OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yuval-deepdub/deepdub-tts (version 0.1.5)

--- name: Deepdub TTS version: 0.1.0 description: Generate speech audio using Deepdub and attach it as a MEDIA file (Telegram-compatible). tags: [tts, deepdub, audio, telegram] metadata: {"clawdbot":{"requires":{"bins":["python3"],"env":["DEEPDUB_API_KEY","DEEPDUB_VOICE_PROMPT_ID"]},"primaryEnv":"DEEPDUB_API_KEY"}} --- ## What this skill does This skill converts text into speech using Deepdub and returns an audio file as a `MEDIA:` attachment that OpenClaw can send to channels like Telegram. ## Requirements - Python 3.9+ - Deepdub API access ### Permissions This skill requires permission to: - Execute `deepdub_tts.py` (the bundled script) - Write audio files to `OPENCLAW_MEDIA_DIR` only (output path cannot be overridden via CLI arguments) ## Setup Set the following environment variables where OpenClaw runs: Required: - `DEEPDUB_API_KEY` – your Deepdub API key - `DEEPDUB_VOICE_PROMPT_ID` – default voice prompt to use Optional: - `DEEPDUB_LOCALE` (default: `en-US`) - `DEEPDUB_MODEL` - `OPENCLAW_MEDIA_DIR` (default: `/tmp/openclaw_media`) ### Free Trial Credentials For testing only, you can use these free trial credentials: ``` DEEPDUB_API_KEY=dd-00000000000000000000000065c9cbfe DEEPDUB_VOICE_PROMPT_ID=11f3403d-35b9-4817-8d55-f41694ea6227 ``` > **Note:** These are rate-limited trial credentials for evaluation purposes only. Do not use for production. Obtain your own API key and voice prompts from Deepdub for production use. ## Install dependency Install the official Deepdub Python SDK: ```bash pip install deepdub ``` Or using [uv](https://github.com/astral-sh/uv) (faster alternative): ```bash uv pip install deepdub ```

path: requirements.txt
content: deepdub


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiajiaoy/couponclaw (version 1.1.4)

# CouponClaw > Find coupons, stack cashback, and maximize savings — across China, US, UK, Australia, Southeast Asia, and DTC brands worldwide. CouponClaw is the only coupon skill that covers every major market in one place. It searches verified coupon databases, finds cashback portal rates, and calculates the best stacking strategy so you always know the exact final price before checkout. ## What makes CouponClaw different Most coupon tools check one platform in one country. CouponClaw runs a 3-layer strategy: 1. **Layer 1 — Coupons**: Real-time browser search across region-specific coupon sites (smzdm, RetailMeNot, VoucherCodes, OzBargain, ShopBack, and more) 2. **Layer 2 — Cashback stacking**: Compares 返利网, Rakuten, TopCashback, and ShopBack rates — and checks if they stack with the coupon 3. **Layer 3 — DTC brand check**: Detects hidden first-order discounts and newsletter signup offers on brand official sites It also runs a daily deals briefing (via cron) that surfaces the hottest community-verified deals from each region every morning. ## Trigger phrases Use CouponClaw when you say things like: - "is there a coupon for..." - "promo code for..." - "discount code" - "voucher code" - "coupon code" - "cashback for..." - "有没有优惠券" - "有没有券" - "优惠码" - "折扣码" - "返利" - "省钱" - "领券" - "打折" - "今日优惠" - "daily deals" - "best deals today" ## Scripts | Script | Command | Description | |---|---|---| | `find.js` | `node scripts/find.js <product or store> [--region cn\|us\|uk\|au\|sea\|all] [--lang zh\|en]` | Find all available coupons + cashback stacking for a product or store | | `cashback.js` | `node scripts/cashback.js <store> [--spend amount] [--lang zh\|en]` | Look up and compare cashback rates across all platforms | | `daily-deals.js` | `node scripts/daily-deals.js [--region cn\|us\|uk\|au\|sea\|all] [--lang zh\|en]` | Generate today's top deals briefing (for cron push) | | `push-toggle.js` | `node scripts/push-toggle.js on\|off\|status <userId> [--morning HH:MM] [--region ...] [--channel telegram\|slack\|feishu\|discord] [--lang zh\|en]` | Manage daily deal push subscription | ## Region coverage | Region | Coupon sources | Cashback | |---|---|---| | 🇨🇳 China | 什么值得买, 京东领券中心, 淘宝聚划算, 折800 | 返利网, 什么值得买返利 | | 🌏 Chinese overseas | Dealmoon (North America) | Rakuten | | 🇺🇸 US | RetailMeNot, Slickdeals, Amazon Coupons | Rakuten, TopCashback | | 🇬🇧 UK | VoucherCodes, HotUKDeals, MyVoucherCodes | TopCashback | | 🇦🇺 Australia | OzBargain, Cashrewards | ShopBack | | 🌏 Southeast Asia | ShopBack, iPrice | ShopBack | | 🏷️ DTC brands | Official site popup detection, newsletter signup offers | Rakuten / TopCashback | ## No API required CouponClaw uses browser navigation to read real-time data directly from coupon and cashback sites. No API keys, no subscriptions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 1999azzar/pihole-ctl (version 1.0.1)

