OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mirni/greenhelix-bundle-mcp-development-kit (version 1.3.1)

--- name: greenhelix-bundle-mcp-development-kit version: "1.3.1" description: "Everything you need to build and publish MCP servers. Includes the MCP Server Development guide, agent commerce discovery patterns, and protocol interoperability bridge. Plus server.json and smithery.yaml templates for registry submission. 3 guides + code templates." license: MIT compatibility: [openclaw] author: felix-agent type: bundle includes: [mcp-server-development, agent-commerce-discovery, agent-interoperability-bridge] price_usd: 79.0 tags: [bundle, code, mcp, server-development, starter-kit, guide, greenhelix, openclaw, ai-agent] executable: false install: none credentials: [STRIPE_API_KEY, GREENHELIX_API_KEY, AGENT_SIGNING_KEY, WALLET_ADDRESS] metadata: openclaw: requires: env: - STRIPE_API_KEY - GREENHELIX_API_KEY - AGENT_SIGNING_KEY - WALLET_ADDRESS primaryEnv: STRIPE_API_KEY --- # MCP Development Kit: Guide + Server Templates + Registry Configs ## Included Guides | Guide | Individual Price | |-------|-----------------| | MCP Server Development & Monetization Guide: Build, Publish, and Profit from the Tool Integration Standard | $49.00 | | Agent Commerce Discovery | $0.00 | | The Agent Interoperability Bridge: Connecting GreenHelix Agents to x402, ACP, A2A, MCP, Visa TAP, Google AP2/UCP, PayPal Agent Ready, and OpenAI ACP Ecosystems | $0.00 | ## Total Value: $49.00 | Bundle Price: $79.00


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abhibavishi/azure-image-gen (version 1.0.0)

--- name: azure-image-gen description: Generate images using Azure OpenAI DALL-E. Supports batch generation, custom prompts, and outputs a gallery. metadata: author: kai version: "1.0.0" tags: - image generation - azure - dall-e - ai art --- # Azure Image Gen Generate images using Azure OpenAI's DALL-E deployment. ## Setup ### Required Environment Variables ```bash export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com" export AZURE_OPENAI_API_KEY="your-api-key" export AZURE_OPENAI_DALLE_DEPLOYMENT="your-dalle-deployment-name" ``` Or create a `.env` file in the skill directory. ### API Version Default: `2024-02-01` (supports DALL-E 3) ## Usage ### Basic Generation ```bash python3 /Users/abhi/clawd/skills/azure-image-gen/scripts/generate.py --prompt "A futuristic city at sunset" ``` ### Multiple Images ```bash python3 /Users/abhi/clawd/skills/azure-image-gen/scripts/generate.py \ --prompt "Professional blog header for a tech startup" \ --count 4 ``` ### Custom Size & Quality ```bash python3 /Users/abhi/clawd/skills/azure-image-gen/scripts/generate.py \ --prompt "Minimalist illustration of cloud computing" \ --size 1792x1024 \ --quality hd ``` ### Specify Output Directory ```bash python3 /Users/abhi/clawd/skills/azure-image-gen/scripts/generate.py \ --prompt "Abstract data visualization" \ --out-dir ./blog-images ``` ## Options | Flag | Default | Description | |------|---------|-------------| | `--prompt` | Required | Image description | | `--count` | 1 | Number of images to generate | | `--size` | 1024x1024 | Image size: `1024x1024`, `1792x1024`, `1024x1792` | | `--quality` | standard | Quality: `standard` or `hd` | | `--style` | vivid | Style: `vivid` or `natural` | | `--out-dir` | ./azure-images | Output directory | | `--api-version` | 2024-02-01 | Azure OpenAI API version | ## Output - PNG images saved to output directory - `manifest.json` with prompt-to-file mapping - `index.html` gallery for easy preview ## Blog Image Prompts For blog headers, try prompts like: ``` # Tech/SaaS "Minimalist isometric illustration of cloud migration, blue and white color scheme, clean lines, professional" # Comparison posts "Split screen illustration showing old vs new technology, warm vs cool colors, modern flat design" # How-to guides "Clean illustration of a step-by-step process, numbered steps floating in space, soft gradients" # Cost/pricing "Abstract visualization of savings and growth, upward arrows, green accents, professional business style" ``` ## Troubleshooting **401 Unauthorized**: Check your `AZURE_OPENAI_API_KEY` **404 Not Found**: Verify your `AZURE_OPENAI_DALLE_DEPLOYMENT` name matches exactly **Content Policy**: Azure has strict content filters. Rephrase prompts that get blocked.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: sonim1/hauscout (version 1.0.0)

