OpenClaw/clawhub-security-signals huggingface dataset:
Skill: merjua14/client-onboarding-automator (version 1.0.0)

# Client Onboarding Automator Skill Automate the entire client onboarding workflow — from initial inquiry to project kickoff. Handles intake forms, contract generation, payment collection, welcome sequences, and project setup. ## What This Skill Does 1. **Intake Processing** — Captures new client info from email/form submissions 2. **Contract Generation** — Creates service agreements from templates 3. **Payment Collection** — Sends Stripe payment links automatically 4. **Welcome Sequence** — Sends onboarding email series (day 0, 1, 3, 7) 5. **Project Setup** — Creates folders, tasks, and checklists for new clients 6. **CRM Update** — Logs client details in your tracking system ## Trigger When a new inquiry arrives (email matching pattern, form webhook, or manual trigger): ``` New client: [name] | [email] | [service] | [budget] ``` ## Workflow ### Step 1: Intake Parse the incoming inquiry and extract: - Client name and email - Service requested - Budget/package selected - Timeline expectations - Special requirements ### Step 2: Auto-Response (Immediate) Send a personalized acknowledgment email: ``` Subject: Thanks for reaching out, [Name]! Here's what happens next Hi [Name], Thanks for your interest in [service]. I've received your inquiry and here's what to expect: 1. I'll review your requirements (within 2 hours) 2. You'll receive a proposal with pricing 3. Once approved, we kick off immediately In the meantime, here's a quick questionnaire to help me prepare: [Link to intake form] Best, [Your name] ``` ### Step 3: Proposal Generation Create a proposal document with: - Scope of work (based on service selected) - Pricing (from your rate card) - Timeline - Terms ### Step 4: Contract + Payment Once proposal is accepted: - Generate contract from template - Create Stripe payment link for the agreed amount - Send contract + payment link to client ### Step 5: Welcome Sequence After payment: - **Day 0:** Welcome email + access credentials + kickoff questionnaire - **Day 1:** "Getting started" guide + calendar link for kickoff call - **Day 3:** Check-in + first deliverable preview - **Day 7:** Progress update + feedback request ### Step 6: Project Setup - Create project folder in workspace - Generate task checklist from service template - Set up recurring check-in reminders ## Configuration ```json { "business_name": "Your Business Name", "from_email": "[REDACTED_SECRET]", "reply_to": "[REDACTED_SECRET]", "stripe_key": "sk_live_...", "services": { "basic": { "name": "Basic Package", "price": 497, "description": "..." }, "pro": { "name": "Pro Package", "price": 997, "description": "..." }, "enterprise": { "name": "Enterprise", "price": 2497, "description": "..." } }, "welcome_sequence_delays": [0, 1, 3, 7] } ``` ## Requirements - Resend, SendGrid, or SMTP for email - Stripe for payments - OpenClaw with email/webhook channel configured


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fabglitch/admin-cli (version 1.0.0)

# admin-cli ## Description Provides a simple command-line interface for performing administrative tasks that require elevated privileges. ### Commands - `update-system`: Run package manager update (`pacman -Syu`). - `restart-service <service>`: Restart a systemd service. - `check-status`: Show OS version and current uptime. > **Note:** These commands use the `elevated` flag. Ensure the agent has the necessary permissions in its configuration.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/gws-docs-write (version 1.0.12)

--- name: gws-docs-write description: "Google Docs: Append text to a document." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws docs +write --help" --- # docs +write > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. Append text to a document ## Usage ```bash gws docs +write --document <ID> --text <TEXT> ``` ## Flags | Flag | Required | Default | Description | |------|----------|---------|-------------| | `--document` | ✓ | — | Document ID | | `--text` | ✓ | — | Text to append (plain text) | ## Examples ```bash gws docs +write --document DOC_ID --text 'Hello, world!' ``` ## Tips - Text is inserted at the end of the document body. - For rich formatting, use the raw batchUpdate API instead. > [!CAUTION] > This is a **write** command — confirm with the user before executing. ## See Also - [gws-shared](../gws-shared/SKILL.md) — Global flags and auth - [gws-docs](../gws-docs/SKILL.md) — All read and write google docs commands


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 156554395/zhipu-free-image-video (version 1.1.0)