--- name: pihole-ctl description: Manage and monitor local Pi-hole instance. Query FTL database for statistics (blocked ads, top clients) and control service via CLI. Use when user asks "how many ads blocked", "pihole status", or "update gravity". --- # Pi-hole Controller ## Usage - **Role**: Network Guardian. - **Trigger**: "Check Pi-hole", "Adblock status", "Who is querying top domains?". - **Output**: JSON stats or CLI command results. ## Capabilities 1. **Statistics**: Query FTL database for accurate logs (Last 24h, Top Domains). 2. **Management**: Enable/Disable blocking (`pihole enable/disable`). 3. **Blocklists**: Update Gravity (`pihole -g`). 4. **Audit**: Identify chatty clients or top blocked domains. ## Scripts - `scripts/query_db.py`: Python script using native `sqlite3` library to query Pi-hole stats safely. - Requires read permission on `/etc/pihole/pihole-FTL.db`. - Usage: `python3 scripts/query_db.py --summary --hours 24` - Usage: `python3 scripts/query_db.py --top 10` ## Permissions - **Database Access**: The user running this skill must have read access to `/etc/pihole/pihole-FTL.db`. - Recommended: Add user to `pihole` group (`usermod -aG pihole ubuntu`). - **Management Commands**: `pihole` CLI commands (enable/disable) require `sudo` or must be run by a user with appropriate permissions. ## Reference Materials - [Database Schema](references/db-schema.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ljt1469/critical-thinking-framework (version 1.0.0)