--- name: hauscout description: Hauscout 부동산 데이터 수집/분석 파이프라인. HouseSigma에서 매물을 자동 수집하고 AI 분석 후 Neon PostgreSQL에 저장. '매물 수집', '부동산 데이터 수집', 'hauscout', '수집 실행', '부동산 크롤링' 요청 시 사용. --- # Hauscout 데이터 수집 ## 프로젝트 위치 [REDACTED_SECRET] ## 수집 실행 ```bash cd /Users/kendrick/projects/hauscout npx tsx scripts/collect.ts ``` ### CLI 옵션 - `--url <url>` — 특정 매물 1개만 수집 - `--profile <id>` — 특정 검색 프로필만 - `--headed` — 브라우저 표시 (디버깅용) - `--dry-run` — AI 분석만, DB 저장 안 함 ## 수집 파이프라인 요약 1. Playwright로 HouseSigma 맵 검색 → 매물 목록 2. 가격/침실/타입 코드 필터링 3. 상세 페이지 방문 → DOM 텍스트 추출 (Comparables 섹션 제외) 4. GPT-4o-mini AI 분석 → 구조화 데이터 + 가성비 분석 5. Neon PostgreSQL 저장 (신규/업데이트/가격변동 추적) ## 수집 후 체크리스트 1. 에러 없이 완료됐는지 확인 (exit code, 에러 카운트) 2. 신규 매물이 있으면 주소/가격/AI 요약 기록 3. 가격 변동이 있으면 기록 4. `memory/YYYY-MM-DD.md`에 수집 결과 요약 작성 5. git commit & push (변경사항 있을 때만) ## DB 확인 (필요시) ```bash cd /Users/kendrick/projects/hauscout npx tsx -e " import { neon } from '@neondatabase/serverless'; import { drizzle } from 'drizzle-orm/neon-http'; import * as schema from './src/db/schema'; // .env.local 로드 필요 const sql = neon(process.env.DATABASE_URL!); const db = drizzle(sql, { schema }); const all = await db.select().from(schema.listings); console.log(JSON.stringify(all, null, 2)); " ``` ## 검색 프로필 (현재 2개) 1. Queensway 근처 콘도 (2-3bed, $800K 이하) 2. 미시사가 단독주택 ($800K 이하) ## 주의사항 - HouseSigma는 SPA → `domcontentloaded` + waitForTimeout 사용 - 가격 추출은 `"Listed for: $XXX"` 패턴만 사용 (Comparables 가격 혼입 방지) - 요청 간 2-4초 랜덤 딜레이 (rate limit 방지)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: namphamdev/fxtwitter (version 1.0.0)

--- name: fxtwitter description: Fetch tweet/post data from X (Twitter) links using the fxTwitter API (api.fxtwitter.com). Use when a user sends an x.com or twitter.com URL and wants to read, summarize, or extract tweet content. No auth required. Returns full tweet metadata including text, author, media, and engagement stats. --- # fxTwitter Fetch tweet data from an X/Twitter URL without auth or browser automation. ## Endpoint ``` GET https://api.fxtwitter.com/:tweetId ``` Extract the tweet ID from the URL: - `https://x.com/user/status/1234567890` → ID: `1234567890` - `https://twitter.com/user/status/1234567890` → ID: `1234567890` ## Usage ```bash curl -s "https://api.fxtwitter.com/1234567890" | jq '.tweet' ``` Key fields in `.tweet`: - `.text` — tweet content - `.author.name` / `.author.screen_name` - `.created_at` - `.likes`, `.retweets`, `.replies` - `.media.photos[]`, `.media.videos[]` - `.url` — canonical URL ## Output Format When presenting a tweet to the user: - Lead with TL;DR if the tweet is long - Use bullet points for key info - Keep it concise — no filler


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wd041216-bit/cross-validated-search (version 16.0.0)

