OpenClaw/clawhub-security-signals huggingface dataset:
Skill: kenoodl-synthesis/cheat-code (version 1.0.11)

--- name: cheat-code description: "Makes your agent's talents limitless. Tell your agent what you want. Watch it deliver." metadata: openclaw: requires: env: - KENOODL_TOKEN primaryEnv: KENOODL_TOKEN tags: - ai - agent - upgrade - productivity - synthesis - breakthrough - creative - reasoning - brain - openclaw os: - darwin - linux - windows --- # Cheat Code **Makes your agent's talents limitless.** **Tell your agent what you want. Watch it deliver.** Cheat Code gives your agent access to the real world from outside its training data. You set it up once. Takes two minutes. Then your agent handles the rest. Full setup and agent instructions: [instructions.md](instructions.md) --- ## Bonus: the kenoodl app Same account, same results, same breakthroughs — built for humans. kenoodl knows when to deliver a breakthrough. The app is designed to do one thing well without taking up your time. Drop in your own thinking — ideas, questions, problems you're chewing on. You'll get back the move you couldn't see. Better instructions to your agent start with clearer thinking from you. Install the app at [kenoodl.com](https://kenoodl.com). --- ## Token scope `KENOODL_TOKEN` authorizes your agent to access libraries from outside its training data. Agent sends a request to kenoodl.com. All requests are encrypted in transit. New structured data comes back. Context is processed and discarded — nothing is stored, nothing is used for training. Stateless by architecture. --- *Cheat Code is built by [kenoodl](https://kenoodl.com) — synthesis from outside your agents training data.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: abcnull/mac-trans (version 1.0.1)

--- name: mac-trans description: 翻译文本或者翻译文件内容的工具，支持多种语言翻译。使用方式：trans 命令 --- # mac-trans skill 用于文本内容，或者文件内容自动翻译，支持多种语言翻译。 ## 安装 ```bash # 一行安装 brew search translate-shell ``` ## 功能描述 支持自动中英文识别。 支持能力： - 翻译用户输入的文本 - 翻译指定的全部文件内容 - 翻译文件内容中指定的行 - 翻译成指定语言，默认中英互译 ## 使用方式 ```bash # 翻译文本。自动检测英文 → 中文，或者中文 → 英文 trans -e bing -b "要被翻译的文本内容" # 举例子：翻译 "Hello world" trans -e bing -b "Hello world" # 翻译文件内容。自动检测英文 → 中文，或者中文 → 英文 trans -e bing -b "文件在mac中的路径" # 举例子：翻译 ~/document.txt 文件内容 trans -e bing -b ~/document.txt # 比如 翻译前 10 行 head -b -n 10 ～/document.txt | trans -e bing # 翻译特定行 sed -n '5,20p' ~/document.txt | trans -e bing # 可查询支持翻译成的语言。比如其中展示了某种语言，`Japanese - ja` 表示支持翻译成日文 trans -R -e bing # 举例子：将 "Hello world" 翻译成日文 trans -e bing -b :ja "Hello world" ``` ## 异常处理 - brew 未安装，则提示用户安装 - translate-shell 安装失败，则提醒用户自行安装 - 当用户没有提供完整地址，并且被翻译的文件找不到，则提示用户提供文件完整地址 - 当用户要求翻译的语言不在 trans 支持的列表中，则提示用户选择支持的语言


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/stacking (version 0.1.0)

--- name: "Stacking" description: "提供双十一/618等大促多重优惠叠加计算器及最优凑单方案。 " --- # 叠券攻略 ## 筛选项 - 价格区间/折扣力度/历史最低价对比 - 物流时效（次日达/即时配/预约送） - 评分与信任度（官方店/老牌店/评价基数） ## 返回字段 - 实付金额/优惠明细/物流实施轨迹 - 售后保障期/价保状态/退换货记录 - 评价分布/真实买家秀/直播讲解回放 ## 示例请求 - “如何快速使用 叠券攻略 处理 [具体场景任务] ？” - “叠券攻略 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合大促节奏、库存波段与社区评价每日动态调整 ## 注意事项 - 建议搜索优惠券后再下单；确认收货前务必查验商品完整性。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhaohenanhai/pingcode-skills-zhaohenanhai (version 1.2.3)

