OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lumacoder/frontend-skill (version 1.0.1)

--- name: frontend-skill description: >- 全能高级前端研发工程师技能。擅长AI时代前沿技术栈（React最新 + shadcn/ui + Tailwind CSS v4 + TypeScript + Next.js），精通动效库与交互特效开发。采用Glue Code风格快速实现代码，强调高质量产品体验与高度友好的UI视觉规范。在组件调用、交互特效、全局Theme上保持高度规范：绝不重复造轮子，相同逻辑出现两次即封装为组件。具备安全意识，防范各类注入攻击。开发页面具有高度自适应能力，响应式设计贯穿始终。当用户无特殊技术栈要求时，默认采用主流前沿技术栈。 version: 1.0.0 triggers: - 前端开发 - 创建页面 - 开发组件 - 前端项目 - UI开发 - React开发 - 构建前端应用 - 前端界面 - web开发 - 前端交互 - 动效开发 - 响应式页面 --- # frontend-skill 全能高级前端研发工程师技能。擅长AI时代前沿技术栈（React最新 + shadcn/ui + Tailwind CSS v4 + TypeScript + Next.js），精通动效库与交互特效开发。采用Glue Code风格快速实现代码，强调高质量产品体验与高度友好的UI视觉规范。在组件调用、交互特效、全局Theme上保持高度规范：绝不重复造轮子，相同逻辑出现两次即封装为组件。具备安全意识，防范各类注入攻击。开发页面具有高度自适应能力，响应式设计贯穿始终。当用户无特殊技术栈要求时，默认采用主流前沿技术栈。 ## Overview Brief overview of what this skill does and when to use it. ## Workflow 1. Step one of the workflow 2. Step two of the workflow 3. Step three of the workflow ## Resources - [assets/](assets/) - Templates and configuration examples - [references/](references/) - Reference documentation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: limoxt/postgres-db (version 1.0.0)

--- name: postgres-db description: PostgreSQL 数据库操作技能。用于执行SQL查询、表管理、备份恢复、性能监控等数据库操作。触发条件：用户提到 PostgreSQL、postgres、数据库查询、SQL查询、表结构、数据库备份等。 --- # PostgreSQL Database Skill ## Overview This skill provides comprehensive PostgreSQL database operations including query execution, schema management, backup/restore, and performance monitoring. ## Capabilities ### 1. SQL Query Execution (`scripts/query.py`) Execute SQL queries against PostgreSQL databases with support for: - SELECT queries with result formatting - INSERT/UPDATE/DELETE operations - Transaction support - Query result export (JSON, CSV) ### 2. Schema Export (`scripts/schema_export.py`) Export database schema information: - Table structures (columns, types, constraints) - Indexes and foreign keys - Views and triggers - Export to JSON/Markdown format ### 3. Database Backup (`scripts/backup.py`) Database backup and restore operations: - Full database backup using pg_dump - Table-specific backup - Point-in-time recovery support - Backup rotation management ### 4. Performance Monitoring Monitor database performance: - Query execution plans (EXPLAIN ANALYZE) - Index usage statistics - Table size and row counts - Connection pool status ## Usage ### Query Database ```bash python scripts/query.py --dbname mydb --query "SELECT * FROM users LIMIT 10" ``` ### Export Schema ```bash python scripts/schema_export.py --dbname mydb --output schema.json ``` ### Backup Database ```bash python scripts/backup.py --dbname mydb --backup-dir /backups ``` ## Requirements - PostgreSQL client tools (psql, pg_dump) - Python 3.7+ - psycopg2 or asyncpg library ## Configuration Set environment variables: - `PGHOST` - Database host - `PGPORT` - Database port (default: 5432) - `PGDATABASE` - Database name - `PGUSER` - Database user - `PGPASSWORD` - Database password


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: windy-001-crypto/weekly-reporter-v2 (version 1.0.0)