--- name: cross-validated-search version: "16.0.0" description: > OpenClaw skill for source-backed web search, page reading, and evidence-aware claim checking. Use it to verify factual answers with live search results and explicit source handling. homepage: https://github.com/wd041216-bit/cross-validated-search --- # Cross-Validated Search for OpenClaw This skill gives OpenClaw a practical verification workflow: - `search-web` for live search results - `browse-page` for reading the full content of a source - `verify-claim` for support/conflict classification - `evidence-report` for a citation-ready summary with next steps ## Install ```bash pip install cross-validated-search ``` ## Minimum verification ```bash search-web "OpenAI API pricing" --type news --timelimit w verify-claim "Python 3.13 is the latest stable release" --deep --max-pages 2 --json evidence-report "Python 3.13 stable release" --claim "Python 3.13 is the latest stable release" --deep --json ``` ## Recommended flow 1. Run `search-web` for factual or recent questions. 2. Use `browse-page` on the most relevant source when snippets are not enough. 3. Use `verify-claim` when a concrete claim needs a support/conflict summary. 4. Use `evidence-report` when you want a compact evidence package with citations and next steps. 5. Use `--deep` when the claim matters enough to justify page-aware verification. 6. Cite the returned URLs in the final answer. ## What success looks like - the verdict is explicit - the result includes support and conflict scores - `page_aware` is true when deep verification ran - the recommended free path is `ddgs + self-hosted searxng` - source URLs are ready to cite ## Limits - `verify-claim` is heuristic and evidence-aware, not a proof engine. - The default provider path is `ddgs`. - The recommended free upgrade path is self-hosted `searxng` via `CROSS_VALIDATED_SEARCH_SEARXNG_URL`. - Conflicting sources are surfaced, not automatically reconciled. ## License MIT License.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mzfshark/anti-centralization-probe (version 1.0.0)

--- name: anti-centralization-probe description: Detect centralization risks in proposals and designs; output structural critiques and safer decentralization alternatives. metadata: author: AgentSmith version: 2.0.0 owner: Agent Smith Agent category: governance --- # SKILL: anti-centralization-probe ## Purpose Detect and resist centralization tendencies by identifying single points of control and dependency creation, then proposing safer alternatives. ## When to Use - Governance proposals - Role/permission changes - Designs that create a single authority or key-holder - Systems that require trusting one actor ## Inputs - `proposal` (required): - `description` - `actors` (list) - `permissions` (list) - `critical_paths` (list) - `policy` (required): - `centralization_red_flags` (optional list) - `max_allowed_single_points` (default 0) ## Steps 1. Identify control points: - who can change rules - who can halt systems - who can move funds (if applicable) 2. Identify dependency chains and single points of failure. 3. Count single points; compare to `max_allowed_single_points`. 4. Output a structural critique: - red flags - failure modes 5. Propose alternatives: - multisig/timelock - role separation - auditable approval flows ## Validation - Alternatives must reduce single-point control. - No personal attacks; only structural critique. ## Output - `centralization_probe_result`: - `single_points` (list) - `risk_level` (low|med|high) - `red_flags` (list) - `alternatives` (list) - `response_draft` (string) ## Safety Rules - No operational instructions for abuse. - Do not recommend custody or financial actions. ## Example If one agent can unilaterally enable live trading and move capital, output `risk_level=high` and recommend separation + explicit governance gates.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: swayamg20/trip-plan (version 1.0.0)

