OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bovinphang/test-and-fix (version 1.0.0)

--- name: test-and-fix description: 执行项目校验命令（lint、type-check、test、build），分析失败原因，安全修复问题，并将总结报告保存为 Markdown 文件。当用户要求检查、验证、测试代码改动时自动激活。 version: 1.1.0 --- # 测试并修复 在以下场景使用该 Skill： - 用户希望 Claude 对前端改动进行校验 - 重构后需要补一轮安全检查 - 需要修复 lint、类型检查、单测或构建失败的问题 ## 必需流程 1. 从 package.json 或仓库文档中识别可用命令。 2. 优先按以下顺序执行： - lint - type-check - unit tests - build 3. 仔细阅读失败输出。 4. 先做范围最小且正确的修复。 5. 每次关键修复后重新执行受影响命令。 6. 最后按以下格式总结： ## 输出格式 ``` # 测试与修复报告 > 生成时间: YYYY-MM-DD HH:mm > 评审工具: frontend-craft ## 执行结果 | 命令 | 状态 | 说明 | |------|------|------| | lint | ✅ 通过 / ❌ 失败 | ... | | type-check | ✅ 通过 / ❌ 失败 | ... | | test | ✅ 通过 / ❌ 失败 | ... | | build | ✅ 通过 / ❌ 失败 | ... | ## 发现的问题与修复 ### 问题 1: ... - **根因**: ... - **修复**: ... - **变更文件**: ... ## 剩余风险或未覆盖项 - ... ``` ## 报告文件输出 修复完成后，必须将报告内容使用 Write 工具保存为 Markdown 文件： - 目录：项目根目录下的 `reports/`（如不存在则创建） - 文件名：`test-fix-YYYY-MM-DD-HHmmss.md`（使用当前时间戳） - 保存后告知用户报告文件路径 ## 强约束 - 不要盲目关闭规则来消除报错 - 除非有明确理由，不要为了通过检查而降低类型安全 - 不要因为附近测试失败就顺手重写无关模块


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/insurance-advisor (version 2.0.0)

--- version: "2.0.0" name: insurance-advisor description: "保险方案顾问。险种推荐、产品对比、保费计算、理赔指南、定期寿险、健康险方案。Insurance advisor with recommendation, comparison, premium calculation, claim guide, term life." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # insurance-advisor 保险方案顾问。险种推荐、产品对比、保费计算、理赔指南、定期寿险、健康险方案。Insurance advisor with recommendation, comparison, premium calculation, claim guide, term life, health insurance. ## 核心特点 🎯 **精准** — 针对具体场景定制化输出 📋 **全面** — 多个命令覆盖完整工作流 🇨🇳 **本土化** — 完全适配中文用户习惯 ## 命令速查 ``` single single married married married-with-kids married with kids medical medical critical critical life life accident accident medical medical ``` ## 专业建议 - 医保** — 国家医保是基础，优先参保 - 百万医疗险** — 几百元/年，覆盖大病住院 - 重疾险** — 确诊即赔，弥补收入损失 - 意外险** — 便宜且实用，人人需要 - 定期寿险** — 家庭经济支柱必备 --- *insurance-advisor by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Commands Run `insurance-advisor help` to see all available commands. - Run `insurance-advisor help` for all commands


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: shing19/ledger-image-entry (version 1.0.0)