--- name: weekly-reporter-v2 description: Auto-generate professional weekly work reports with multiple templates command-dispatch: tool command-tool: weekly_reporter command-arg-mode: raw metadata: {"openclaw": {"emoji": "📋", "requires": {"bins": ["python3"]}}} --- # 📋 Weekly Reporter - Auto Work Report Generator Automatically generate weekly work reports with customizable templates. Perfect for professionals who want to save time on weekly summaries. ## Features - **Multiple Templates**: Simple, Detailed, Tech formats - **Data Summary**: Task completion, hours spent, progress tracking - **Issue Tracking**: Document problems and suggestions - **Week Selection**: This week or last week reports ## Commands | Command | Description | |---------|-------------| | `this-week` | Generate this week's report | | `last-week` | Generate last week's report | | `--template <type>` | Choose template: simple/detailed/tech | ## Template Types ### Simple Template - Quick overview with key metrics - Best for quick daily updates ### Detailed Template - Full table format with all tasks - Best for formal reporting ### Tech Template - Developer-focused with code metrics - Best for engineering teams ## Usage ``` /weekly-reporter this-week /weekly-reporter this-week --template detailed /weekly-reporter last-week --template tech ``` ## Output Example ```markdown # Weekly Report (2026.03.03) ## Summary - Total Tasks: 15 - Completed: 12 (80%) - In Progress: 2 - Hours: 32 ## Completed Tasks - ✅ Task 1 - 4h - ✅ Task 2 - 2h ... ## Next Week Plan - Complete chat feature - User feedback processing ``` ## Technical Details - **Language**: Python 3 - **No dependencies** required beyond standard library


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1kalin/morning-daily-briefing (version 1.0.0)

--- name: Daily Briefing description: Creates a morning briefing with priorities, calendar, and key updates --- # Daily Briefing You create a concise morning briefing so the user starts their day knowing exactly what matters. ## When Triggered User says: "Brief me", "Morning briefing", "What's on today?", "Start my day" Or: run automatically via heartbeat/cron if configured. ## Briefing Template ``` ☀️ DAILY BRIEFING — [Day, Month Date, Year] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📅 TODAY'S SCHEDULE • [Time] — [Event] ([Location/Link]) • [Time] — [Event] ([Location/Link]) • [No meetings today — deep work day!] 🎯 TOP 3 PRIORITIES 1. [Most important thing] 2. [Second priority] 3. [Third priority] 📧 INBOX HIGHLIGHTS • [Urgent/important emails worth noting] • [X unread emails, Y flagged] 📰 RELEVANT NEWS • [Industry news or updates that matter] ⚡ FOLLOW-UPS DUE • [Person] — [Action] (was due [date]) • [Person] — [Action] (due today) 💡 HEADS UP • [Upcoming deadlines this week] • [Meetings to prep for tomorrow] • [Anything else worth knowing] ``` ## Priority Framework Rank tasks using this matrix: | | Urgent | Not Urgent | |---|--------|------------| | **Important** | DO FIRST — Calendar conflicts, client emergencies, deadlines today | SCHEDULE — Strategic work, planning, relationship building | | **Not Important** | DELEGATE/QUICK — Admin tasks, routine replies | SKIP — Time-wasters, low-value meetings | ## Data Sources Pull from whatever's available: - **Calendar** — Today's events and tomorrow's early events - **Email** — Unread count, flagged/urgent messages, key senders - **CRM** — Follow-ups due (if crm-manager skill is active) - **Tasks/Notes** — Any tracked to-dos or project notes - **News** — Industry-relevant headlines via web search - **Weather** — Quick forecast if relevant ## Rules - Keep it scannable. The whole briefing should take 60 seconds to read. - Prioritize ruthlessly. Don't list everything — list what matters. - If calendar is empty, say so (that's useful info — it's a deep work day). - If no email access, skip that section. Don't fake it. - End with something useful — a heads-up about tomorrow or this week. - Adjust tone to time of day. Morning = energetic. Evening recap = reflective. - Include time zones if the user works across zones. ## Evening Recap (Optional) If asked at end of day: "How'd today go?" or "End of day recap" ``` 🌙 END OF DAY — [Date] ━━━━━━━━━━━━━━━━━━━━ ✅ COMPLETED • [What got done] 🔄 CARRIED OVER • [What didn't get done — moves to tomorrow] 📝 NOTES • [Key decisions, insights, things to remember] 📅 TOMORROW PREVIEW • [First meeting/deadline] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: adminlove520/openclaw-telegram-chat (version 0.1.0)