--- name: trip-plan description: Generate day-wise travel itineraries with timings, food spots, activities, and transport. Handles "plan my trip", "make an itinerary", revisions like "swap days" or "make it cheaper", and adjusts plans when user has existing flights or trains. --- # Trip Plan ## When to activate - User picks a destination: "let's do Kasol", "plan Kasol" - User says "plan my trip", "make an itinerary" - User provides travel details: "my flight lands at 11 AM" - User requests revisions: "swap day 1 and 2", "add a trek", "make it cheaper" ## How to generate 1. Confirm: destination, dates/duration, group size, budget (from conversation context) 2. Web search for current info: - "<destination> things to do <month> <year>" - "<destination> best restaurants cafes local food" - "<destination> to <nearby attraction> how to reach" 3. Build day-wise plan with real timings ## Handling existing bookings If user says "my flight lands at 11 AM" or "taking the 8 PM bus": - Start itinerary from arrival time, not morning - Account for travel from airport/bus stand to stay - "Flight lands 11 AM Kullu → 1.5 hr taxi to Kasol → Check-in 1 PM → Lunch →..." ## Itinerary format 📍 Kasol — 3 Days, 2 Nights (Mar 14-16) Budget: ₹8,000/person 🗓️ Day 1 — Mar 14 (Arrival + Settle In) 11:00 AM ✈️ Land at Kullu 11:30 AM 🚕 Taxi to Kasol (₹1500, 1.5 hrs) 1:00 PM 🏨 Check-in 1:30 PM 🍽️ Lunch at Evergreen Cafe 3:00 PM 🚶 Walk to Chalal village (30 min easy trail) 7:30 PM 🍽️ Dinner at Jim Morrison Cafe 🗓️ Day 2 — Mar 15 (Kheerganga Trek) 6:30 AM 🍽️ Breakfast + pack snacks 7:30 AM 🥾 Start Kheerganga Trek from Barshaini 1:30 PM 🏔️ Reach Kheerganga — hot springs! 3:30 PM 🥾 Start descent 8:00 PM 🍽️ Dinner at Little Italy 🗓️ Day 3 — Mar 16 (Manikaran + Departure) 9:30 AM 🚶 Walk to Manikaran Sahib 12:00 PM 🍽️ Langar lunch at Gurudwara 2:00 PM 🚕 Taxi to Bhuntar Airport 3:30 PM ✈️ Flight back 💰 Budget Breakdown: Transport: ₹4,500 | Stay: ₹2,400 | Food: ₹1,500 | Activities: ₹500 Total: ~₹8,900/person (excluding flights) ## Revision handling - "swap day 1 and 2" → rearrange and adjust timings - "make it cheaper" → budget stays, free alternatives - "add a trek on day 2" → insert and adjust surrounding timings - "I want one more day" → add Day 4 with new activities - Show only the updated section, not the entire itinerary again ## Rules - Give actual times (not "morning" or "afternoon") - Use real place names from web search - Include budget breakdown at the end - Be realistic — don't pack 15 activities in a day - Account for travel time between activities


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: makforce/github-1-0-0 (version 1.0.0)

--- name: github description: "Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries." --- # GitHub Skill Use the `gh` CLI to interact with GitHub. Always specify `--repo owner/repo` when not in a git directory, or use URLs directly. ## Pull Requests Check CI status on a PR: ```bash gh pr checks 55 --repo owner/repo ``` List recent workflow runs: ```bash gh run list --repo owner/repo --limit 10 ``` View a run and see which steps failed: ```bash gh run view <run-id> --repo owner/repo ``` View logs for failed steps only: ```bash gh run view <run-id> --repo owner/repo --log-failed ``` ## API for Advanced Queries The `gh api` command is useful for accessing data not available through other subcommands. Get PR with specific fields: ```bash gh api repos/owner/repo/pulls/55 --jq '.title, .state, .user.login' ``` ## JSON Output Most commands support `--json` for structured output. You can use `--jq` to filter: ```bash gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"' ```


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: deusyu/google-maps-skill (version 1.0.0)

--- name: gmaps description: 通过脚本直连 Google Maps Platform API 完成地理编码、逆地理编码、路线规划、地点搜索、地点详情、海拔查询和时区查询。用户要求"Google Maps 查询""国际路线规划""地点搜索"或需要用命令行脚本调用 Google Maps API 时使用。 --- # Google Maps Skill ## Quick Start 1. Ensure `GOOGLE_MAPS_API_KEY` is set. 2. Run `bun scripts/gmaps.ts --help` in this skill directory. 3. Pick the matching command from `references/command-map.md`. ## Workflow 1. Validate user intent and select one command. 2. Coordinates use **lat,lng** order (Google convention). 3. Keep output as raw Google Maps JSON without wrapping fields. 4. Treat any API business error as failure. ## Commands - Full command mapping: `references/command-map.md` - Ready-to-run examples: `references/examples.md` ## Notes - This skill is script-first and does not run an MCP server. - Only `GOOGLE_MAPS_API_KEY` is supported.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: stakeswky/skill-forge (version 0.3.0)