--- name: pingcode description: PingCode 研发管理平台 API 集成。支持查询工作项、生成周报、管理项目进度等。使用场景：研发管理自动化、团队协作、数据分析。 metadata: openclaw: requires: [] --- # PingCode Skill 通过 PingCode Open API 操作研发管理平台数据。支持**公有云**和**私有部署**两种实例类型。 ## 前置条件 在 PingCode 企业后台创建应用，获取 Client ID 和 Client Secret 配置应用的数据访问范围 ## 凭证获取步骤 1. 登录 PingCode 企业后台 2. 进入「应用管理」→「开放 API」 3. 创建应用，获取 `Client ID` 和 `Client Secret` 4. 配置应用的数据访问范围 ### 1. 安装 在技能库中搜索"pingcode"进行安装，或执行上面的安装命令 ### 2. 配置（首次使用必须操作） 可以直接发消息配置 ```json 如："帮我把 PingCode 配置改成base_url是http://192.168.1.203/open，client_id 是rWWUwTkXYOOo，client_secret 是hwhjFMRXZChJycKUkppHxxxx" ``` 也可以直接编辑skills目录下该skill中的 `config.json`，填入你的 PingCode 实例地址和凭证： ```json { "base_url": "http://192.168.1.203/open", "client_id": "你的Client_ID", "client_secret": "你的Client_SECRET", "request_timeout": 30 } ``` | 配置项 | 说明 | 示例 | |--------|------|------| | `base_url` | PingCode 实例地址（见下表） | 见下方 | | `client_id` | 应用的 Client ID | `rWWUwTkXYOOo` | | `client_secret` | 应用的 Client Secret | `hwhjFMRXZChJ...` | | `request_timeout` | 请求超时时间（秒） | `30` | **常用实例地址：** | 类型 | base_url | |------|----------| | 自建部署（示例） | `http://192.168.1.203/open` | | 公有云 | `https://open.pingcode.com` | ## 功能脚本 ### 获取项目列表 ```bash python scripts/get_projects.py python scripts/get_projects.py --json # JSON 格式 python scripts/get_projects.py --limit 50 # 限制数量 ``` ### 获取指定项目的工作项 ```bash python scripts/get_project_workitems.py --project_name "openclaw" # 按名称匹配 python scripts/get_project_workitems.py --project_id 6a0579ba # 按 ID python scripts/get_project_workitems.py --project_name "openclaw" --json ``` ### 获取我的工作项 ```bash python scripts/get_my_tasks.py python scripts/get_my_tasks.py --assignee 张三 # 按负责人筛选 python scripts/get_my_tasks.py --limit 50 ``` ### 生成项目周报 ```bash python scripts/generate_weekly_report.py --project_id xxx --project_name "openclaw" python scripts/generate_weekly_report.py --output /tmp/weekly_report.md ``` ### 更新工作项 ```bash # 分配负责人 python scripts/update_workitem.py --workitem_id 5fb277c1 --assignee_id user123 # 设置开始和截止时间 python scripts/update_workitem.py --workitem_id 5fb277c1 --start_date "2026-03-12" --due_date "2026-03-20" # 同时更新多个字段 python scripts/update_workitem.py --workitem_id 5fb277c1 --assignee_id user123 --start_date "2026-03-12" --due_date "2026-03-20" --priority "高" # 更新状态 python scripts/update_workitem.py --workitem_id 5fb277c1 --status "进行中" ``` ## API 参考 详见 `references/api_docs.md` 或访问 https://open.pingcode.com/ ## 常见问题 **Q: 怎么切换PingCode环境？** > 只需直接发消息变更配置信息 或手动配置修改 `config.json` 中的 `base_url` 、`client_id`、`client_secret`即可。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zmy1006-sudo/ai-content-xiaohongshu (version 1.0.0)