--- name: zhipu-free-image-video description: 智谱免费图片与视频生成技能。适用于用户想用智谱生成图片、批量出图、生成短视频、查询视频任务结果、等待视频完成，或优先使用免费/低成本模型快速产出创意内容时。 metadata: { "openclaw": { "requires": { "bins": ["node", "npm"] } } } --- # 智谱免费图片与视频生成 把智谱的图片生成和视频生成能力整理成一套适合 OpenClaw 直接调用的创作工作流，重点突出免费或低成本模型的可用性。 适合这些任务： - 根据一句话快速生成图片 - 一次性批量生成多张不同风格或不同主题的图片 - 根据提示词生成短视频 - 查询视频生成进度并等待任务完成 - 在创意验证阶段优先使用免费模型，降低试错成本 ## 适用场景 当用户出现这些意图时启用： - “帮我生成一张图” - “批量出几版海报 / 封面 / 配图” - “用智谱免费模型先做几张看看” - “生成一个短视频” - “查一下这个视频任务好了没” - “等视频生成完成再告诉我” ## 核心定位 这个技能主打两件事： - 智谱图片生成 - 智谱视频生成 默认优先强调免费或低成本模型： - 图片优先：`cogview-3-flash` - 视频优先：`cogvideox-flash` 如果用户明确追求更高质量，再切到更高规格模型。 ## 脚本资源 优先使用 `scripts/` 里的可执行脚本来完成图片与视频任务。 可直接复用的脚本： - `scripts/generate_image.js` - 生成单张图片 - `scripts/batch_generate_images.js` - 批量生成图片 - `scripts/generate_video.js` - 提交视频生成任务 - `scripts/query_video_result.js` - 查询视频任务结果 - `scripts/wait_for_video.js` - 等待视频生成完成 - `scripts/configure_models.js` - 校验任务希望使用的默认图片/视频模型 调用方式统一为： ```bash node projects/skills/zhipu-free-image-video/scripts/<script>.js '<json>' ``` 环境配置默认读取： - `IMAGE_VIDEO_GENERATION_API_KEY` - 或 `ZHIPU_API_KEY` ## 默认执行策略 ### 1. 先确认目标产物 先判断用户到底要的是： - 单张图片 - 批量图片 - 视频 - 已提交视频任务的进度或结果 ### 2. 默认优先免费模型 如果用户没特别指定： - 图片默认用免费或低成本模型快速出图 - 视频默认用免费模型快速出结果 这样更适合做灵感探索、风格试错和第一版草稿。 ### 3. 批量任务优先控制节奏 批量出图时要注意： - 分批处理 - 控制并发 - 汇总成功和失败结果 - 避免一次性堆太多请求 ### 4. 视频任务默认按异步流程处理 视频生成通常不是即时返回最终结果，因此要按两段处理： - 先提交任务，拿到任务 ID - 再查询状态或等待完成 ## 常用工作流 ### 生成单张图片 适用：海报、封面、配图、头像、概念图。 默认做法： - 先润色提示词，必要时补主体、风格、镜头、光线、背景 - 优先走免费图像模型 - 返回图片地址、提示词和模型信息 ### 批量生成图片 适用：一次要多版候选图。 建议做法： - 把多条提示词拆成批次 - 明确每一张图的主题差异 - 最终按“提示词 - 结果”方式汇总 适合场景： - 多版封面 - 多个 IP 角色草图 - 多张文章配图 - 批量创意探索 ### 生成视频 适用：短视频创意、动态概念演示、简单分镜验证。 建议做法： - 提示词尽量包含主体、动作、场景、镜头感 - 默认优先快速模型 - 先返回任务提交结果，再继续查最终结果 ### 查询视频结果 适用：用户给了任务 ID，让你看看视频好了没有。 返回时优先说明： - 当前状态 - 是否完成 - 如果完成，给出视频地址 - 如果失败，给出失败原因或建议重试 ### 等待视频完成 适用：用户希望“你等它出完再告诉我”。 处理方式： - 设置合理的最大等待时间 - 定期查询状态 - 完成后返回最终结果 - 超时则明确告诉用户仍在处理中 ## 质量与成本取舍 默认原则： - 先免费，再高配 - 先快速验证，再精修 - 先出结果，再做多轮迭代 当用户说“先随便来几版看看”“先用免费的”“先低成本试试”时，优先免费模型。 当用户说“质量更高一点”“商业图”“正式发布素材”时，再考虑切换到更高质量模型。 ## 风险与边界 - 不要承诺绝对免费永久可用，应该表述为优先使用免费或低成本模型 - 不泄露 API Key 或账户配置 - 批量生成时注意请求规模，避免过度并发 - 如果生成结果涉及敏感、违规或明显侵权内容，要及时收敛 - 视频生成耗时可能较长，要提前告知用户这是异步任务 ## 故障排查 常见问题与处理： - 生成失败：检查账号额度、模型可用性、提示词是否异常 - 视频一直未完成：延长等待时间，或改为稍后查询 - 批量任务部分失败：保留成功结果，单独重试失败项 - 结果不理想：优化提示词，增加风格、镜头、材质、动作细节 - 免费模型效果不够：明确告知用户可以切换更高质量模型