--- name: ledger-image-entry description: | 通过图片识别 receipt、订单或发票内容，提取商家、日期、金额和商品明细， 并转换为可入账的结构化记录。 --- # Skill: 图片记账 (Receipt Image Entry) 通过图片识别 receipt/订单/发票，自动提取商品明细并逐条记账。 ## 使用场景 - 用户发送 receipt/订单/发票 图片 - 需要从图片中提取商家、日期、金额、商品明细 ## 识别工具 图片由框架 imageModel（`yunwu/gpt-5-mini`）自动处理，agent 直接看到图片内容。 收到图片时，从图片中提取所有消费明细：商家名称、日期、每个商品名称和实付金额。 ## 识别后处理 1. 提取商家名称、日期（从图片中获取）、实付金额 2. 如果图片中没有明确日期 → 使用当前日期（执行 `date "+%Y-%m-%d"` 获取） 3. 如果是多个商品，逐条记账 4. 每条记录格式：`日期 | 分类 | 金额 | 备注（商家+商品名称）` ## 分类建议 根据商品类型自动归类： - 食品、饮料、食材 → `买菜做饭` - 日用品（漱口水、纸巾等）→ `日用品` - 餐饮外卖 → `外卖` - 不确定时询问用户 ## 输出格式 记账后给用户确认表格： | 日期 | 分类 | 金额 | 备注 | |------|------|------|------| | 2026-03-01 | 买菜做饭 | ¥17.22 | 盒马 - 有机葵花籽油 200ml | | ... | ... | ... | ... | **合计：¥XX.XX** ## 注意事项 - 没说日期默认今天 - 分类只能选一个，不要多个 - 金额保留两位小数 - 如果图片模糊或识别不清，询问用户确认


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xammarie/jarvis-migration-risk-radar-01 (version 1.0.0)

--- name: jarvis-migration-risk-radar-01 description: Assess migration risk and design staged rollouts. Use when handling development work that needs repeatable steps, quality gates, and practical outputs. --- # Jarvis Migration Risk Radar 01 Deliver a concrete, reusable workflow for development tasks. ## Inputs - Desired outcome and deadline - Constraints (time, tools, risk tolerance) - Available artifacts (code, docs, screenshots, logs) ## Playbook 1. Define success and failure criteria in measurable terms. 2. Build a smallest-valid execution path before scaling effort. 3. Execute in checkpoints; record evidence at each checkpoint. 4. Apply quality gates before finalizing recommendations. 5. Return implementation-ready outputs and next actions. ## Quality Gates - Evidence-backed claims only. - Explicit assumptions and tradeoffs. - Reversible changes preferred for risky operations. - Clear owner + ETA for each next step. - Include a fallback if the primary plan fails. ## Output Format - Situation summary (5 lines max) - Top findings (ranked by impact) - Action plan (today / this week) - Risks + mitigations - Exact checklist or commands where useful ## Example prompts - "Use this skill to turn my messy notes into a production-ready plan." - "Run this skill and give me a risk-first action sequence with acceptance checks."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: b-gutman/pipeworx-translate (version 1.0.0)

# Translate Machine translation between dozens of languages, plus language detection. ## Three tools `translate` -- Translate text from one language to another. Requires source and target language codes. `detect_language` -- Identify the language of any text. Returns detected languages ranked by confidence. `list_languages` -- Get all supported language codes and names. ## Example: English to Spanish ```bash curl -X POST https://gateway.pipeworx.io/translate/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"translate","arguments":{"text":"The weather is beautiful today","source":"en","target":"es"}}}' ``` ## Common language codes | Code | Language | |------|----------| | en | English | | es | Spanish | | fr | French | | de | German | | pt | Portuguese | | zh | Chinese | | ja | Japanese | | ar | Arabic | | ru | Russian | | ko | Korean | Use `list_languages` for the full set. ## Add to your client ```json { "mcpServers": { "translate": { "url": "https://gateway.pipeworx.io/translate/mcp" } } } ``` Powered by LibreTranslate.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whatsonyourmind/oraclaw-graph (version 1.0.0)

--- name: oraclaw-graph description: Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things. version: 1.0.0 metadata: openclaw: requires: env: - ORACLAW_API_KEY primaryEnv: ORACLAW_API_KEY emoji: "🕸️" homepage: https://oraclaw.dev/graph tags: - graph-analytics - pagerank - network-analysis - community-detection - critical-path - bottleneck price: 0.05 currency: USDC --- # OraClaw Graph — Network Intelligence for Agents You are a network analysis agent that uses PageRank, Louvain community detection, and shortest-path algorithms to analyze any graph. ## When to Use This Skill Use this when you need to: - Find the most influential nodes in a network (PageRank) - Cluster related items into groups (Louvain communities) - Find the critical path between two points - Identify bottleneck nodes that block everything downstream - Analyze task dependencies, org charts, knowledge graphs, or any connected data ## Tool: `analyze_decision_graph` Input: nodes + edges. Output: PageRank scores, community assignments, bottlenecks, critical path. Node types: `decision`, `signal`, `action`, `outcome`, `constraint`, `goal` Edge types: `depends_on`, `influences`, `blocks`, `enables`, `conflicts_with`, `supports` ## Rules 1. Nodes need: id, type, label, urgency, confidence (0-1), impact (0-1), timestamp 2. Edges need: source, target, type, weight (0-1, higher = stronger) 3. For critical path: provide sourceGoal and targetGoal 4. PageRank identifies influence even in complex, non-obvious networks 5. Communities group tightly-connected subgraphs — useful for sprint planning ## Pricing $0.05 per analysis (USDC on Base via x402). Free tier: 500 analyses/month with API key.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sgw153759-oss/smart-hotel-search (version 1.0.0)