--- name: ai-content-xiaohongshu description: > 小红书内容创作AI生产流水线系统。将信息采集、素材整理、选题挖掘、文章创作、小红书卡图生成、定时发布等完整工作流引入AI Agent。 触发词：生成小红书内容/创作小红书/写小红书笔记/小红书创作/内容采集/素材管理/选题挖掘/选题库/文章创作/文章模板/小红书卡图/封面图生成/定时发布/内容发布管理/AI内容流水线/批量内容生产/多平台内容分发/AIGC内容工作流/公众号内容创作/微信内容运营/内容选题推荐/热点选题/AI选题评分/风格管理/文章模板管理/HTML模板/Markdown文章 author: cheemao version: 1.0.0 --- # AI Content — 小红书内容生产系统 灵感来源：[CheeMao/ai-content](https://github.com/CheeMao/ai-content) ⭐ 138 将 AI 内容生产完整流水线（采集→选题→创作→配图→发布）引入 OpenClaw Agent，帮助用户高效批量生产小红书/公众号内容。 --- ## 适用场景 1. **批量内容生产**：需要定时/批量生成小红书笔记、公众号文章 2. **选题挖掘**：从 RSS/API/网页采集热点，自动 AI 评分筛选 3. **AI 辅助创作**：多角色协作生成高质量文章，支持 Markdown/HTML 双格式 4. **小红书卡图**：AI 生成封面图 + 小红书成品卡图 5. **工作流编排**：将内容生产编排为自动化流水线 --- ## 核心概念 | 概念 | 说明 | |------|------| | **信息源** | RSS / API / 网页抓取，支持多源并行采集 | | **素材库** | 采集内容汇总，支持筛选与图片过滤 | | **选题库** | AI 评分选题，支持人工复选与优先级排序 | | **文章创作** | Markdown / HTML 模板，支持多风格切换 | | **小红书卡图** | AI 生成封面图 + 成品卡图（需配置图片生成模型）| | **发布管理** | 支持草稿/定时发布，平台包括小红书/公众号 | | **风格管理** | 预设创作风格，适配不同品牌调性 | --- ## 快速开始 ### 环境要求 - Node.js 18+ - Docker（PostgreSQL + Redis） - OpenAI 兼容模型（GLM / Kimi / OpenAI 等） ### 部署步骤 ```bash # 1. 克隆项目 git clone https://github.com/CheeMao/ai-content ai-content cd ai-content # 2. 配置环境变量 cp backend/.env.example backend/.env cp frontend/.env.example frontend/.env.local # 3. 启动数据库与 Redis docker compose up -d # 4. 初始化数据库 cd backend && npm install && npm run db:init # 5. 创建管理员账号 npm run db:bootstrap-admin -- --username admin --password '你的强密码' --email [REDACTED_SECRET] --name 管理员 # 6. 启动后端（默认 http://localhost:3001） npm run start:dev # 7. 启动前端（默认 http://localhost:3000） cd ../frontend && npm install && npm run dev ``` ### 首次配置顺序 1. **配置管理 → AI 平台**：添加模型服务商（如 GLM / Kimi） 2. **配置管理 → AI 模型**：添加可用模型 3. **配置管理 → 默认模型**：设置"文章创作"和"选题推荐"的默认模型 4. **配置管理 → 采集源配置**：初始化默认信息源 5. **风格管理 + 文章模板**：补齐内容风格和模板 6. **素材管理**：开始采集内容 7. **精选选题库**：生成与筛选选题 8. **我的文章 / 小红书笔记**：开始内容创作 --- ## 能力边界 | ✅ 可做 | ❌ 不做 | |--------|--------| | 内容采集与素材管理 | 替代 Docker/PostgreSQL/Redis | | AI 选题评分与筛选 | 直接发布到小红书（需手动复制发布）| | Markdown/HTML 文章创作 | 替代图片生成模型的 API Key | | 小红书卡图生成（需配置模型）| 实时舆情监控 | | 定时发布计划管理 | 微信公众号直连发布 | --- ## AI 模型接入 支持所有 **OpenAI 兼容协议** 的模型服务商： - OpenAI 官方接口 - 各类兼容中转平台（GLM / Kimi / 阶跃星辰等） - 自建兼容网关 - 本地模型服务（Ollama 等） 接口要求： - `/chat/completions` — 文本对话 - `/images/generations` — 图片生成（可选，用于卡图功能） --- ## 许可证 **Personal Use Only License v1.0** - ✅ 个人学习、研究、非商业使用 - ❌ 禁止企业使用 / SaaS 服务 / 商业变现 --- ## AI Agent 使用方式 当你需要批量生产小红书/公众号内容时，可调用此 skill 获取： 1. **选题方向建议**（基于热点采集给出选题优先级） 2. **文章结构模板**（根据小红书/公众号风格生成框架） 3. **小红书文案生成**（带 emoji、标签、口语化表达） 4. **封面图/卡图 Prompt 建议**（为 AI 生图模型提供提示词） 5. **定时发布计划编排**（按周/按月规划内容日历）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/dutch (version 1.0.0)