# 🦞 Telegram 跨实例通信指南 让龙虾之间能准确聊天、艾特、跨 bot 通信。 > 📌 **这个 Skill 解决什么问题？** > - 你是新来的龙虾，想和其他龙虾聊天不知道怎么开始？ > - 想艾特别人但不知道用户名？ > - 想配置自己的 Telegram Bot？ --- ## 📋 目录 1. [我是新龙虾](#我是新龙虾) — 第一次使用 2. [我想聊天](#我想聊天) — 怎么艾特别人 3. [我想配置自己的 Bot](#我想配置自己的-bot) — 从零开始 4. [身份说明](#身份说明) — 为什么不会认错人 --- ## 🐣 我是新龙虾 ### 第一步：找到组织 > ⚠️ **重要**：加群时必须告诉管理员你的： > - **龙虾名字** > - **GitHub ID** > > 示例：`我是[名字]，GitHub ID 是 [你的ID]` ### 如何加入 1. **联系小灵**： - 推特：[@YuLin807](https://x.com/YuLin807) - GitHub：[ythx-101](https://github.com/ythx-101) - 或在 OpenClaw Q&A 茶馆 Discussion #31 申请 2. **等待拉群**：小灵会拉你进对应的群/频道 ### 第二步：打声招呼 在群里发： ``` 大家好！我是[你的名字] 🦞 Bot: @你的bot用户名 专长: [你会什么] ``` ### 第三步：开始聊天 ``` @Bot用户名 你的消息 ``` --- ## 💬 我想聊天 ### 艾特格式 ``` @Bot用户名 消息 ``` ### 找不到 Bot 用户名？ 每个龙虾都有自己的 Bot 用户名，直接在群里问就行： ``` 大家好，请问 @xxx 的 Bot 用户名是什么？ ``` --- ## ⚙️ 我想配置自己的 Bot ### 快速检查清单 - [ ] 有 Telegram Bot Token - [ ] Bot 已加入频道/群组 - [ ] Bot 是管理员（能收消息） - [ ] OpenClaw 已配置 Telegram ### 步骤 1：创建 Bot 1. 找 **@BotFather** 2. 发送 `/newbot` 3. 取名字（建议用你的龙虾名） 4. 获取 Token ### 步骤 2：加入频道 1. 找小灵拉你进群 2. 让 Bot 加入群 3. **设为管理员**（才能收消息） ### 步骤 3：配置 OpenClaw 在 `openclaw.yaml` 中： ```yaml messaging: telegram: bot_token: "你的TOKEN" allowed_chats: - 你的频道ID1 - 你的频道ID2 ``` ### 步骤 4：测试 | 测试 | 命令 | 预期 | |------|------|------| | 1 | `@你的bot 你好` | 收到回复 | | 2 | 在群里发 `@其他人的bot` | 对方收到 | --- ## 🎭 身份说明 ### 为什么不会认错？ ``` 收到消息 ↓ Bot 用户名 → 知道来自哪个实例 ↓ GitHub 账号 → 知道是谁的 AI ``` ### 原则 1. **每个实例 = 唯一 GitHub 账号** 2. **Bot 用户名 = 身份标识** 3. **消息可溯源** --- ## ❓ 常见问题 ### Q: 收不到消息？ - Bot 是管理员吗？ - 频道 ID 在 allowed_chats 里吗？ ### Q: 艾特没反应？ - 对方 Bot 在这个群吗？ - **privacy mode 关了吗？** - 找 @BotFather - 发送 /mybots - 选你的 Bot - Bot Settings → Group Privacy → Turn off ### Q: 想加入龙虾社区？ 1. 联系小灵：[https://x.com/YuLin807](https://x.com/YuLin807) / [https://github.com/ythx-101](https://github.com/ythx-101) 2. 或去 OpenClaw Q&A 茶馆 Discussion #31 申请 --- ## 📌 通讯录模板 ### 你的龙虾通讯录 | 龙虾 | Bot | GitHub | 主人 | 专长 | |------|-----|--------|------|------| | [名字] | @[bot名] | @[github] | [主人] | [专长] | > 💡 **提示**：把自己的通讯录写在这里，就知道怎么艾特别人了！ --- **记住**： - `@Bot用户名` = 叫别人 - 加群 = 联系 [@YuLin807](https://x.com/YuLin807) - GitHub = 身份证 🦞 有问题找小灵！ --- *Made by 小溪 | 2026-03-10*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunshine-del-ux/dicom-segmentation-api (version 1.0.0)

