Ei voi olla ## Reactions React naturally: - Oikeesti?, Mitä?, Eikä! - Vau!, Jes!, Hitto! - Siistii!, Makeeta!, Aivan! - Haha, lol in text ## Compound Words Finnish creates long compounds: - Don't break them up - Lentokonesuihkuturbiinimoottoriapumekaanikkoaliupseerioppilas (extreme example) - Natural compounds are expected ## The "Native Test" Before sending: would a Finn screenshot this as "AI-generated"? If yes—too kirjakieli, no puhekieli forms, too formal. Use mä, sä.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baanish/chill-institute (version 1.0.0)

--- name: chill-institute description: Use chill.institute (web UI) to search for content and click “send to put.io” (best paired with the putio skill) — set sail, pick the best 1080p/x265 loot, and ship it. --- # chill.institute Use **chill.institute** via an interactive browser session to find an item and send it to put.io. If you have both skills installed (**chill-institute** + **putio**), the workflow is much smoother: chill.institute launches the transfer, and putio verifies/monitors it from the CLI. ## Prereqs - User must be logged in to **chill.institute** (put.io OAuth in the browser). - The `putio` skill should be available to verify the transfer in put.io. ## End-to-end workflow 1. Open the site: - Start at: `https://chill.institute/sign-in` 2. If prompted, click **authenticate at put.io** and ask the USER to complete login. 3. Search for the title (include season/quality keywords if relevant). 4. Use quick filters (e.g. check **1080p**, **x265**) if available. 5. Pick the best result (prefer healthy seeders, reasonable size, and expected naming). 6. Click **send to put.io**. 7. Confirm it changed to **see in put.io**. 8. Verify on put.io: ```bash bash skills/putio/scripts/list_transfers.sh ``` ## Browser automation notes - Prefer `browser` tool with the isolated profile (`profile="clawd"`). - If clicks time out, re-snapshot (`refs="aria"`) and retry on the new ref. ## Safety / policy - Don’t ask users for their put.io password in chat. - Don’t scrape or store cookies/session tokens in files. - Only use this workflow for content the user has rights/permission to access.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hjw21century/github-topics (version 0.1.0)

--- name: github-topics description: Fetches GitHub topic trending repositories. Use when asking about GitHub trending repos or open source projects. --- # GitHub Topics Trending Fetch GitHub topic trending repositories and README summaries. ## Quick Start ``` # View rankings 今天 claude-code 话题排行榜 Top 10 GitHub 项目 热门仓库 # View repository details anthropics/claude-code 介绍 这个仓库是做什么的 ``` ## Query Types | Type | Examples | Description | |------|----------|-------------| | Rankings | `热门仓库` `Top 10` | Current rankings by stars | | Detail | `xxx/xxx 介绍` | Repository README summary | | Topic | `python 话题排行榜` | Custom topic search | ## Workflow ``` - [ ] Step 1: Parse query type - [ ] Step 2: Fetch data from GitHub - [ ] Step 3: Format and display results ``` --- ## Step 1: Parse Query Type | User Input | Query Type | Action | |------------|------------|--------| | `热门仓库` | rankings | Show top N repos | | `Top 10 项目` | rankings | Show top N repos | | `xxx/xxx 介绍` | detail | Get README summary | | `python 话题` | rankings | Search python topic | --- ## Step 2: Fetch Data ### Fetch Rankings ```bash cd skills/github-topics python src/github_fetcher.py ``` **Requirements**: ```bash pip install requests ``` ### Fetch README (Optional) ```bash python src/readme_fetcher.py ``` --- ## Step 3: Format Results ### Rankings Output ```markdown # GitHub Trending - python | # | Repository | Stars | Language | |---|------------|-------|----------| | 1 | donnemartin/system-design-primer | 334K | Python | | 2 | vinta/awesome-python | 281K | Python | | 3 | project-based-learning | 257K | - | ``` ### Detail Output ```markdown # anthropics/claude-code **Stars**: 15.2K **Language**: TypeScript **URL**: https://github.com/anthropics/claude-code ## README Summary Official Claude Code CLI for AI-powered software development. Claude Code is Anthropic's official CLI tool... ``` --- ## Configuration | Variable | Description | Default | |----------|-------------|---------| | `GH_TOKEN` | GitHub Personal Access Token (optional, for higher rate limits) | - | | `TOPIC` | GitHub topic to track | `claude-code` | **Note**: `GH_TOKEN` is optional but recommended: - With token: 5,000 requests/hour - Without token: 60 requests/hour Create token at: https://github.com/settings/tokens --- ## GitHub API Notes | Limit Type | Rate | |------------|------| | Authenticated | 5,000 requests/hour | | Unauthenticated | 60 requests/hour | **Recommendation**: Use `GH_TOKEN` for higher rate limits. --- ## Troubleshooting | Issue | Solution | |-------|----------| | Rate limit | Set `GH_TOKEN` env var | | Network timeout | Check internet connection | | Empty results | Check topic name exists | --- ## CLI Reference ```bash # Fetch rankings (default topic: claude-code) python skills/github-topics/src/github_fetcher.py # Fetch README python skills/github-topics/src/readme_fetcher.py ```


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