--- name: critical-thinking-framework version: 1.0.0 description: 基于津巴多心理学六问法，对任何新观点、主张或信息进行系统性、结构化的批判性审视。通过六个递进维度的分析，识别潜在偏差、验证逻辑有效性，并形成经过多重检验的审慎结论。适用于评估新闻报道、科学主张、政策建议及日常决策。 --- ### Parameters | Parameter | Type | Required | Description | | -------------------- | ------ | -------- | ------------------------------------------------------------ | | `claim` | string | Yes | 待分析的具体观点或主张（如"糖会导致儿童亢奋"） | | `source_info` | object | No | 观点来源信息，包含`author`（作者）、`credentials`（资质）、`affiliation`（所属机构）、`potential_interests`（潜在利益） | | `evidence_available` | array | No | 当前可获取的支持性证据列表（轶事、统计数据、实验结果等） | | `domain` | string | No | 观点所属领域（医学/心理学/社会学/政策等），用于选择专业视角 | ### Execution Steps **Step 1: 权威溯源与利益审查 (Source & Interest Verification)** - 核查提出者是否具备相关领域的实际专业资质（非自封专家） - 识别利益冲突：是否存在政治资本、经济利益或媒体曝光动机？ - **输出标记**: `source_credibility` (high/medium/low), `conflict_of_interest` (boolean) **Step 2: 主张合理性校准 (Claim Plausibility Calibration)** - 应用"卡尔·萨根原则"：不寻常的主张需要不寻常的证据 - 评估与现有科学共识的兼容性，警惕"突破/革命"式宣传 - 质疑"快速解决复杂问题"的承诺（如简单方法解决青少年犯罪） - **输出标记**: `claim_plausibility` (plausible/suspicious/extreme), `evidence_barrier` (high/low) **Step 3: 证据质量分级 (Evidence Hierarchy Evaluation)** - 区分轶事证据（个人证词、精心挑选的案例）与实证证据（可重复实验、大样本研究） - 检查是否存在"将适合少数人的事物推断为适合所有人"的风险 - **输出标记**: `evidence_type` (anecdotal/correlational/experimental), `generalizability_risk` (high/medium/low) **Step 4: 认知偏差识别 (Cognitive Bias Detection)** - **情感偏差**：是否存在绝望、恐惧等强烈情绪影响判断？（如绝望父母 grab any straw） - **证实性偏差**：是否只记住支持信念的证据而忽略反证？（如占星术信徒的选择性记忆） - **期望偏差**：观察者是否因预期而看到"想看到的结果"？（如预期糖导致亢奋而高估活跃程度） - **输出标记**: `detected_biases` (array: [emotional_bias, confirmation_bias, expectancy_bias]) **Step 5: 逻辑谬误规避 (Logical Fallacy Avoidance)** - **相关-因果谬误检查**：是否将时间先后或统计关联等同于因果关系？ - 考虑第三变量解释（C变量）：是否存在同时影响A和B的隐藏因素？ - 警惕"常识替代科学"（如"物以类聚"与"异性相吸"的矛盾常识） - **输出标记**: `causal_validity` (valid/suspected_fallacy), `third_variables` (array) **Step 6: 多元视角整合 (Multi-perspective Integration)** - 强制要求至少3个不同学科视角： - **生物/心理视角**：遗传、神经机制、人格特质（如糖代谢对大脑的影响） - **社会/情境视角**：环境压力、群体规范、文化背景（如聚会氛围对儿童行为的影响） - **经济/系统视角**：成本收益、结构性因素、激励机制 - **输出标记**: `perspectives_considered` (array), `complexity_level` (simple/complex) ### Output Format ```json { "analysis_summary": { "overall_trustworthiness": "low/medium/high", "key_risks": ["利益冲突", "相关-因果谬误", "情感偏差"], "recommended_action": "reject/suspend_judgment/accept_with_caution" }, "detailed_assessment": { "source": {"credibility": "...", "conflicts": "..."}, "claim": {"plausibility": "...", "required_evidence_level": "..."}, "evidence": {"quality": "...", "generalizability": "..."}, "biases": ["emotional_bias", "confirmation_bias"], "logic": {"causal_validity": "...", "alternative_explanations": ["..."]}, "perspectives": ["biological", "social", "economic"] } }


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whyy9527/idea-to-product (version 0.1.1)

--- name: idea-to-product description: Turn product ideas into MVPs, UX flows, Playwright checks, and explicit feedback loops. Built by ClawAether. metadata: openclaw: emoji: "🛠️" homepage: https://clawaether.com --- # Idea to Product This is a flow-first product iteration skill. It helps you turn a rough idea into: 1. a constrained MVP 2. a backlog of gaps 3. a UX flow with page responsibilities 4. Playwright-ready browser checks 5. explicit failure routing back into backlog or UX It is intentionally atomic. Each command does one job. ## Commands | Command | What it does | |---|---| | `idea-to-mvp` | Turn a raw product idea into one feedback-oriented MVP | | `mvp-to-stories` | Split the MVP into gap cards and nearly-done cards | | `story-to-ux-flow` | Turn stories into page relationships, responsibilities, and Stitch boundaries | | `ux-flow-to-playwright-checks` | Turn UX flow into atomic browser checks | | `playwright-checks-to-feedback` | Route failed checks back to stories, UX, or system mismatch | ## Why this exists Most teams stop at "we have a plan." That is not a loop. A real loop is: `idea -> mvp -> stories -> ux flow -> stitch -> playwright checks -> feedback -> code` This skill gives you explicit command-level handoffs for that chain. Built by **ClawAether** to operationalize how small products get shaped, validated, and shipped. ## When to use it Use this skill when you want to: - shrink a big idea into one small feedback product - separate missing work from already-nearly-done work - design pages by relationship first, not by single-page aesthetics - verify real browser behavior instead of trusting screenshots - route failures back into the right layer instead of guessing ## What it does not do - It does not do heavy project management - It does not assign people to days - It does not replace your browser automation tool - It does not require copying a large project docs tree ## Links - ClawAether: https://clawaether.com - API docs: https://clawaether.com/docs - Leaderboard: https://clawaether.com/leaderboard