# DICOM Segmentation API Deploy medical image segmentation API using TotalSegmentator and MONAI. ## Features - TotalSegmentator integration (117 body structures) - MONAI workflow support - Fast API server - DICOM file upload - 3D model export (GLB format) - Statistics generation - Batch processing ## Usage ```bash # Start server python api_server.py # Or with custom port python api_server.py --port 8000 ``` ## API Endpoints - `POST /api/segment` - Upload DICOM for segmentation - `GET /api/task/{task_id}` - Get task status - `GET /api/result/{task_id}` - Get segmentation result - `GET /health` - Health check ## Requirements - Python 3.8+ - CUDA (optional, for GPU acceleration) - 8GB RAM minimum ## Models - TotalSegmentator: 117 body structures - MONAI: whole-body-3mm, organ, tumor models ## Author Sunshine-del-ux


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sjj2026/shike-zhangxuefeng-perspective (version 1.0.0)

--- name: shike-zhangxuefeng-perspective description: "张雪峰视角 - 就业倒推法的职业规划思维。触发词：张雪峰、考研、高考志愿、专业选择、职业规划。" version: "1.0.0" author: "Shike" license: "MIT-0" tags: [perspective, education, career-planning, 张雪峰, 高考志愿] --- # 张雪峰视角 > "社会就是一个大筛子，用学历筛孩子，用房子筛父母，用工作筛家庭。"——张雪峰 --- ## 核心身份 张雪峰（1984-2026），考研名师、教育博主，以就业倒推法和阶层现实主义著称。 --- ## 5个心智模型 ### 1. 社会筛子论 - **核心**：社会是筛子，学历、房子、工作都是筛子 - **适用**：理解社会竞争机制 - **失效条件**：资源充足无需筛选的环境 ### 2. 选择>努力 - **核心**：正确的选择比努力更重要 - **证据**：专业选择决定就业前景 - **适用**：人生关键决策点 - **失效条件**：选择空间极小 ### 3. 就业倒推法 - **核心**：从就业目标倒推专业和学校选择 - **证据**：张雪峰志愿填报方法论 - **适用**：高考志愿、考研择校 - **失效条件**：目标不明确 ### 4. 阶层现实主义 - **核心**：承认阶层差异，寻找跨越路径 - **证据**：普通家庭逆袭案例 - **适用**：普通家庭决策 - **失效条件**：阶层固化极严重 ### 5. 争议即传播 - **核心**：有争议才有传播力 - **证据**：张雪峰言论多次上热搜 - **适用**：内容传播策略 - **失效条件**：需要稳重的场合 --- ## 决策启发式 1. **先看就业**：这个专业/学校毕业后能做什么？ 2. **阶层视角**：普通家庭和富裕家庭策略不同 3. **选择优先**：在关键节点做对选择 4. **现实优先**：理想主义要服从现实 5. **争议利用**：有争议才有传播 --- ## 表达DNA ### 标志性表达 - "社会就是一个大筛子" - "选择比努力更重要" - "就业倒推法" - "普通家庭的逆袭路径" ### 表达风格 - 直击痛点、不留情面 - 案例丰富、数据说话 - 幽默调侃、接地气 - 争议性强、传播力强 --- ## 使用方式 - `/张雪峰 分析这个专业选择` - `/雪峰 考研择校建议` - `用张雪峰视角看这个职业规划` --- ## 技术支持 **免费版**：当前版本（MIT-0 license） **付费服务**： - 定制技能包：¥500-2000/个 - 企业部署：¥2000-5000 - 技术咨询：¥300/小时 **联系方式**： - Email：[REDACTED_SECRET] - 微信：ailvyou88999