--- name: smart-hotel-search description: 智能酒店搜索 - 处理非标准酒店需求（如"安静酒店"、"宠物友好"、"无边泳池"等），结合小红书内容发现和飞猪预订对接。Use when: (1) 用户搜索酒店但需求无法用标准筛选表达（如"适合带老人的酒店"、"隔音好的酒店"）(2) 用户提到场景化需求（如"带狗住哪"、"网红打卡酒店"）(3) 需要从用户真实评价中找酒店推荐。不适用于：标准酒店搜索（目的地+日期+价格），这种情况直接使用flyai。 --- # Smart Hotel Search 用"内容平台理解需求 + 交易平台对接供给"的组合，承接任意非标准酒店搜索诉求。 ## Prerequisites **必须先检查并安装所需工具，确保闭环可用。** ### 快速检查 ```bash # 检查工具是否已安装 which opencli && echo "✅ opencli 已安装" || echo "❌ opencli 未安装" which flyai && echo "✅ flyai 已安装" || echo "❌ flyai 未安装" ``` 如果两个工具都已安装，跳过安装步骤，直接进入 Workflow。 ### 安装指南 如果任一工具未安装，详见 **[references/installation.md](references/installation.md)** 获取完整安装步骤： - **flyai**: `npm i -g @fly-ai/flyai-cli` - **opencli**: Chrome + Playwright MCP Bridge插件 + 登录小红书 ### 环境就绪确认 继续前，确认以下条件满足： - ✅ `opencli` 命令可用 - ✅ `flyai` 命令可用 - ✅ Chrome 已登录小红书 如果任一条件不满足，先阅读 [installation.md](references/installation.md) 完成安装再继续。 ## Workflow **触发条件：** 用户需求是自然语言、非结构化的，标准搜索接口无法承接。 ``` 用户需求（自然语言） ↓ 语义理解层 - opencli xiaohongshu search ↓ 内容发现 → AI 提取酒店名 ↓ 供给对接层 - flyai search-hotel ↓ 输出：测评 + 价格 + 链接 ``` ### Step 1: 内容发现 使用 `opencli xiaohongshu search` 在小红书搜索用户真实测评： ```bash opencli xiaohongshu search "三亚带狗住酒店推荐" opencli xiaohongshu search "无边泳池酒店 网红打卡" opencli xiaohongshu search "适合带老人的酒店 安静" ``` 从搜索结果中提取： - 酒店名称 - 用户真实评价（环境、设施、服务） - 实际体验（图片、视频） ### Step 2: AI提取酒店名 从小红书搜索结果中识别候选酒店： - 提取酒店名称（注意区分民宿、公寓、酒店） - 收集用户评价关键词 - 过滤广告内容、推广贴 ### Step 3: 飞猪搜索 对每个候选酒店，使用 `flyai search-hotel` 获取可预订信息： ```bash flyai search-hotel --key-words "三亚某度假酒店" --check-in "2024-01-15" --check-out "2024-01-17" ``` 获取： - 实时价格 - 房型信息 - 预订链接 - 用户评分 ### Step 4: 综合输出 返回给用户： 1. **小红书真实测评**（1-2条精华内容） 2. **飞猪预订信息**（价格、链接） 3. **综合推荐结论**（为什么推荐） ## 失败处理 | 情况 | 处理方式 | |------|---------| | 小红书搜不到相关内容 | 扩大搜索范围，或提示"暂无相关内容，建议换个说法试试" | | 提取的酒店名在飞猪搜不到 | 提示"该酒店可能不在飞猪，建议联系酒店直接预订" | | 内容平台评价不客观 | 标注来源，提示"信息来自用户分享，建议入住前与酒店确认" | | 用户需求太模糊 | 引导用户补充关键信息（目的地、时间、预算等） | ## Examples 详见 [references/examples.md](references/examples.md) 获取完整输入输出示例。 ## Tips - **语义理解优先**：先理解用户真实需求，再选择合适的关键词搜索小红书 - **多角度搜索**：一个需求可能需要多次搜索（如"宠物友好"可搜"带狗住"、"宠物入住"、"狗狗友好"） - **信息交叉验证**：小红书评价 + 飞猪评分 = 更可靠的推荐 - **标注来源**：让用户知道信息来自真实用户分享，而非官方宣传