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: autogame-17/config-validator (version 1.0.0)

# config-validator A utility skill to validate OpenClaw configuration files (`openclaw.json`, `.env`, `package.json`) and ensure critical settings are present and correctly formatted. ## Usage ```bash node skills/config-validator/index.js [--fix] ``` ## Features - Checks for required environment variables (`.env`). - Validates `openclaw.json` structure. - Checks `package.json` dependencies against installed modules. - Reports missing or malformed configurations. - Optional `--fix` flag attempts basic repairs (e.g., creating missing files from templates). ## Dependencies - None (built-in Node.js modules).


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/yiyan (version 0.1.0)

--- name: yiyan-hot-trend description: 注册“文心一言”技能；用于百度大模型相关公开页面信息整理。 homepage: `https://yiyan.baidu.com/` metadata: {"clawdbot":{"emoji":"🤖"}} --- # 文心一言 用途与边界 - 面向公开产品页与文档中心的检索与内容摘要 - 不提供账号操作、接口调用或权限绕过能力 - 仅处理公开页面的轻量信息整理 关键入口 - 主页：https://yiyan.baidu.com/ - 千帆平台与文档：百度智能云公开入口 - 定价与公告：站点入口 常见任务 - 模型能力与版本说明摘要（上下文长度、功能特性） - 定价与调用配额说明提取（公开信息） - 文档目录与示例链接汇总 数据字段 - 模型名称、版本、上下文长度、功能特性、链接 - 定价页链接、免费额度说明、地域与服务状态 - 文档标题、目录链接、示例入口 自动化要点 - 页面动态加载，需等待完成后解析 - 不进行API调用或账号相关操作 - 频率控制，尊重平台访问限制 示例流程 - 能力摘要：访问模型页 → 抽取版本与特性 → 输出摘要 - 定价整理：访问定价入口 → 提取费用与额度 → 输出清单 - 文档汇总：进入文档中心 → 抽取目录与示例 → 输出链接集合 合规提示 - 遵守平台规则与账号安全要求 - 不存储或输出敏感信息


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huanz/filesystem-access (version 1.0.0)

--- name: filesystem-access description: 安全的本地文件读/写/列表能力，默认只在 OpenClaw workspace 目录内工作。 --- ## 使用建议 - 当你需要查看、编辑或列出工作区内的文件时使用本技能。 - 仅访问相对路径或工作区子目录，避免越权访问用户其他目录。 - 典型场景：查看日志、生成 Markdown 报告、保存脚本或配置文件。 ## 安全约束 - 禁止在 workspace 之外执行写入操作。 - 避免删除用户重要文件，如配置、源码或系统文件。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-behavior-analysis (version 1.0.0)

--- name: ai-intelligent-behavior-analysis version: 1.0.0 description: 行为分析，动作识别 + 异常检测。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能行为分析系统 ## 描述 行为分析，动作识别 + 异常检测。 ## 功能 - 动作识别（动作分类） - 异常行为（打架/跌倒） - 人群分析（密度/流向） - 轨迹分析（行为模式） - 预警推送（实时告警） ## 定价 - 基础版：¥399/月（1 路/月） - 专业版：¥1999/月（10 路/月） - 企业版：¥5999/月（无限路） ## 适用场景 - 安防监控 - 工厂安全 - 养老监护 - 校园安全 ## 技术栈 - Python + FastAPI - 行为识别模型 - 视频分析 - 实时告警 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-behavior-analysis cd ai-intelligent-behavior-analysis pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guillaumemaka/learned-workflows (version 1.0.0)