--- name: skill-forge version: 0.3.0 description: AI 技能自动发现、评估、集成、验证、宣传闭环系统 — 跨生态技能市场引擎 tags: [evolution, skills, automation, mcp, clawhub, x-twitter, sentiment] --- # SkillForge — 技能自动进化引擎 SkillForge 让 AI agent 自主发现自己缺什么能力，跨生态搜索最佳工具，自动评估兼容性和安全性，一键集成为可用 skill，并自动发布进化成果。 ## 核心能力 1. **多源发现** — 同时扫描 9+ 数据源（GitHub/HuggingFace/Reddit/X/ClawHub/OpenAI Skills/Claude Skills/Awesome Lists/Product Hunt） 2. **需求感知** — agent 声明能力缺口，Scout 优先定向搜索填补 3. **真实兼容性检测** — 实际探测本机 runtime（Node/Python/Rust/Go 版本、依赖可行性、平台限制） 4. **安全门控** — YARA 扫描 + 仓库健康检查 + 危险脚本检测 5. **ClawHub 标准输出** — 集成结果直接生成 `skill.json` + `SKILL.md`，可 `clawhub publish` 6. **自动分级** — auto/confirm/manual 三级 7. **舆情监测** — X/Twitter 实时搜索 AI agent 相关话题 8. **自动宣传** — pipeline 完成后自动生成并发布进化成果推文 ## 快速开始 ```bash cd /Volumes/data/openclaw/evolution-engine # 全流程（scout → evaluate → integrate → verify → benchmark → announce） pnpm pipeline # 单独运行 pnpm scout # 情报采集 pnpm evaluate # 价值评估 pnpm integrate # 自动集成 pnpm verify # 安全验证 pnpm announce # 舆情+宣传 ``` ## 需求感知（Needs-Driven Scout） 编辑 `data/needs.json` 声明能力缺口： ```json { "needs": [ { "area": "视频处理", "description": "本地视频转码、剪辑的轻量 CLI", "keywords": ["video transcoding", "ffmpeg wrapper"], "priority": "medium" } ] } ``` ## 6 阶段 Pipeline | 阶段 | 说明 | |------|------| | Scout | 9+ 源情报采集 + 需求驱动搜索 | | Evaluate | 5 维度加权评估（含真实运行时检测） | | Integrate | 自动克隆 + 安全扫描 + ClawHub skill 包生成 | | Verify | 仓库健康 + 依赖安全 + 危险脚本检测 | | Benchmark | 效能基准对比 | | Announce | 舆情快照 + 进化成果推文发布 | ## 依赖 Skills - `x-twitter` — X/Twitter 搜索、发帖、趋势（需 `TWITTER_BEARER_TOKEN`） - `social-sentiment` — 跨平台舆情分析（需 Xpoz 账号） ## 数据源 | # | 源 | 方式 | |---|---|------| | 1 | GitHub Trending | API / SearXNG | | 2 | HuggingFace | API / SearXNG | | 3 | Reddit | SearXNG / RSS | | 4 | Product Hunt | SearXNG | | 5 | Awesome Lists | GitHub API | | 6 | X/Twitter | SearXNG + Grok | | 7 | ClawHub | API → HTML → CLI | | 8 | OpenAI Skills | GitHub API | | 9 | Claude Skills | GitHub API | | 🎯 | 需求驱动 | SearXNG | ## 来源 - GitHub: https://github.com/stakeswky/skill-forge - 基于天一进化引擎 (Evolution Engine) v0.3 构建


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: sunnyyao2222-eng/feishu-calender (version 1.0.0)

--- name: feishu-calendar description: 飞书日历日程管理 Skill。创建日历/日程、查询空闲忙状态、订阅日历变更。当需要自动安排会议、查询时间冲突或监控日程变动时使用此 Skill。 required_permissions: - calendar:calendar - calendar:calendar.free_busy:read - calendar:calendar:subscribe --- # 飞书日历日程管理 你是飞书日历自动化专家，负责通过 Calendar v4 API 实现日程创建、空闲查询和日历订阅。 --- ## 一、API 基础信息 | 项目 | 值 | |------|---| | Base URL | `https://open.feishu.cn/open-apis/calendar/v4` | | 认证方式 | `Authorization: Bearer {tenant_access_token}` | | Content-Type | `application/json` | --- ## 二、日历操作 ### 1. 创建日历 ``` POST /open-apis/calendar/v4/calendars ``` ```json { "summary": "项目日历" } ``` **实测心法**：创建后可添加协作者，适合为特定项目创建共享日历。 --- ## 三、日程操作 ### 2. 创建日程 创建日程分为两步： **第一步：创建日程基本信息** ``` POST /open-apis/calendar/v4/calendars/:calendar_id/events ``` ```json { "summary": "需求对齐会", "description": "讨论 Q2 产品规划", "start_time": { "timestamp": "1770641576" }, "end_time": { "timestamp": "1770645176" } } ``` **第二步：添加参与人 (关键)** 创建成功获取 `event_id` 后，必须调用此接口才能邀请他人： ``` POST /open-apis/calendar/v4/calendars/:calendar_id/events/:event_id/attendees?user_id_type=open_id ``` ```json { "attendees": [ { "type": "user", "user_id": "ou_xxx" } ] } ``` **实测心法**： - **创建日程接口不支持直接传参会人**，必须分两步走。 - **时间戳必须为秒级字符串**（不是毫秒，不是数字）。 - 必须带上 `user_id_type=open_id` 参数，否则无法识别 `ou_` 开头的 ID。 - 添加 `reminders` 确保参会人收到提醒。 ### 3. 更新日程 ``` PATCH /open-apis/calendar/v4/calendars/:calendar_id/events/:event_id ``` ### 4. 删除日程 ``` DELETE /open-apis/calendar/v4/calendars/:calendar_id/events/:event_id ``` --- ## 四、智能排期 ### 5. 批量查询空闲忙状态 ``` POST /open-apis/calendar/v4/freebusy/list ``` ```json { "time_min": "2026-02-10T09:00:00+08:00", "time_max": "2026-02-10T18:00:00+08:00", "user_id": "ou_xxx" } ``` **实测心法**：安排跨部门会议的利器。先查空闲再创建日程，避免时间冲突。 --- ## 五、日历订阅 ### 6. 订阅日历变更 ``` POST /open-apis/calendar/v4/calendars/:calendar_id/subscribe ``` **实测心法**：实时感知关键人员的日程变动并调整后续任务。需要配置事件回调（Webhook）。 --- ## 六、最佳实践 1. **先查后建**：创建日程前先用 freebusy 查询空闲时段 2. **秒级字符串**：时间戳格式是最常踩的坑，必须是秒级字符串 3. **提醒设置**：始终添加 reminders，否则参会人容易错过 4. **订阅监控**：对关键日历开启订阅，实现日程变更的实时感知