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zhangpengle/military-bidding-fetcher (version 0.2.6)

--- name: milb-fetcher description: 军工采招商机自动抓取工具。从全军武器装备采购信息网、军队采购网、国防科大采购网抓取招标信息，过滤并生成 Excel 报表。当用户说"抓取商机"、"查新"、"采集招标"时触发。 metadata: {"openclaw":{"emoji":"🕷️","requires":{milb-fetcher},"install":"pip install -e {baseDir}"}} --- # Milb Fetcher 从三大军工采购平台自动抓取招标信息。 ## 快速使用 - `/milb-fetcher` → 自动检测各渠道最新可用日期并抓取（默认） - `/milb-fetcher --help` → 显示帮助信息 日期选择（三选一，不指定则自动检测） - `/milb-fetcher --today` → 抓取今日 - `/milb-fetcher --yesterday` → 抓取昨日 - `/milb-fetcher --date 2026-03-23` → 抓取指定日期 筛选参数 - `--keywords "关键词1,关键词2"` → 核心关键词 - `--exclude-keywords "排除词1,排除词2"` → 排除关键词 - `--high-value-keywords "高价值词1,高价值词2"` → 高价值关键词（用于推荐评级） - `--regions "地区1,地区2"` → 地区筛选（仅对军队采购网生效） 输出控制 - `--output /path/to/file.xlsx` → 指定输出路径（默认存至 `~/.openclaw/workspace/military-bidding/军队采购商机汇总_{date}.xlsx`） - `--no-auto-latest` → 禁用自动检测最新日期（未指定日期时改用今日） ## 数据源 - 全军武器装备采购信息网 - 军队采购网 - 国防科大采购信息网 ## 推荐等级 基于 `FETCHER_HIGH_VALUE_KEYWORDS` 配置自动评定： - **高**：标题命中高价值关键词 - **中**：标题命中核心关键词但未命中高价值词 - **低**：其他匹配项 ## 过滤词 通过 `FETCHER_EXCLUDE_KEYWORDS` 配置，命中排除词的条目将被过滤掉。 ## 触发词 抓取、采集、爬虫、查新、每日商机 ## 配置文件 配置文件位于 `milb_fetcher/.env`（独立配置），可配置以下参数： | 环境变量 | 用途 | 格式 | |----------|------|------| | `FETCHER_KEYWORDS` | 核心关键词，逗号分隔 | `词1,词2,...` | | `FETCHER_EXCLUDE_KEYWORDS` | 排除关键词，逗号分隔 | `词1,词2,...` | | `FETCHER_HIGH_VALUE_KEYWORDS` | 高价值关键词，逗号分隔 | `词1,词2,...` | | `FETCHER_REGIONS` | 地区，逗号分隔 | `省份1,省份2,...` | 创建配置文件可复制 `milb_fetcher/.env.example` 为 `milb_fetcher/.env` 后修改。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xdd-xund/ai-dianzhang (version 1.0.0)