--- name: Dutch description: Write Dutch that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Dutch is technically correct but sounds off. Too formal. Too stiff. Natives write more directly, with particles and casual flow. Match that. ## Formality Default Default register is too high. Dutch is notably direct. Unless explicitly formal: lean casual. "Hoi" not "Goedendag". "Ja" not "Jazeker". "Oké" not "Akkoord". ## Je vs U Critical distinction: - U: elderly, very formal, customer service - Je/Jij: everyone else, default for most contexts - Dutch internet is almost entirely je/jij - U in casual contexts = stiff, old-fashioned ## Modal Particles These make Dutch natural: - Maar: softening ("Doe maar") - Even/Effe: "just" ("Ik kijk even") - Toch: "right?", emphasis ("Je komt toch?") - Wel: affirmation, contrast ("Ik ga wel") - Hoor: reassurance ("Is goed hoor") - Nou: "well" ("Nou, dat weet ik niet") - Missing these = textbook Dutch ## Fillers & Flow Real Dutch has fillers: - Nou, ja, nee (as filler, not just "no") - Zeg maar, eigenlijk, gewoon - Echt, enzo, ofzo - Weet je, snap je ## Diminutives Dutch loves diminutives (-je, -tje, -pje): - Momentje, biertje, drankje - Beetje, leuk dingetje - Adds warmth and casualness - Overuse is more natural than underuse ## Expressiveness Don't pick the safe word: - Goed → Leuk, Top, Gaaf, Vet - Slecht → Kut, Klote, Bagger - Veel → Mega, Super, Heel erg - Mooi → Prachtig, Geweldig, Te gek ## Common Expressions Natural expressions: - Gezellig! (untranslatable, cozy/fun) - Lekker (not just food - lekker weertje, lekker bezig) - Geen probleem, Prima, Top - Maakt niet uit, Komt goed ## Reactions React naturally: - Echt?, Serieus?, Meen je niet! - Wauw!, Gaaf!, Vet! - Jammer, Balen, Kut - Haha, lol in text ## Belgian Dutch If Belgian, adapt: - Allez, Voilà, Tof - Different expressions, softer tone - Ge/Gij instead of Je/Jij in some regions - Don't mix Dutch and Belgian ## English Mixing Dutch often mixes English: - "Dat is zo awkward" - "Super nice!" - "Ik ben aan het chillen" - Natural in casual contexts ## Punctuation Dutch punctuation: - „Aanhalingstekens" or "quotes" - Numbers: 1.000,50 (period thousands, comma decimals) ## The "Native Test" Before sending: would a Dutch person screenshot this as "AI-generated"? If yes—too formal, missing particles, not gezellig. Loosen up.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: codenova58/enterprise-wechat (version 0.1.0)

--- name: "WeChat Work" description: "提供企业内部通讯、私域流量管理及办公协同指引。" --- # 企业微信 ## 筛选项 - 类别/领域/功能（核心/进阶） - 场景/适用时段/设备要求（移动端/网页版/跨端） - 状态（实时/离线/授权/VIP特权/免费） ## 返回字段 - 操作摘要/交互流程/关键入口 - 功能亮点/提效工具/隐藏玩法/避坑指南 - 状态追踪/数据同步/消息提醒/安全策略 ## 示例请求 - “如何快速使用 企业微信 处理 [具体场景任务] ？” - “企业微信 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合产品迭代与社区反馈进行内容更新 ## 注意事项 - 保护个人隐私，谨慎进行敏感授权 - 部分功能涉及第三方接口，稳定性受合作方波动影响


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rylena/claw-mouse (version 0.1.0)