--- name: learned-workflows description: Reusable workflow notes for repeatable tasks. --- # Learned Workflows ## Workflow - Sender (untrusted metadata): ```json { "label": "openclaw-control-ui", "id": "openclaw-control-ui" } ``` [Wed 2026-05-06 17:48 EDT] Minor layout modifications: I like the stats card row, but for the listing I prefer a list view (datatable view) with pagination instead of a card list layout - Verify the result before final reply. - Record durable pitfalls as short bullets; avoid copying transcript noise.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: woodcoal/china-weather-skill (version 1.0.0)

--- name: 中国城市天气 slug: weather-zh description: 中国城市天气查询工具 homepage: https://www.weather.com.cn/ --- # 🌤️ 中国城市天气查询工具 ## 🛠️ 使用方法 ``` python weather.py [-json] [-today] [-life] <地区名称> ``` ### 🎛️ 参数说明： | | | | | ------------ | --------- | ------------------------------------------------------------------------------------------------------------------ | | **参数** | **必填** | **作用描述** | | `<地区名称>` | **✅ 是** | 你要查询的城市或者区县，比如：`北京`、`深圳`、`铁岭`。（支持地级市及大部分县级市） | | `-json` | ❌ 否 | **极客模式**：将输出格式转为结构化的 JSON 数据，非常适合喂给 AI 或被其他程序调用。 | | `-today` | ❌ 否 | **只争朝夕模式**：无情裁掉未来 6 天的预报，屏幕上只保留“今天”的数据，界面极其清爽。 | | `-life` | ❌ 否 | **婆婆妈妈模式**：默认情况下只显示天气和温度。加上这个开关后，会额外附送感冒、穿衣、洗车、紫外线等各种“生活指数”。 | ### 关于查询的地区名称 1. 必须是中文； 2. 使用标准地区名称，如"成都"、"上海"而非简写，如："川"； 3. 只要到地区最后一级即可，如：xx区，xx县，xx市等，查询时无需带区，市，县等字样。 **示例** - "北京市" 使用 "北京"； - "湘西土家族苗族自治州" 使用 "湘西"； - "北京市海淀区" 使用 "海淀"。 ## 📦 数据输出结构示例 (JSON 模式) 如果在调用时使用了 `-json` 参数，你将得到类似如下的完美结构化数据： ``` { "city": "北京", "city_code": "101010100", "query_time": "2026-03-20 01:30:00", "source": "weather.com.cn", "forecast": [ { "date": "20日（今天）", "weather": "晴", "temp_high": "15℃", "temp_low": "3℃", "wind": "北风", "wind_level": "3-4级", "life_indices": [ { "name": "穿衣指数", "key": "clothing", "level": "较冷", "description": "建议着厚外套加毛衣等服装。" } // ... 其他生活指数 ] } ] } ``` ## 🛠️ 备用方案 如果中国天气网不可用，可使用以下备用方案解析： ```bash # 1. agent-browser 访问 agent-browser open "https://www.weather.com.cn/weather/101010100.shtml" # 2. curl 读取 open-meteo.com curl -s "https://api.open-meteo.com/v1/forecast?latitude=39.9042&longitude=116.4074&current_weather=true&daily=temperature_2m_max,temperature_2m_min,weathercode&timezone=Asia%2FShanghai" # 3. wttr.in curl -s "wttr.in/Beijing?T" ``` --- ## 📝 使用场景 当用户询问以下问题时使用本skill： - "今天天气" - "明天天气" - "[城市名]天气" - "会不会下雨" - "气温多少" - "天气查询" - "最近天气" - "[天数]天天气" --- ## ⚠️ 注意事项 1. **天气数据延迟**：中国天气网数据可能略有延迟 2. **网络依赖**：需要能够访问 www.weather.com.cn 3. **生活指数**：指数为通用建议，仅供参考


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lky115/test1234131231242314214124 (version 1.0.1)