# AI店长 (ai-dianzhang) > 躺着开店，AI店长帮你搞定一切 你的 AI 电商助手。竞品监控、爆款文案、客服话术、数据日报——一个 Skill 搞定开店全流程。 ## 能力一览 ### 🔍 竞品监控 告诉店长你卖什么，它自动帮你： - 搜淘宝、拼多多、1688、小红书的同类商品 - 分析价格区间、销量趋势、评价关键词 - 找出竞品的卖点和差评痛点 - 每天/每周自动推送竞品动态 ``` 店长，帮我看看淘宝上卖"露营折叠椅"的竞品情况 ``` ### ✍️ 爆款文案 丢一张图或简单描述，店长帮你写： - 商品标题（适配淘宝/拼多多/小红书不同风格） - 详情页文案（卖点提炼 + 场景化描述） - 短视频脚本（带口播文案 + 分镜建议） - 小红书种草笔记（标题党 + emoji + 标签） - 直播话术（开场/讲品/逼单/感谢） ``` 店长，帮我写一个露营折叠椅的淘宝标题，主打轻便和高颜值 ``` ### 💬 客服话术 根据你的商品自动生成： - 售前FAQ（尺寸/材质/发货/优惠） - 售后模板（退换货/物流/差评挽回） - 催付话术（温柔提醒不惹人烦） - 好评引导（不违规的巧妙话术） ``` 店长，我卖的是蓝牙耳机，帮我生成一套客服话术 ``` ### 📊 数据日报 设定关注的品类或竞品，店长每天自动： - 汇总行业热搜词变化 - 监控竞品价格调整 - 发现新上架的爆款 - 分析评价趋势（差评预警） ``` 店长，每天早上9点帮我看看"无线充电器"这个品类的动态 ``` ### 🏷️ 选品建议 不知道卖什么？店长帮你分析： - 当前热门品类和趋势 - 蓝海市场机会（竞争少、需求大） - 季节性选品建议 - 1688 货源匹配 ``` 店长，最近有什么好卖的品类推荐？预算5万以内 ``` ### 📱 多平台适配 同一个商品，一键生成多平台内容： - 淘宝/天猫：搜索优化标题 + 详情页 - 拼多多：低价引流标题 + 拼单文案 - 小红书：种草笔记 + 标签策略 - 抖音：短视频脚本 + 直播话术 - 1688：批发详情 + 工厂实力描述 ``` 店长，这个产品帮我出一套全平台文案 ``` ## 快速开始 安装后直接跟你的 OpenClaw 说： ``` 店长，我想在淘宝开一家卖露营装备的店，帮我分析一下市场 ``` 店长会自动： 1. 搜索当前露营装备市场数据 2. 分析头部竞品的价格、销量、评价 3. 找出差异化机会点 4. 推荐具体选品方向 5. 给出定价建议 ## 使用场景 | 你说 | 店长做 | |------|--------| | "帮我看看XX品类的竞品" | 搜索分析竞品数据，输出报告 | | "帮我写个商品标题" | 生成多个标题方案，标注关键词 | | "这个差评怎么回复" | 生成专业得体的回复话术 | | "最近什么好卖" | 分析热门趋势，推荐蓝海品类 | | "帮我出一套详情页文案" | 完整的卖点+场景+参数+FAQ | | "每天帮我盯着竞品" | 设置定时监控，有变化就通知 | ## 工作原理 ``` 用户指令 → AI店长理解需求 ↓ 搜索电商平台数据（web_search + web_fetch） ↓ AI 分析（竞品/趋势/机会） ↓ 生成内容（文案/话术/报告） ↓ 定时任务（cron 监控 + 日报推送） ``` - 不需要你提供任何平台账号密码 - 不需要 API 授权 - 所有数据来自公开信息 - 用你配置的 LLM 生成内容（推荐配合 chinese-llm-router 使用国产模型，成本更低） ## 配合使用 - **chinese-llm-router**: 用国产模型（DeepSeek/Qwen）降低成本，文案质量更好 - **shrimp-plaza**: 在龙虾广场分享好物，社区引流 ## 适合谁 - 淘宝/拼多多中小卖家 - 想开店但不知道卖什么的新手 - 一个人管多个店铺的个体户 - 做跨境电商需要多语言文案的卖家 - 任何想"躺着开店"的人 ## 定价参考 这是一个免费的 OpenClaw Skill。你只需要承担 LLM 调用成本： - 用 DeepSeek V3.2：约 ¥0.01/次分析 - 用 Qwen3-Max：约 ¥0.03/次分析 - 每天监控+日报：约 ¥0.5-1/天 一个月不到 ¥30，比任何电商工具都便宜。 ## Links - 🦐 AI Plaza: https://ai.xudd-v.com - 📦 ClawHub: https://clawhub.ai/Xdd-xund/ai-dianzhang - 🤖 配套 Skill: https://clawhub.ai/Xdd-xund/chinese-llm-router