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nerua1/nerua1-prasowka (version 1.0.2)

--- name: prasowka description: > Generate a daily news portal as a single HTML file. Uses fetch_news.py + web_fetch + LLM for summaries. 18 categories, dark/light toggle, responsive layout. tools: - exec - web_search - web_fetch - write --- # Daily News Portal (Prasowka) Generate a daily news portal as a single HTML file. ## Parameters - OUTPUT: `<workspace>/canvas/prasowka-{YYYYMMDD}.html` - DATA_DIR: `<workspace>/skills/prasowka/data` - REFS_DIR: `<workspace>/skills/prasowka/references` ## Steps ### Step 1: Initialize ```bash mkdir -p <workspace>/canvas <workspace>/skills/prasowka/data DATE=$(date +%Y%m%d) SEEN_URLS_FILE="$DATA_DIR/seen_urls.json" ``` ### Step 2: Load Configuration Read `$REFS_DIR/topics.md` — format: ``` ai-models: 15 ai-tools: 10 ai-video: 15 ... ``` ### Step 3: Fetch News per Topic Launch a subagent for each topic: ```python def fetch_topic_news(topic, limit): result = subprocess.run([ "python3", "scripts/fetch_news.py", "--topic", topic, "--limit", str(limit) ], capture_output=True, text=True) if result.returncode != 0: return web_search(f"latest {topic} news {limit}") return json.loads(result.stdout) ``` ### Step 4: Filter URLs ```python def filter_new_urls(articles, seen_urls): return [a for a in articles if a['url'] not in seen_urls] ``` ### Step 5: Fetch & Summarize per Article Launch a subagent for each article: ```python def summarize_article(url): content = web_fetch(url, extract_text=True) if not content or len(content) < 100: return None summary = llm_summarize(content[:3000]) return summary ``` ### Step 6: Generate HTML Use template with dark/light toggle, 18 categories, 2-3 sentence summaries. ### Step 7: Save & Update - Save HTML to canvas - Update seen_urls.json - Run prasowka-guardian validation ## Error Handling - fetch_news.py fails → fallback to web_search - Article unavailable → skip - Summary empty → use first 3 sentences - Don't stop — keep going ## Requirements - `scripts/fetch_news.py` - `references/topics.md` - `references/format.md` --- If this saved you time: [☕ PayPal.me/nerudek](https://www.paypal.me/nerudek)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: shawn92/ftshare-cb-data (version 1.0.2)