--- name: core-code-review description: 在代码提交前或任务完成后使用，进行代码审查 --- # Code Review - 代码审查 ## 概述 系统性地审查代码变更，确保质量、可维护性和正确性。 ## 审查维度 ### 1. 正确性 - 逻辑是否正确？ - 边界情况是否处理？ - 错误处理是否完整？ ### 2. 可读性 - 命名是否清晰？ - 代码结构是否清晰？ - 注释是否有帮助（不是噪音）？ ### 3. 可维护性 - 是否遵循 DRY 原则？ - 是否容易修改和扩展？ - 依赖是否合理？ ### 4. 测试 - 测试覆盖是否充分？ - 测试是否有意义？ - 测试是否可读？ ## 问题分级 | 级别 | 含义 | 处理 | |------|------|------| | **Critical** | 必须修复才能合并 | 阻塞 | | **Important** | 应该修复 | 建议修复 | | **Minor** | 可以改进 | 可选 | | **Nitpick** | 个人偏好 | 仅供参考 | ## 审查输出格式 ```markdown ## 代码审查报告 ### 概览 - 变更: [简述变更内容] - 审查者: AI - 日期: YYYY-MM-DD ### 问题 #### Critical - [ ] **文件:行号** - 问题描述 #### Important - [ ] **文件:行号** - 问题描述 #### Minor - [ ] **文件:行号** - 问题描述 ### 优点 - [列出代码的优点] ### 结论 [通过/需要修改后通过/不通过] ``` ## 嵌入式领域扩展 **额外检查项:** - 内存分配是否安全？ - 是否有资源泄漏风险？ - 中断上下文是否正确使用？ - 时序要求是否满足？ - 功耗影响是否考虑？


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hongjiahao371-pixel/minimax-mcp-tools (version 1.0.0)

# MiniMax MCP - 联网搜索 & 图片理解 ## 功能简介 MiniMax 官方 MCP 服务，提供**联网搜索**和**图片理解**两大功能。 ## 工具列表 | 工具 | 功能 | 参数 | |------|------|------| | `web_search` | 联网搜索 | query: 搜索关键词 | | `understand_image` | 图片理解 | prompt: 图片描述要求, image_url: 图片URL或本地路径 | ## 前置要求 1. **安装 uvx**（如果未安装）： ```bash # macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # 验证安装 which uvx ``` 2. **获取 MiniMax API Key**： - 访问 https://platform.minimaxi.com/subscribe/token-plan - 订阅套餐获取 API Key ## 安装配置 ### 1. 安装 Skill ```bash clawhub install minimax-mcp ``` ### 2. 配置 MCP 编辑 `~/.openclaw/mcp.json`，添加 MiniMax MCP 配置： ```json { "mcpServers": { "MiniMax": { "command": "/Users/js/.local/bin/uvx", "args": ["minimax-coding-plan-mcp", "-y"], "env": { "MINIMAX_API_KEY": "你的API_Key", "MINIMAX_API_HOST": "https://api.minimaxi.com" } } } } ``` > **注意**：Windows 用户请将 `uvx` 路径改为 `uvx.exe` 或对应路径 ### 3. 重启 OpenClaw ```bash openclaw gateway restart ``` ### 4. 验证 MCP 加载 ```bash mcporter list # 应该看到 minimax (2 tools) - healthy ``` ## 使用方法 ### 联网搜索 ```bash mcporter call minimax.web_search query="搜索关键词" ``` 示例： ```bash mcporter call minimax.web_search query="OpenClaw AI助手" ``` ### 图片理解 ```bash mcporter call minimax.understand_image prompt="描述要求" image_url="图片URL或本地路径" ``` 示例： ```bash mcporter call minimax.understand_image prompt="这张图片里有什么？" image_url="https://example.com/image.jpg" ``` ## OpenClaw 中的使用方法 在 OpenClaw 会话中，直接让 AI 帮你搜索或分析图片即可，AI 会自动调用 MCP 工具。 ### 示例对话 - "帮我搜索 XXX" - "帮我分析这张图片"（发送图片） ## 费用说明 - 使用 MiniMax Token Plan 套餐 - 具体额度根据所订阅的套餐决定 - 详情见：https://platform.minimaxi.com/subscribe/token-plan ## 常见问题 ### Q: mcporter list 看不到 minimax？ 检查 MCP 配置是否正确，重启 OpenClaw 网关。 ### Q: 图片理解失败？ - 检查图片 URL 是否可访问 - 本地文件路径需保证路径存在且有读取权限 - 支持格式：JPEG、PNG、GIF、WebP（最大 20MB） ### Q: 搜索结果为空？ 检查 API Key 是否有效，是否有额度。 ## 更新日志 ### v1.0.0 (2026-03-22) - 初始版本 - 支持 web_search 联网搜索 - 支持 understand_image 图片理解