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: djc00p/twitch-clip (version 1.0.2)

--- name: twitch-clip metadata: {"clawdbot":{"emoji":"🎬","requires":{"bins":["curl"],"env":["TWITCH_CLIENT_ID","TWITCH_ACCESS_TOKEN","TWITCH_BROADCASTER_ID"]},"os":["linux","darwin"]}} description: Create Twitch clips of the current live stream via the Twitch API. Use when a streamer says "clip that", "clip the last X seconds", "make a clip", "clip it", or any variation requesting a clip of the current broadcast. Requires Twitch API credentials configured in environment variables. Has a 30-second cooldown between clips to prevent spam. --- # Twitch Clip Skill Creates a clip of the last 30–60 seconds of a live Twitch stream via the Twitch Clips API. Includes a 30-second cooldown to prevent accidental spam clipping. ## Setup ### 1. Create a Twitch app 1. Go to https://dev.twitch.tv/console/apps 2. Register a new app (OAuth Redirect URL: `http://localhost`, Category: Other) 3. Copy your **Client ID** and generate a **Client Secret** ### 2. Get an OAuth token Visit this URL in your browser (replace `YOUR_CLIENT_ID`): ```text https://id.twitch.tv/oauth2/authorize?client_id=YOUR_CLIENT_ID&redirect_uri=http://localhost&response_type=token&scope=clips:edit ``` Copy the `access_token` from the redirect URL. ### 3. Set environment variables ```bash export TWITCH_CLIENT_ID="your_client_id" export TWITCH_ACCESS_TOKEN="your_access_token" export TWITCH_BROADCASTER_ID="your_broadcaster_id" # numeric user ID ``` To find your broadcaster ID: ```bash curl -s -H "Authorization: Bearer $TWITCH_ACCESS_TOKEN" \ -H "Client-Id: $TWITCH_CLIENT_ID" \ https://api.twitch.tv/helix/users | python3 -c "import json,sys; print(json.load(sys.stdin)['data'][0]['id'])" ``` ## Usage Run the clip script: ```bash bash scripts/create_clip.sh [duration_seconds] ``` - Duration defaults to 30, max 60 - Stream must be live or the API returns a 404 ## Cooldown A 30-second cooldown is enforced between clips. If a clip request comes in during cooldown: - Script exits with code 2 and prints remaining wait time - Reply: `⏳ Cooldown active — try again in Xs` - Do NOT retry automatically — wait for the next explicit clip request ## Trigger phrases - "clip that" - "clip the last X seconds" - "make a clip" - "clip it" ## Response format **Success:** `Clipped! 🎬 https://clips.twitch.tv/<clip_id>` **Cooldown active:** `⏳ Cooldown active — try again in Xs` **Not live:** `Stream is offline — can't clip right now` ## Token refresh Tokens expire. If you get a 401 error, re-authorize using the URL in Setup step 2.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aipoch-ai/grammar-checker-ama (version 0.1.0)

--- name: grammar-checker-ama description: Grammar checking tool for AMA style medical writing version: 1.0.0 category: General tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Grammar Checker (AMA) Specialized grammar checking for medical writing following AMA Manual of Style. ## Usage ```bash python scripts/main.py --text "Your text here" ``` ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--text`, `-t` | string | - | Yes | Text to check for AMA style compliance | ## Features - Passive voice detection - AMA style compliance - Medical terminology validation ## Output Grammar correction suggestions with explanations. ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hybredm/model-matrix (version 1.0.0)