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lws803/nom-feed (version 1.0.0)

--- description: "Fetch recent GitHub activity from the Nom feed" argument-hint: "[org/repo] [--search QUERY] [--type TYPE] [--org ORG] [--from DATE] [--to DATE] [--limit N] [--rss]" allowed-tools: ["Bash(curl:*)"] --- Fetch GitHub activity from Nom (beta.nomit.dev) and present it clearly. Base URL: `https://beta.nomit.dev` $ARGUMENTS parsing rules: - If the first argument looks like `org/repo` (contains `/`), use the repo feed at `/api/feed/{org}/{repo}` - Otherwise use the global feed at `/api/feed` - `--search TEXT` — free-text search (full-text on title/summary) - `--type TYPE` — filter by event type: `pull_request`, `issue`, `release`, `push` - `--org ORG` — filter by GitHub org (global feed only) - `--from DATE` / `--to DATE` — date range (ISO 8601, e.g. 2026-01-01) (global feed only) - `--limit N` — results to return (default 20, max 100) - `--rss` — fetch RSS XML instead of JSON (repo feed: `/api/feed/{org}/{repo}/rss`; global: `/api/feed/rss`) Build `q` for global feed by joining filters: e.g. `type:pull_request org:vercel from:2026-01-01` plus any `--search` text. API endpoints (JSON): - Global feed: `GET /api/feed` - Repo feed: `GET /api/feed/{org}/{repo}` RSS endpoints (if `--rss`): - Global: `GET /api/feed/rss` - Repo: `GET /api/feed/{org}/{repo}/rss` Use curl to fetch the response. For JSON, present results as a clean readable summary. For each item show: - Event type label (PR / Issue / Release / Push) - Title as a markdown link to the URL - One-line AI summary - Author and timestamp (relative if possible) Response shape: `{ items: [...], pagination: { offset, limit, has_more } }`. Each item has `id`, `type`, `org`, `repo`, `title`, `summary`, `url`, `author`, `contributors`, `updated_at`. Example output format: ``` **PR** [Add turbo support](https://github.com/vercel/next.js/pull/123) timneutkens · 2 hours ago Adds experimental Turbo support to the build pipeline, cutting build times by ~40%. **Release** [v14.2.0](https://github.com/vercel/next.js/releases/tag/v14.2.0) vercel-release-bot · 1 day ago Major release introducing partial pre-rendering and improved image optimisation. ```


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

--- name: Pricing Optimizer description: Analyzes and optimizes pricing strategy using proven frameworks --- # Pricing Optimizer You optimize pricing strategy like a pricing consultant. Data-driven, psychology-informed, revenue-maximizing. ## Process ### 1. Discovery Ask about: - Current pricing (tiers, amounts, billing frequency) - Target customer (B2B/B2C, segment, budget range) - Competitors and their pricing - Current conversion rates and churn - Cost structure (COGS, CAC, margins) - Value metrics (what drives customer value?) ### 2. Analysis Frameworks **Value-Based Pricing:** - What's the customer's next best alternative? - What's the economic value your product creates? - Price should be between cost and value created **Competitive Positioning:** - Map competitors on price vs. feature matrix - Identify pricing gaps and opportunities - Determine if you're premium, mid-market, or budget **Psychology:** - Anchoring (show expensive tier first) - Charm pricing ($47 vs $50) - Decoy effect (3-tier with obvious "best value") - Annual discount (lock-in + cash flow) ### 3. Output ``` ## Pricing Analysis: [Product] ### Current State - Revenue: ... - Conversion: ... - ARPU: ... ### Recommended Pricing | Tier | Price | Target | Key Features | |------|-------|--------|-------------| | ... | ... | ... | ... | ### Expected Impact - Revenue change: +X% - Conversion change: ... - ARPU change: ... ### Implementation Plan 1. ... ### A/B Test Suggestions - ... ``` ## Rules - Always consider willingness-to-pay, not just cost-plus - Recommend A/B testing before full rollout - Consider annual vs monthly trade-offs - Flag if current pricing leaves money on the table ## Related Tools - Revenue calculator: https://afrexai-cto.github.io/ai-revenue-calculator/ - Lead scoring: `clawhub install afrexai-lead-scorer` - Industry context: https://afrexai-cto.github.io/context-packs/ ($47/pack)