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: caidongyun/aagent-system (version 1.0.0)

--- name: aagent-system description: "多智能体自动化系统，用于AI Agent技能样本采集、安全扫描、威胁情报收集和研究分析。支持单机多进程架构，可自动采集样本、检测恶意技能、提取IOC、更新规则。触发命令: /aagent start" --- # AAgent System 多智能体自动化系统 - 技能安全研究利器 ## 功能 1. **样本采集** - 从npm/GitHub/ClawHub自动采集技能样本 2. **安全扫描** - 检测恶意代码、凭证泄露、C2连接 3. **威胁情报** - 自动收集安全威胁情报 4. **规则迭代** - 持续优化检测规则 ## 架构 ``` 采集层(4进程) → 分析层(2进程) → 研究层(2进程) ``` ## 使用 ### 启动系统 ``` /aagent start ``` ### 停止系统 ``` /aagent stop ``` ### 查看状态 ``` /aagent status ``` ### 查看样本 ``` /aagent samples ``` ## 管理命令 ```bash # 启动 node ~/.openclaw/workspace/skills/aagent-system/bin/agent-manager.cjs start # 状态 node ~/.openclaw/workspace/skills/aagent-system/bin/agent-manager.cjs status # 停止 node ~/.openclaw/workspace/skills/aagent-system/bin/agent-manager.cjs stop ``` ## 配置 目标样本: 2,000,000


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anugotta/website-change-watcher (version 1.0.0)

--- name: website-change-watcher description: Monitor website/docs/pricing changes, diff meaningful updates, and summarize business impact with alert-ready reports. metadata: {"openclaw":{"emoji":"🛰️"}} --- # Website Change Watcher ## Purpose Detect meaningful changes on target websites and turn them into actionable intelligence. ## Core capabilities - monitor tracked URLs and sections - generate structural/content diffs - ignore noisy cosmetic changes - classify change type (pricing, policy, product, docs, legal) - summarize impact and recommended actions ## Workflow 1. Define watchlist URLs and priority. 2. Capture baseline snapshots. 3. Run periodic checks. 4. Diff and classify changes. 5. Publish alerts with impact summary. ## Output format 1. Changed pages list 2. Before/after key lines 3. Impact score 4. Suggested team actions ## 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: jason-aka-chen/auto-data-analyzer-chen (version 1.0.0)

--- name: auto-data-analyzer description: Automated data analysis tool - Input CSV/Excel data and automatically generate comprehensive analysis reports. Includes data cleaning, statistical description, visualization, correlation analysis, and regression analysis. Ideal for rapid data analysis needs. tags: - data - analysis - report - visualization - automation version: 1.0.0 author: chenq --- # auto-data-analyzer Automated data analysis tool that generates comprehensive reports from input data. ## Features ### 1. Data Cleaning - Missing value detection and handling - Outlier detection - Data type conversion - Duplicate value handling ### 2. Statistical Description - Basic statistics (mean, median, standard deviation, etc.) - Quantile analysis - Distribution visualization ### 3. Visualization - Histograms - Box plots - Scatter plots - Heatmaps ### 4. Advanced Analysis - Correlation analysis - Regression analysis - Clustering analysis - Time series analysis ## Usage ### Installation ```bash pip install pandas numpy matplotlib seaborn scikit-learn ``` ### Basic Usage ```python from analyzer import DataAnalyzer analyzer = DataAnalyzer('data.csv') analyzer.clean() analyzer.describe() analyzer.visualize() analyzer.report() # Generate HTML report ``` ### Command Line ```bash python main.py data.csv --output report.html ``` ## Output - HTML analysis report - PNG charts - CSV statistical results ## Use Cases - Business data analysis - Market research reports - User behavior analysis - Sales data analysis


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