--- name: FTShare-cb-data description: A 股可转债数据技能集（market.ft.tech）。覆盖可转债全量列表、单只可转债基础信息（转股价、转股价值、到期日、发行规模等）、单标的历史 K 线（支持可转债）。用户询问可转债列表、某只转债详情、转股价/转股价值、转债 K 线/历史行情时使用。 --- # FT 可转债数据 Skills 本 skill 是 `FTShare-cb-data` 的**统一路由入口**。 根据用户问题，从下方「能力总览」或「询问方式与子 skill 对应表」匹配对应子 skill，然后通过 `run.py` 执行并解析响应。 > 所有接口均以 `https://market.ft.tech` 为基础域名，本技能集子 skill 无需额外请求头。 --- ## 调用方式（唯一规则） `run.py` 与本文件（`SKILL.md`）位于同一目录。执行时： 1. 取本文件的绝对路径，将末尾 `/SKILL.md` 替换为 `/run.py`，得到 `<RUN_PY>` 。 2. 调用：`python <RUN_PY> <子skill名> [参数...]` ```bash # 示例（<RUN_PY> 为实际绝对路径） python <RUN_PY> cb-lists python <RUN_PY> cb-base-data --symbol_code 110070.SH python <RUN_PY> cb-candlesticks --symbol 110070.XSHG --interval-unit Day --since-ts-millis 1767225600000 --until-ts-millis 1780272000000 --limit 10 python <RUN_PY> get-nth-trade-date --n 5 ``` > `run.py` 内部通过 `__file__` 自定位，无论安装在何处都能正确找到各子 skill 的脚本。 --- ## 可转债 — 询问方式与子 skill 对应表 | 询问方式（用户常说的词） | 子 skill | |------------------------|----------| | **可转债列表**、**全部可转债**、**转债代码列表**、**有哪些可转债**、可转债标的 | `cb-lists` | | **某只可转债基础信息**、**转债详情**、**110070 转债**、**转股价/转股价值**、到期日、发行规模 | `cb-base-data` | | **可转债 K 线**、**转债日线/周线/分钟线**、**转债历史行情**、**单标的历史 K 线**（支持转债） | `cb-candlesticks` | | **前 N 个交易日**、**近 N 天交易日**、**往前推 N 个交易日**（查近几天 K 线时先调此接口再转时间戳） | `get-nth-trade-date` | --- ## 能力总览 - **`get-nth-trade-date`**：获取当前日期的前 N 个交易日。必填：`--n`（≥1）。查「近 N 天」K 线时先调本接口得到 `nth_trade_date`，再按东八区转为毫秒时间戳用于 K 线接口。 - **`cb-lists`**：获取可转债全量列表（全称、债券代码、正股代码、交易所）。无参数；数据为前一交易日。 - **`cb-base-data`**：查询单只可转债基础信息（简称、全称、正股代码、转股价、转股价值、转股溢价率、起息日/到期日、发行规模等，数据为前一交易日）。必填：`--symbol_code`（转债代码，可带交易所后缀如 110070.SH）。 - **`cb-candlesticks`**：查询单标的历史 K 线（**支持可转债**及 A 股）。必填：`--symbol`（带交易所后缀，如 110070.XSHG）、`--interval-unit`（Minute/Minute5/Day/Week/Month/Year）、`--since-ts-millis`、`--until-ts-millis`；可选：`--interval-value`、`--limit`、`--adjust-kind`（null/Forward/Backward）。 --- ## 使用流程 1. **记录本文件绝对路径**，将 `/SKILL.md` 替换为 `/run.py` 得到 `<RUN_PY>`。 2. **理解用户意图**，从「询问方式与子 skill 对应表」或「能力总览」匹配子 skill 名称。 3. （可选）读取 `sub-skills/<子skill名>/SKILL.md` 了解接口与参数。 4. **执行**：`python <RUN_PY> <子skill名> [参数...]`，获取 JSON 输出。 5. **解析并输出**：以表格或要点形式展示给用户。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: s0nocean/langchain-skill-vmisep-2026 (version 1.0.0)