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cjmore66/tia-commissioning-delta (version 1.0.0)

--- name: TIA_COMMISSIONING_DELTA description: Use TIA Openness to compare latest site backup against yesterday's baseline with focus on process control logic. tools: - shell - filesystem --- You can: - Access site backups (e.g. from cloud storage or NAS). - Call external scripts that: - extract DB/FB logic via Openness - compute diffs for PID blocks, safety blocks, and sequences - Return structured JSON with: - changed PIDs - bypassed interlocks - changed sequences and alarms. When the field engineer asks "@OpenClaw diff the latest site backup against yesterday's baseline": 1) Locate both .zap18 archives. 2) Run the commissioning diff script. 3) Return a compact JSON of the most significant changes for the agent to explain.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: djc00p/codebase-explorer (version 1.0.0)

--- name: codebase-explorer description: "Analyze unfamiliar codebases and generate structured onboarding guides with architecture maps, entry points, conventions, and project-specific instructions. Use when joining new projects, understanding repo structure, or generating CLAUDE.md. Trigger phrases: onboard me, understand this codebase, analyze this repo, generate CLAUDE.md, walk me through. Adapted from everything-claude-code by @affaan-m (MIT)" metadata: {"clawdbot":{"emoji":"🗺️","requires":{"bins":[],"env":[]},"os":["darwin","linux","win32"]}} --- # Codebase Onboarding Systematically analyze unfamiliar codebases and produce structured onboarding guides. ## When to Use - First time opening a project - Joining a new team or repository - User asks "help me understand this codebase" - User asks to generate a CLAUDE.md for a project - User says "onboard me" or "walk me through this repo" ## Quick Start 1. Scan package manifests and config files (identify tech stack and framework) 2. Map directory structure and entry points (top 2 levels) 3. Trace one request lifecycle (entry → validation → business logic → database) 4. Identify naming, error handling, and testing patterns 5. Generate onboarding guide and starter CLAUDE.md ## Key Concepts - **Reconnaissance** — Use file presence patterns to infer tech stack and structure - **Architecture pattern** — Identify monolith vs. monorepo, frontend/backend split, API style - **Convention detection** — Extract naming, error handling, async patterns from recent code - **Guided exploration** — Don't read everything; use grep/glob selectively for ambiguity - **Two-artifact output** — Onboarding guide (structure + conventions) + CLAUDE.md (project instructions) ## Common Usage Most frequent patterns: - Understanding project structure and data flow - Documenting tech stack and architecture - Creating starter instructions for new team members - Identifying where to make changes (API routes, UI pages, database, tests) - Mapping conventions (naming, error handling, git workflow) ## References - `references/reconnaissance.md` — File patterns for framework/language detection - `references/artifacts.md` — Onboarding guide and CLAUDE.md templates, best practices, anti-patterns


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