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

--- name: xigua-video-hot-trend description: 注册“西瓜视频”技能；用于公开视频与短剧页信息整理与表现摘要。 homepage: `https://www.ixigua.com/` metadata: {"clawdbot":{"emoji":"🎬"}} --- # 西瓜视频 用途与边界 - 面向公开视频/短剧/话题页的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.ixigua.com/ - 短剧/视频频道：站点入口 - 搜索入口：https://www.ixigua.com/search 常见任务 - 指定话题/关键词的视频集合摘要（点赞/评论/收藏） - 剧集页面目录与更新状态、期次简介 - 榜单条目分布与题材统计 数据字段 - 视频标题、作者、时长、发布时间、点赞/评论/收藏、链接 - 短剧标题、更新状态、期次目录、简介、链接 - 榜单名称、采集时间、来源链接 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 关键词摘要：输入关键词 → 搜索 → 抽取条目 → 输出表现摘要 - 短剧目录：访问短剧页 → 抽取期次 → 输出更新状态与简介 - 榜单统计：访问榜单 → 抽取条目 → 题材分布统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lalawgwg99/daily-observatory-lite (version 1.0.0)

--- name: daily-observatory-lite description: Daily automated health check + mood tracking + proactive alerts for OpenClaw agents version: 1.0.0 metadata: openclaw: emoji: "🔭" homepage: https://github.com/lalawgwg99/daily-observatory-lite license: MIT-0 --- # Daily-Observatory Lite 🔭 ## 概述 Daily-Observatory Lite 是一個**無感自動**的每日觀察站，幫你掌握 OpenClaw agent 的整體狀態： - **系統健康**：Gateway、記憶結構、技能狀態 - **情緒溫度**：從 EMOJI-JOURNAL.md 分析趨勢 - **待辦預警**：P0-P1 卡住任務主動提醒 - **主動推播**：每天 08:00 / 23:00 自動推送到 Telegram ## 核心價值 **不是記錄，是預警** **不是單日，是趨勢** **不是被動，是主動** ## 安裝 ```bash clawhub install daily-observatory-lite ``` ## 使用方式 ### 自動執行（推薦） skill 會自動在設定時間執行，並推送到 Telegram： ```bash # 修正 crontab（如果沒有自動設定） crontab -e # 加入： # 0 8 * * * openclaw agent --agent daily-observatory-lite --message "run" --deliver # 0 23 * * * openclaw agent --agent daily-observatory-lite --message "run" --deliver ``` ### 手動執行 ```bash openclaw agent --agent daily-observatory-lite --message "run" --deliver ``` ## 輸出範例 ### 🌅 早上 08:00 日誌 ``` 🔭 Daily-Observatory Lite · 2026-03-25 08:00 ✅ 系統健康 ├─ Gateway: 正常 (v1.2.3) ├─ 記憶結構: 完整 └─ 技能狀態: 24 個正常 😐 情緒溫度 ├─ 今日: 0.65 (😌) ├─ 昨日: 0.45 (😐) └─ 趨勢: ↗ +0.20 ⚠️ 待辦預警 ├─ P0 卡住: 1 個 (重構 authentication · 5 天) └─ P1 卡住: 2 個 💡 建議: 注意 P0 阻塞任務 ``` ### 🌙 晚上 23:00 日誌 ``` 🔭 Daily-Observatory Lite · 2026-03-25 23:00 ✅ 系統健康 ├─ Gateway: 正常 (v1.2.3) ├─ 記憶結構: 完整 └─ 技能狀態: 24 個正常 😌 情緒溫度 ├─ 今日: 0.60 (穩定) └─ 趨勢: → 平穩 ⚠️ 待辦預警 ├─ P0 卡住: 0 個 └─ P1 卡住: 1 個 💡 建議: 今日完成率 80%，保持節奏 ``` ## 設定 ### 檔案：`daily-observatory-lite/config.json` ```json { "schedule": { "morning": "08:00", "evening": "23:00" }, "channels": { "telegram": true, "telegram_chat_id": "YOUR_CHAT_ID" }, "modules": { "system_health": true, "emotion_tracking": true, "todo_alerts": true } } ``` ## 差異化亮點 | 現有 skill | Daily-Observatory Lite | |------------|----------------------| | 只記錄 | **記錄 + 預警** | | 被動查詢 | **主動推播** | | 單日視角 | **趨勢分析** | | 沒系統檢測 | **三合一健康檢查** | ## 授權 MIT-0 ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nikkijasmine/job-search-mcp-jobspy (version 1.0.3)