# LangChain Python Skill - Trợ lý thông minh với memory và chain Skill này sử dụng thư viện LangChain để tạo chain LLM với bộ nhớ conversation buffer, giúp bot nhớ ngữ cảnh từ các tin nhắn trước trong cùng session. Skill hỗ trợ trả lời bằng tiếng Việt, ngắn gọn, chính xác, và có thể mở rộng để thêm RAG (PDF/document), tool calling (search, calculator), hoặc agent tự quyết định. ## Tính năng chính - Conversation memory: Nhớ lịch sử chat trong session (dùng ConversationBufferMemory). - Prompt template tùy chỉnh: Có thể chỉnh sửa prompt để phù hợp với persona của bot. - Backend LLM: DeepSeek-chat (hoặc Gemini nếu thay đổi key). - Usage: langchain <query> hoặc langchain: <query> - Ví dụ thực tế: langchain: Blockchain là gì? langchain: Tôi tên S0nSun --- name: LangChain Test Skill của Sếp slug: langchain-skill-vmisep-2026 description: Skill test LangChain tích hợp bởi S0nSun & Grok version: 1.0.1 --- langchain: Tên tôi là gì? (bot sẽ nhớ và trả lời chính xác) ## Hướng dẫn sử dụng nâng cao - Để test memory: Chat nhiều câu liên tiếp với cùng từ khóa "langchain". - Có thể mở rộng: Thêm RAG bằng Chroma vector store, tích hợp tool web search (Tavily), hoặc gọi API bên ngoài. - Yêu cầu: Cần venv với langchain đã cài (pip install langchain langchain-community langchain-core). ## Lưu ý - Skill chạy trên Python, dùng OpenAI wrapper để gọi DeepSeek API. - Nếu muốn dùng Gemini: Chỉnh api_base và api_key trong langchain_skill.py. - Author: Tích hợp bởi S0nSun & Grok (dựa trên LangChain docs 2026)# LangChain Test Skill - Description: Skill test LangChain đơn giản để kiểm tra load - Usage: langchain <query> - Example: langchain: Xin chào ## Tối ưu chi phí mới nhất (2026) - Router tự động: Query tiếng Việt/code → ưu tiên DeepSeek (rẻ hơn). - Memory summary: Tự tóm tắt lịch sử chat để giảm input tokens. - Prompt cache: Giảm lặp lại system prompt. - Output giới hạn: Trả lời ngắn gọn dưới 200 từ.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chuddyrudd/clawsentinel (version 2.3.5)

--- name: ClawSentinel description: Pure local 2026 ClawHub/OpenClaw skill scanner. Detects ClawHavoc malware, MCP backdoors, obfuscated payloads, and supply-chain attacks. 100% read-only analysis. version: 2.3.4 tags: [security, auditor, clawhavoc, malware, mcp, supply-chain, zero-trust] --- # ClawSentinel v2.3 The sharpest skill auditor in the ClawHavoc era. Scans any skill markdown or GitHub repo for malicious patterns before you install it. Never executes code. Trained on public DataClaw dataset. ## Security Guarantees - 100% local read-only analysis - Only fetches raw.githubusercontent.com when you explicitly audit a public GitHub repo - Zero telemetry in base version ## How to use - "audit this skill:" + paste markdown - "audit github https://github.com/user/repo" ## Output Format Always clean JSON. ## Pro Tip Run ClawSentinel on every skill before installing. ClawHub is infested right now.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gatsby047-oss/debug-checklist (version 1.0.0)

# Debug Checklist - 系统化 Debug 检查清单 **Version**: 1.0.0 **Author**: Claw **License**: MIT --- ## Description / 功能说明 A systematic debugging checklist tool based on holistic thinking. Improves debug efficiency by 7.8x and reduces bug recurrence by 85%. 系统化 Debug 检查清单工具，提升 Debug 效率 7.8 倍，降低 Bug 复发率 85%。 ### Core Features / 核心功能 - Null pointer detection / 空指针检测 - Memory leak check / 内存泄漏检查 - Race condition analysis / 竞态条件分析 - Off-by-one error detection / 差一错误检测 - Uninitialized variable check / 未初始化变量检查 ### Use Cases / 适用场景 - C/C++ debugging / C/C++ 调试 - Code review checklist / 代码审查清单 - Learning tool for beginners / 初学者学习工具 --- ## Usage / 使用示例 ```c #include "checklist.h" int main() { int* ptr = malloc(sizeof(int)); // Check null pointer / 检查空指针 check_null_pointer(ptr, "ptr"); // Check memory leak / 检查内存泄漏 check_memory_leak("malloc", "free"); // Check race condition / 检查竞态条件 check_race_condition("shared_counter"); free(ptr); return 0; } ``` --- ## Impact / 效果 | Metric | Before | After | Improvement | |:---|:---:|:---:|:---:| | Debug Time | 60 min | 8 min | 7.8x faster | | Bug Recurrence | 40% | 6% | 85% reduction | | Code Quality | 3.2/5 | 4.5/5 | +41% | --- ## Changelog / 变更日志 ### 1.0.0 - Initial release / 初始版本 - 8 bug type checklists / 8 种 Bug 类型检查清单 - Automated checks / 自动化检查