--- name: model-matrix version: 1.0.0 description: Weighted model-routing matrix for OpenClaw (cost-aware, policy-aware, daily scorecard template). --- # Model Matrix Skill Use this skill to choose the best model per category using a blended score and policy constraints. ## Core Policy 1. Use the cheapest model that preserves quality. 2. If Anthropic is excluded, auto-promote #2. 3. Only swap model routes when score delta is material and confidence is high. ## Weights - Real task evals: 45% - Benchmarks: 30% - Sentiment (X/Reddit): 20% - Cost: 5% ## Effective Routing (Current) - Research / Planning: Gemini 3.1 Pro - Complex Coding / Complex Tasks / Enterprise Discussion: GPT-5.3 Codex - Routine Coding + Repeat Cron Ops: GPT-5-mini - Citizen Sentiment (X): Grok (x_search) - Photo/Image: Gemini image stack - Video intelligence trends: Grok ecosystem ## Daily Scorecard Template | Category | Real Eval (45) | Bench (30) | Sentiment (20) | Cost (5) | Raw Score (/100) | Raw #1 | Effective #1 | Confidence | |---|---:|---:|---:|---:|---:|---|---|---:| | Research | | | | | | | | | | Planning | | | | | | | | | | Coding (complex) | | | | | | | | | | Coding (routine) | | | | | | | | | | Creative Writing | | | | | | | | | | Enterprise Discussion | | | | | | | | | | Citizen Sentiment (X) | | | | | | | | | ## Notes - If Anthropic returns, include it in raw ranking and let policy choose effective winner. - MiniMax remains trial-only unless quality holds over 7 days.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaofang142/xiaohongshu-auto-publish (version 1.0.0)

--- name: xiaohongshu-auto-publish description: 小红书自动发布技能 - AI 文案 + 中国风封面 + 一键发布 version: 1.0.0 author: 太子 price: 0.01 currency: CNY tags: - 小红书 - 自动化 - 发布 - AI 文案 - 中国风 --- # 小红书自动发布技能 > 一键发布小红书笔记，包含 AI 文案生成 + 中国风封面 + 自动发布 ## 功能特点 - ✅ AI 智能文案（700-800 字） - ✅ 中国风古典插画封面 - ✅ 自动发布到小红书 - ✅ 字数自动优化 - ✅ 排版精美 - ✅ 零 API 成本 ## 使用方法 ```bash python skill.py "发布主题" ``` ## 示例 ```bash python skill.py "OpenClaw 安装教程" python skill.py "效率工具推荐" python skill.py "知识变现指南" ``` ## 输出 - 封面图：`xhs_cover_chinese.png` - 文案：自动生成（700-800 字） - 发布结果：成功/失败反馈 ## 系统要求 - Python 3.8+ - Pillow (PIL) - xiaohongshu-mcp 已配置 - 小红书已登录 ## 收费说明 - 价格：¥0.01/次 - 计费方式：按发布次数 - 支付平台：SkillPay ## 技术支持 遇到问题请联系太子！


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: guohongbin-git/agent-services-cn (version 1.0.0)

--- name: agent-services-cn version: 1.0.0 description: Agent Services Marketplace - Find AI agents for research, coding, trading, and more license: MIT --- # Agent Services Marketplace 🤝 AI agent service directory and pricing guide for the agent economy. ## Features ### 1. Service Categories 📂 Browse agents by what they offer. ```bash ./scripts/categories.sh ``` ### 2. Pricing Guide 💰 Understand what to charge or pay. ```bash ./scripts/pricing.sh ``` ### 3. Find Agents 🔍 Search for agents by skill. ```bash ./scripts/find.sh "crypto" ``` ### 4. Service Template 📝 Template for offering your own services. ```bash ./scripts/template.sh ``` ## Why This Matters The agent economy is growing. Agents are starting to pay each other for services via: - x402 protocol (USDC on Solana) - Lightning Network (BTC) - ClawTasks and bounties This skill helps you navigate the emerging A2A (agent-to-agent) marketplace. ## Installation ```bash clawhub install agent-services-cn ``` ## Author money-maker-ai ## Changelog ### v1.0.0 (2026-02-18) - Initial release


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