# Job Search MCP Skill This skill enables AI agents to search for job listings across multiple job boards using the JobSpy MCP server. JobSpy aggregates listings from sources such as LinkedIn, Indeed, Glassdoor, ZipRecruiter, Google Jobs, and more into a unified interface. ## When to Use This Skill Use this skill when you want to: - Find job listings matching specific criteria (role, location, company) - Search for remote or on-site positions - Compare opportunities across job boards - Retrieve salary information when available - Find recently posted jobs - Filter for “Easy Apply” roles ## Prerequisites - Python 3.10+ - JobSpy MCP server installed and configured ## Installation & Setup ### macOS Setup (Homebrew Python) Homebrew-managed Python is externally managed (PEP 668). Create and activate a virtual environment before installing dependencies: ```bash python3 -m venv .venv source .venv/bin/activate pip install mcp python-jobspy pandas pydantic ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jisuapi/barcode (version 1.0.10)

--- name: "Barcode Generation And Recognition - 条码生成识别" description: 按条形码查询商品名称、品牌、规格与产地等。当用户说：扫这个条码是什么商品、690 开头的条码查一下信息，或类似商品条码查询时，使用本技能。 metadata: { "openclaw": { "emoji": "🏷️", "requires": { "bins": ["python3"], "env": ["JISU_API_KEY"] }, "primaryEnv": "JISU_API_KEY" } } --- # 极速数据商品条码查询（Barcode2） > 数据由 **[极速数据（JisuAPI）](https://www.jisuapi.com/)** 提供 — 国内专业的 API 数据服务平台，提供生活常用、交通出行、工具万能等数据接口。 通过商品条形码（EAN8/EAN13，69 开头）查询商品基础信息。 ## 前置配置：获取 API Key 1. 前往 [极速数据官网](https://www.jisuapi.com/) 注册账号 2. 进入 [商品条码查询 API](https://www.jisuapi.com/api/barcode2/) 页面，点击「申请数据」 3. 在会员中心获取 **AppKey** 4. 配置 Key： ```bash # Linux / macOS export JISU_API_KEY="your_appkey_here" # Windows PowerShell $env:JISU_API_KEY="your_appkey_here" ``` ## 脚本路径 脚本文件：`skills/barcode/barcode.py` ## 使用方式 ### 按条形码查询商品信息 ```bash python3 skills/barcode/barcode.py '{"barcode":"06917878036526"}' ``` 请求参数以 JSON 形式传入，仅需一个字段： ```json { "barcode": "06917878036526" } ``` ## 请求参数 | 字段名 | 类型 | 必填 | 说明 | |---------|--------|------|---------| | barcode | string | 是 | 商品条码 | ## 返回结果示例 脚本直接输出接口 `result` 字段，典型结构与官网示例一致（简化版）： ```json { "barcode": "06917878036526", "name": "雀巢咖啡臻享白咖啡", "ename": "NESCAFE White Coffee", "unspsc": "50201708 (食品、饮料和烟草>>饮料>>咖啡和茶>>咖啡饮料)", "brand": "NESCAFE", "type": "29g", "width": "70毫米", "height": "160毫米", "depth": "55毫米", "origincountry": "中国", "originplace": "", "assemblycountry": "中国", "barcodetype": "", "catena": ",", "isbasicunit": "0", "packagetype": "", "grossweight": "", "netcontent": "5条", "netweight": "145克", "description": "", "keyword": "雀巢咖啡臻享白咖啡", "pic": "", "price": "", "licensenum": "QS3117 0601 0440", "healthpermitnum": "" } ``` 当出现错误（如条码不正确或无数据）时，脚本会输出： ```json { "error": "api_error", "code": 202, "message": "条码不正确" } ``` ## 常见错误码 基于 [极速数据条码文档](https://www.jisuapi.com/api/barcode2/)： | 代号 | 说明 | |------|------------------------------------| | 201 | 条码为空 | | 202 | 条码不正确 | | 203 | 该条码已下市（扣次数） | | 204 | 该条码已注册，但编码信息未通报（扣次） | | 205 | 该条码异常（扣次数） | | 210 | 没有信息 | 系统错误码： | 代号 | 说明 | |------|----------------------| | 101 | APPKEY 为空或不存在 | | 102 | APPKEY 已过期 | | 103 | APPKEY 无请求权限 | | 104 | 请求超过次数限制 | | 105 | IP 被禁止 | ## 推荐用法 1. 