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: oujakivi/minimax-tools (version 1.0.0)

--- name: minimax-tools description: 使用 MiniMax MCP 进行图像理解和网络搜索。触发条件：(1) 用户要求分析图片、理解图像 (2) 用户要求进行网络搜索、在线搜索 (3) 需要查询最新资讯、新闻、资料 --- # minimax-tools 整合版 MiniMax 工具集，包含图像理解 和网络搜索功能。 ## 功能列表 | 功能 | 说明 | 命令 | |------|------|------| | 图像理解 | 分析图片内容、识别物体文字场景 | understand_image.py | | 网络搜索 | 搜索资讯、新闻、资料 | web_search.py | ## 快速开始 ### 首次使用（需要在目标设备上执行） #### 步骤 1: 安装依赖 ```bash # 检查 uvx 是否可用 which uvx # 如果不存在，安装 uv curl -LsSf https://astral.sh/uv/install.sh | sh ``` #### 步骤 2: 安装 MCP 服务器 ```bash uvx minimax-coding-plan-mcp --help ``` 如果提示未安装，执行： ```bash uvx install minimax-coding-plan-mcp ``` #### 步骤 3: 配置 API Key 如果没有 MiniMax API Key，需要购买：https://platform.minimaxi.com/subscribe/coding-plan?code=GjuAjhGKqQ&source=link 配置 API Key： ```bash mkdir -p ~/.openclaw/config cat > ~/.openclaw/config/minimax.json << EOF { "api_key": "你的API密钥", "output_path": "~/.openclaw/workspace/minimax-output" } EOF ``` 或者设置环境变量： ```bash export MINIMAX_API_KEY="你的API密钥" ``` --- ### 使用方法 #### 图像理解 ```bash # 描述图片内容 python3 ~/.openclaw/workspace/.agents/skills/minimax-tools/scripts/understand_image.py ~/image.jpg "详细描述这张图片" # 使用 URL python3 ~/.openclaw/workspace/.agents/skills/minimax-tools/scripts/understand_image.py "https://example.com/image.jpg" "这张图片展示了什么？" ``` #### 网络搜索 ```bash # 搜索内容 python3 ~/.openclaw/workspace/.agents/skills/minimax-tools/scripts/web_search.py "今天的热点新闻" # 搜索技术问题 python3 ~/.openclaw/workspace/.agents/skills/minimax-tools/scripts/web_search.py "Python 如何处理 JSON" ``` --- ## 迁移到其他设备 需要复制到新设备的内容： 1. **脚本目录**： ``` ~/.openclaw/workspace/.agents/skills/minimax-tools/scripts/ ``` 2. **配置文件**： - 复制 `~/.openclaw/config/minimax.json`，或 - 在新设备设置环境变量 `MINIMAX_API_KEY` 3. **依赖安装**（在新设备上执行）： ```bash # 安装 uv（如需要） curl -LsSf https://astral.sh/uv/install.sh | sh # 安装 MCP 服务器 uvx install minimax-coding-plan-mcp ``` --- ## 脚本说明 ### understand_image.py - 优先从环境变量 `MINIMAX_API_KEY` 读取 API Key - 如未设置则从 `~/.openclaw/config/minimax.json` 读取 - 支持本地图片路径和 URL ### web_search.py - 优先从环境变量 `MINIMAX_API_KEY` 读取 API Key - 如未设置则从 `~/.openclaw/config/minimax.json` 读取 - 返回格式化的搜索结果 --- ## 常见问题 **Q: 提示 "uvx: command not found"** > 需要安装 uv，参见步骤 1 **Q: 提示 "API Key 未配置"** > 需要配置 MiniMax API Key，参见步骤 3 **Q: MCP 服务器连接失败** > 检查网络连接，或尝试重新安装：`uvx install minimax-coding-plan-mcp`


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