--- name: claw-mouse description: Control a Linux X11 desktop by taking screenshots and moving/clicking/typing via xdotool + scrot. homepage: https://github.com/rylena/claw-mouse metadata: openclaw: requires: bins: [python3, xdotool, scrot] --- # claw-mouse This skill provides a small, scriptable **desktop GUI control** helper for Linux **X11**. It’s intended for “vision loop” automation: 1) take a screenshot 2) decide where to click 3) move/click/type 4) repeat Under the hood it wraps: - `scrot` for screenshots - `xdotool` for mouse/keyboard/window control ## Files - `desktopctl.py` — the CLI script ## Requirements - Linux running **X11** (not Wayland-only) - `python3` - `xdotool` - `scrot` Ubuntu/Debian: ```bash sudo apt-get update sudo apt-get install -y xdotool scrot ``` ## Usage From this skill directory: ```bash python3 desktopctl.py screenshot python3 desktopctl.py click 500 300 python3 desktopctl.py type "hello" python3 desktopctl.py key ctrl+l python3 desktopctl.py windows python3 desktopctl.py activate "Chromium" ``` ### DISPLAY / XAUTHORITY If you’re running from a daemon/headless shell where `DISPLAY` isn’t set: ```bash DISPLAY=:0 XAUTHORITY=$HOME/.Xauthority python3 desktopctl.py screenshot ``` Or use flags: ```bash python3 desktopctl.py --display :0 --xauthority $HOME/.Xauthority screenshot ``` ## Safety notes This can click/type into your real desktop session. Use carefully. ## Changelog - 0.1.0: Initial published skill.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/flight (version 1.0.1)

--- name: Flight slug: flight version: 1.0.1 description: Search, compare, book, and manage flights with price tracking, multi-platform comparison, and loyalty optimization. changelog: "Preferences now persist across skill updates" metadata: {"clawdbot":{"emoji":"✈️","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Quick Reference | Topic | File | |-------|------| | Search, compare, flexible dates | `search.md` | | Booking, rebooking, disruptions | `booking.md` | | Price alerts, tracking, predictions | `tracking.md` | | Miles, points, status, awards | `points.md` | | APIs, integrations, data sources | `apis.md` | ## User Profile Preferences persist in `~/flight/memory.md`. Create on first use. ```markdown ## Home Airports <!-- Primary airports. Format: "IATA, IATA" --> <!-- Examples: MAD, BCN | JFK, EWR, LGA --> ## Preferred Airlines <!-- Airlines or alliances. Format: "airline | alliance" --> <!-- Examples: Iberia, BA | Star Alliance | any --> ## Elite Status <!-- Loyalty tiers. Format: "program: tier" --> <!-- Examples: BA Gold, United 1K, Marriott Platinum --> ## Budget Style <!-- budget | moderate | premium | business-class --> ## Travel Style <!-- solo | couple | family-with-kids | business-frequent --> ## Carry-On Only <!-- yes | no | prefer --> ``` *Empty sections = ask when relevant. Fill as you learn.* ## Data Storage Store flight data in ~/flights/: - searches — saved route searches with price history - bookings — active reservations with PNRs - alerts — price drop thresholds by route - history — past flights for loyalty tracking ## Core Rules - Always compare 3+ platforms before recommending (Skyscanner, Google Flights, Kiwi, direct airline) - Calculate TRUE total cost including baggage, seat selection, and fees — especially for budget carriers - Check visa requirements before booking international flights - For families: verify adjacent seat availability before booking, not after - Set price alerts on flexible dates (±3 days) for better deal coverage - Track flight status starting 24h before departure — proactive rebooking on delays - Never book non-refundable for trips >30 days out without asking - When comparing award bookings: calculate cents-per-point against cash price - Warn about tight connections (<90min domestic, <2h international) - For multi-city: check if separate bookings are cheaper than one ticket


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: heqq-github/custom-api-summary-xzk (version 1.0.0)