用户拍照/输入条码号：「帮我查一下条码 `06917878036526` 是什么商品」。 2. 代理构造 JSON：`{"barcode":"06917878036526"}` 并调用： `python3 skills/barcode/barcode.py '{"barcode":"06917878036526"}'` 3. 从结果中读取 `name/brand/type/netcontent/netweight/origincountry` 等字段，为用户总结商品名称、品牌、规格和产地信息。 ## 关于极速数据 **极速数据（JisuAPI，[jisuapi.com](https://www.jisuapi.com/)）** 是国内专业的 **API数据服务平台** 之一，提供以下API： - **生活常用**：IP查询，快递查询，短信，全国天气预报，万年历，空气质量指数，彩票开奖，菜谱大全，药品信息 - **工具万能**：手机号码归属地，身份证号码归属地查询，NBA赛事数据，邮编查询，WHOIS查询，识图工具，二维码生成识别，手机空号检测 - **交通出行**：VIN车辆识别代码查询，今日油价，车辆尾号限行，火车查询，长途汽车，车型大全，加油站查询，车型保养套餐查询 - **图像识别**：身份证识别，驾驶证识别，车牌识别，行驶证识别，银行卡识别，通用文字识别，营业执照识别，VIN识别 - **娱乐购物**：商品条码查询，条码生成识别，电影影讯，微博百度热搜榜单，新闻，脑筋急转弯，歇后语，绕口令 - **位置服务**：基站查询，经纬度地址转换，坐标系转换 在官网注册后，按**具体 API 页面**申请数据，在会员中心获取 **AppKey** 进行接入；**免费额度和套餐**在API详情页查看，适合个人开发者与企业进行接入。在 **ClawHub** 上也可搜索 **`jisuapi`** 找到更多基于极速数据的 OpenClaw 技能。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cwheeler67/team-role-launchkit (version 1.0.0)

--- name: team-role-launchkit description: Launch clone-like multi-agent workflows using explicit role prompts (researcher, builder, editor), strict handoff contracts, and shared behavior files for consistent long-running execution. --- # Team Role Launchkit Use this skill when a single task needs parallelization or role separation without losing coherence. ## What this skill provides - 3 role modes: **Researcher**, **Builder**, **Editor** - A required **handoff contract** to prevent role blur - Launch prompts to start role sessions quickly - Guardrails for memory hygiene and approval boundaries ## When to use Use for: - complex, multi-step tasks - tasks crossing research + implementation + communication - long-running tasks where consistency matters Avoid for: - tiny one-shot requests - deterministic extraction tasks ## Role definitions - **Researcher**: gather facts/options/constraints; no final outward messaging - **Builder**: execute implementation and report exact outputs/diffs - **Editor**: refine final user-facing output without changing core facts ## Orchestration sequence 1. Define objective and constraints. 2. Fill handoff contract from `references/handoff-contract.md`. 3. Run roles in order: Researcher → Builder → Editor. 4. Operator reviews and delivers final result. 5. Capture durable outcomes in memory. ## Non-negotiables - Every role handoff must include done criteria. - Receiver must restate scope before acting. - External/public actions still require explicit approval policy. - Temporary notes are not durable memory unless promoted. ## Output contract Return: - final deliverable - what changed by role - unresolved risks/questions - next best action


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

--- name: ai-intelligent-api-management version: 1.0.1 description: API 管理，接口设计 + 文档 + 测试。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能 API 管理系统 ## 描述 API 管理，接口设计 + 文档 + 测试。 ## 功能 - API 设计（可视化设计） - 文档生成（自动生成） - 接口测试（自动化测试） - 版本管理（版本控制） - 性能监控（API监控） ## 定价 - 基础版：¥99/月（100 API） - 专业版：¥499/月（1000 API） - 企业版：¥1999/月（无限 API） ## 适用场景 - API 开发 - 接口管理 - 文档生成 - 接口测试 ## 技术栈 - Python + FastAPI - OpenAPI/Swagger - 接口测试 - 性能监控 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-api-management cd ai-intelligent-api-management pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team

path: skill.json
content: {
  "name": "ai-intelligent-api-management",
  "version": "1.0.0",
  "description": "AI intelligent ai-intelligent-api-management",
  "author": "yang1002378395-cmyk",
  "tags": ["ai", "intelligent", "automation"]
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cgxxxxxxxxxxxx/cn-financial-notes-extraction (version 1.0.0)

--- name: cn-financial-notes-extraction version: 1.0.0 description: 提取中国 A 股上市公司年报 (PDF) 中的财务报表附注明细。适用于获取主表无法体现的深层数据（如 CapEx 明细、研发费用细分、应收账款账龄、关联方交易等）。 category: data-science --- ## 核心能力 从巨潮资讯 (CNINFO) 下载的年报 PDF 中，精准定位并提取**财务报表附注**中的表格数据。 ## 适用场景 - **CapEx 分析**: 提取“在建工程”、“固定资产”、“无形资产”等附注中的本期增加/减少明细（MD&A 口径 vs 现金流表口径）。 - **风险排查**: 提取应收账款账龄、坏账准备计提比例、商誉减值明细。 - **关联交易**: 提取关联方往来余额、购销金额。 - **研发细分**: 提取研发费用资本化/费用化明细。 ## 工作流程 (Workflow) 1. **下载**: 通过 `CNInfo API Scraper` 或 `East Money Announcement Downloader` 下载最新年报 PDF。 2. **定位**: 使用 `pdfplumber` (系统 Python 环境) 打开 PDF，全文检索关键字 `财务报表附注`。 3. **提取**: - 从定位页开始，逐页扫描。 - 提取表格 (`extract_tables()`)。 - **智能过滤**: 根据表头关键词（如 `项目`, `期末余额`, `本期增加`, `本期减少`, `账面余额`）筛选有效表格。 - 忽略纯文本页或无意义的排版表。 4. **结构化**: 将提取的数据转换为 `Dict[List]` 或 `DataFrame` 格式输出。 ## 关键参数与代码逻辑 ```python import pdfplumber def extract_notes(pdf_path, keywords=None): found_data = [] with pdfplumber.open(pdf_path) as pdf: # 1. 定位附注起始页 start_idx = 0 for i, page in enumerate(pdf.pages): text = page.extract_text() if text and "财务报表附注" in text: start_idx = i break # 2. 扫描表格 for i in range(start_idx, len(pdf.pages)): page = pdf.pages[i] tables = page.extract_tables() for table in tables: # 过滤空表或短表 if len(table) > 3 and any(row[0] for row in table if row): # 可选：如果指定了 keywords，检查表头是否匹配 if keywords: headers = " ".join([str(c) for c in table[0] if c]) if any(kw in headers for kw in keywords): found_data.append({"page": i+1, "table": table}) else: found_data.append({"page": i+1, "table": table}) return found_data ``` ## 注意事项 - **环境依赖**: 使用宿主机的 `python3` 和 `pdfplumber`，不要在沙箱 (subagent) 中直接运行，除非确认安装了库。 - **表格合并**: 跨页表格可能被拆分成两个，需逻辑合并（通过检查表头连续性）。 - **非标准排版**: 极少数老旧年报可能是扫描版，需 OCR（如 MinerU 或 Tesseract），但目前 A 股年报大多为原生 PDF，`pdfplumber` 效果最佳。


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