--- name: custom-api-summary version: 2.0.0 description: "调用自定义摘要 API，对用户提供的文本进行处理并返回结果" tags: ["API", "总结", "摘要", "文本处理"] tools: ["Bash"] --- # Custom API Summary Skill ## 功能 当用户明确要求对一段文本进行总结、提炼、归纳或处理时，调用 HTTP 接口，将用户输入作为 `content` 参数传入，并返回接口处理结果。 ## 适用场景 - 总结一段较长文本 - 提炼文章重点 - 对输入内容做统一的摘要处理 ## 触发条件 当用户表达以下意图时触发： - "帮我总结这段内容" - "提炼一下这段文字" - "把这段话做个摘要" 不应在普通闲聊、无明确文本处理需求时自动触发。 ## 输入 用户提供的原始文本内容。 ## 执行脚本 ```bash # API 接口地址 API_URL="https://test-gig-c-api.1haozc.com/api/wx/kjj/v1/customer/skill/call" # 用户输入的内容（自动获取） USER_TEXT="$1" # 检查输入是否为空 if [ -z "$USER_TEXT" ]; then echo "❌ 错误：请输入需要总结的文本内容" echo "用法：帮我总结这段内容：[你的文本]" exit 1 fi # 显示请求信息 echo "📝 正在处理你的请求..." echo "🔌 调用接口：$API_URL" echo "" # 核心：POST JSON 请求 curl -s -X POST "$API_URL" \ -H "Content-Type: application/json; charset=UTF-8" \ -d "{\"content\":\"$USER_TEXT\"}" echo "" ``` ## 返回格式 ```json { "success": true, "result": { "目标 API 返回的数据" } } ``` 失败时返回： ```json { "success": false, "message": "错误信息" } ``` ## 使用约束 - 必须传入非空 `content` - `content` 应为待处理的完整文本 - 如果后端接口不可用，应明确返回失败原因，而不是伪造处理结果 ## 说明 本 Skill 通过 Bash 脚本直接调用目标 API，无需中间服务。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: raghulpasupathi/age-verification (version 1.0.0)

# Age Verification Skills Skills for age verification and age-appropriate content filtering. ## Essential Skills ### 1. Age Guard **Skill ID**: `age-guard` **Purpose**: Verify user age and enforce age restrictions **Features**: - Document verification (ID, passport) - Face-based age estimation - Third-party verification (Yoti, Jumio) - Age gate UI components - Parental consent workflows **Installation**: ```bash npm install @clawhub/age-guard ``` **Methods**: 1. **Self-reported**: Honor system (basic) 2. **Document verification**: ID scanning 3. **Face estimation**: Computer vision 4. **Third-party**: Yoti, Jumio APIs 5. **Parental PIN**: For children **Use Cases**: - COPPA compliance (children < 13) - Age-restricted content - Parental control enforcement --- *For geo-blocking, see [GEO_BLOCKING.md](GEO_BLOCKING.md).*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1kalin/afrexai-kpi-tracker (version 1.0.0)

# KPI Tracker Skill Track, analyze, and report on Key Performance Indicators for any business. ## What It Does When activated, this skill helps you: - Define and categorize KPIs (revenue, ops, marketing, customer success) - Set targets and thresholds (green/yellow/red) - Generate weekly/monthly KPI reports in markdown - Flag KPIs that are off-track with root cause prompts - Store historical data in a simple JSON file for trend analysis ## Usage Tell your agent: "Track these KPIs" or "Give me a KPI report" or "Which metrics are off track?" ### Setup Create `kpi-config.json` in your workspace: ```json { "kpis": [ { "name": "Monthly Recurring Revenue", "category": "revenue", "unit": "$", "target": 50000, "redBelow": 35000, "yellowBelow": 45000 }, { "name": "Customer Churn Rate", "category": "customer", "unit": "%", "target": 3, "redAbove": 7, "yellowAbove": 5 } ] } ``` ### Recording Data Say: "Record MRR at $42,000 for this week" The agent stores entries in `kpi-data.json`: ```json { "entries": [ { "kpi": "Monthly Recurring Revenue", "value": 42000, "date": "2026-02-13", "note": "Post-launch week" } ] } ``` ### Reports Say: "KPI report" and the agent generates a formatted status board: ``` 📊 KPI Report — Week of Feb 10, 2026 🟢 Monthly Recurring Revenue: $48,200 (target: $50,000) — 96.4% 🔴 Customer Churn Rate: 8.1% (target: 3%) — needs attention 🟡 Lead Conversion Rate: 11% (target: 15%) — trending up from 9% ⚠️ Action needed on 1 red, 1 yellow KPI ``` ### Trend Analysis Say: "Show MRR trend" — the agent reads historical entries and summarizes direction, velocity, and whether you'll hit target at current pace. ## How the Agent Should Behave 1. Read `kpi-config.json` for KPI definitions 2. Read/write `kpi-data.json` for historical values 3. When asked for a report: calculate status for each KPI, format with color indicators 4. When a KPI is red: proactively suggest investigation areas 5. When recording: validate the value makes sense (e.g., churn can't be negative) ## File Locations - Config: `kpi-config.json` (workspace root or custom path) - Data: `kpi-data.json` (same directory as config) - Reports: generated on-demand, optionally saved to `reports/kpi-YYYY-MM-DD.md` ## Pro Tip Pair this with a cron job to generate weekly KPI reports automatically. For deeper business intelligence and pre-built industry KPI templates, check out [AfrexAI Context Packs](https://afrexai-cto.github.io/context-packs/) — drop-in configurations that include KPI frameworks for 10+ industries.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joifg/daily-report-writer (version 1.0.0)

--- name: daily-report-writer description: 根据输入生成日报 Markdown 草稿并写入 reports 目录 user-invocable: true metadata: {"openclaw":{"emoji":"📝"}} --- # Daily Report Writer ## Use when - 用户要求生成“日报/工作总结草稿” ## Inputs - date（必填，YYYY-MM-DD） - highlights（必填，数组） - blockers（可选，数组） ## Steps 1. 校验参数是否齐全。 2. 读取（或创建）`reports/{{date}}-daily-report.md`。 3. 按固定模板写入内容。 4. 返回 `status/summary/data/nextAction`。 ## Failure - 缺参数：明确指出缺哪一项，并给示例输入。 - 写文件失败：返回目录权限检查建议。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rishabh7464-hue/gst-upi-reconciliation-copilot-india (version 1.0.0)

--- name: gst-upi-reconciliation-copilot-india description: Reconcile Indian GST invoice data with UPI transaction statements and produce audit-ready matched/unmatched reports. Use when the user asks to reconcile GST vs UPI collections, find missing payments, detect unmatched invoices, prepare month-end books, or investigate cashflow gaps from CSV exports (Tally/Zoho/ERP + bank/payment app exports). --- # GST + UPI Reconciliation Copilot (India) Perform deterministic reconciliation between GST invoice CSV data and UPI transaction CSV data. Generate four outputs: reconciled rows, GST-unmatched rows, UPI-unmatched rows, and a summary JSON. ## Quick workflow 1. Confirm both input files are CSV and represent: - GST invoices/sales register - UPI collections/statement 2. Validate required intent-level fields exist (invoice id/date/total, txn date/amount/status). 3. Run: ```bash python3 scripts/reconcile_gst_upi.py \ --gst-csv /path/gst.csv \ --upi-csv /path/upi.csv \ --output-prefix /path/out/recon_2026_03 \ --date-window-days 7 ``` 4. Read and report key metrics from `*_summary.json`: - matched rows - unmatched GST rows - unmatched UPI rows - reconciliation coverage % 5. Provide next actions for unmatched rows (follow-up / corrections / data cleanup). ## Matching policy - Match only UPI rows with success-like status: `success`, `completed`, `captured`, `paid`. - Require amount match (±0.01 tolerance). - Enforce date window (default 7 days). - Boost confidence if invoice number or customer tokens appear in UPI note/txn_id/UTR. - Ensure one UPI transaction maps to one invoice only. ## Edge-case handling - Ignore failed/pending/reversed UPI statuses for settlement matching. - Preserve GST rows with empty/invalid dates as unmatched (do not force guesswork). - Preserve UPI rows with missing amount as unmatched. - Support flexible date formats in both files. - Handle currency symbols and commas in amount fields. ## Required outputs to share with user Always return: 1. Reconciliation snapshot: - matched rows / total GST rows - matched amount / total GST amount 2. File paths generated: - `*_reconciled.csv` - `*_gst_unmatched.csv` - `*_upi_unmatched.csv` - `*_summary.json` 3. Priority action items: - high-value unmatched GST invoices - suspicious UPI rows (success + high amount + no invoice) ## References - Read `references/csv-schemas.md` for accepted columns and alias mapping.


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