OpenClaw/clawhub-security-signals huggingface dataset:
Skill: leooooooow/ugc-brief-to-script-engine (version 1.0.3)

--- name: ugc-brief-to-script-engine description: Convert a brand or campaign brief into creator-native UGC script packs with hooks, shot lists, and revision-safe variants. Use when the user has a sponsor brief, needs ad-ready short scripts, or wants fast UGC deliverables that still sound human and platform-native. --- # UGC Brief To Script Engine ## Skill Card - **Category:** Creator Content - **Core problem:** How to turn a brand brief into creator-native scripts? - **Best for:** UGC production and agency workflows - **Expected input:** Brand brief, ICP, product facts, forbidden claims - **Expected output:** Script pack (angles, hooks, CTA, proof points) - **Creatop handoff:** Batch-generate and review scripts via Creatop ## What this does Turn a raw brand brief into creator-ready UGC scripts that can be filmed quickly and safely. ## Workflow ### 1) Parse brief essentials Extract: - product and audience - key claims - mandatory points - banned claims - offer/CTA - visual constraints If brief gaps exist, mark assumptions explicitly. ### 2) Build script pack Generate 3 variants: - Problem/Solution - Demo/Proof - Personal Story Each variant includes: - hook line - scene-by-scene shot notes - script lines - CTA close ### 3) Add revision-safe options Provide: - softer-claim version - 15s cutdown - stronger proof-first cut ### 4) Add compliance handoff Include: - claim safety notes - mandatory proof shots (if any) - quick filming checklist - approval-ready summary for brand/client ## Quality rules - Keep wording human and creator-native. - Respect all mandatory and banned claim constraints. - Do not invent scientific/medical claims. - Prioritize filmability for small creator teams. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nhzallen/agent-paddleocr-vision (version 1.1.0)

--- name: agent-paddleocr-vision description: Multi-language document understanding with PaddleOCR metadata: openclaw: requires: env: - PADDLEOCR_DOC_PARSING_API_URL - PADDLEOCR_ACCESS_TOKEN bins: - python primaryEnv: PADDLEOCR_ACCESS_TOKEN emoji: "👁️" homepage: https://github.com/NHZallen/agent-paddleocr-vision --- # Agent PaddleOCR Vision **OCR with Agent Actions — powered by PaddleOCR only.** Automatically classifies documents and provides actionable prompts. ## What It Does - OCR extraction via **PaddleOCR cloud API** (requires credentials) - 11 document types: invoice, business card, receipt, table, contract, ID card, passport, bank statement, driver's license, tax form, general - Action suggestion with structured parameters - Batch processing - Searchable PDF generation (with bbox alignment) ## Quick Start ```bash # Install dependencies pip3 install -r scripts/requirements.txt # Configure PaddleOCR API export PADDLEOCR_DOC_PARSING_API_URL=https://your-api.paddleocr.com/layout-parsing export PADDLEOCR_ACCESS_TOKEN=your_token # Process a file python3 scripts/doc_vision.py --file-path ./invoice.jpg --pretty --make-searchable-pdf ``` ## Batch ```bash python3 scripts/doc_vision.py --batch-dir ./inbox --output-dir ./out ``` ## Output See `docs/README.zh.md` for full JSON schema and integration guide. ## Supported Types | Type | Actions | |------|---------| | Invoice | create_expense, archive, tax_report | | Business Card | add_contact, save_vcard | | Receipt | create_expense, split_bill | | Table | export_csv, analyze_data | | Contract | summarize, extract_dates, flag_obligations | | ID Card | extract_id_info, verify_age | | Passport | store_passport_info, check_validity | | Bank Statement | categorize_transactions, generate_report | | Driver License | store_license_info, check_expiry | | Tax Form | summarize_tax, suggest_deductions | | General | summarize, translate, search_keywords | ## Configuration Required environment variables: - `PADDLEOCR_DOC_PARSING_API_URL` — API endpoint ending in `/layout-parsing` - `PADDLEOCR_ACCESS_TOKEN` — Access token Optional: - `PADDLEOCR_DOC_PARSING_TIMEOUT` — Default 600 seconds ## Searchable PDF With `--make-searchable-pdf`, embeds OCR text layer aligned to original layout using bounding boxes. Requires `pdf2image` + `poppler` (system) and `reportlab`, `pypdf`, `pillow` (Python). ## Full Documentation Detailed usage, troubleshooting, and development guide available in multiple languages under `docs/`: - 中文: `docs/README.zh.md` - English: `docs/README.en.md` - Español: `docs/README.es.md` - العربية: `docs/README.ar.md` ## License MIT-0 --- **Made for OpenClaw.** Let your agent see and act.

path: scripts/__init__.py
content: # Agent Vision Skill

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: keson1521/video-collector (version 1.0.0)

--- name: video-collector description: 抖音/B站视频收藏入库——接收视频链接，自动抓取信息、生成总结、分类、入库飞书多维表格。触发词：视频入库 / 收藏视频 / 帮我整理这个视频。 argument-hint: <视频链接> allowed-tools: Bash(curl *), Bash(node *), Bash(date *), Bash(mkdir *), Bash(rm *), WebFetch, Skill --- # 视频收藏入库 接收抖音/B站视频链接 → 自动抓取信息 → 生成总结+分类 → 写入飞书多维表格。 --- ## 配置说明 使用前需要在 skill 文件顶部填写你的飞书多维表格配置： ``` Base [REDACTED_SECRET] Token（格式：app_xxxxxxxxxxxx） Table ID: 你的Table ID（格式：tblxxxxxxxxxxxx） Base URL: https://my.feishu.cn/base/你的BaseToken ``` > 如何获取：在飞书多维表格页面 URL 中提取，例如 `https://my.feishu.cn/base/U7DRbtjO6aMlJcsYBGUcvah2nsf`，则 Base Token 为 `U7DRbtjO6aMlJcsYBGUcvah2nsf`。 --- ## 字段映射（飞书多维表格） 建议按以下结构创建多维表格字段： | 字段名 | 类型 | 说明 | |-----------|-------------|------------------------| | 标题 | 文本 | 视频标题（主字段） | | 平台 | 单选 | 抖音 / B站 | | 链接 | 文本 | 原始视频链接 | | UP主/博主 | 文本 | UP主/博主名称 | | 时长 | 文本 | 如 "12:34" | | 类型 | 单选 | 知识/娱乐/资讯/教程/其他 | | 主题 | 多选 | AI/产品/编程/设计/投资/生活/职场/其他 | | 内容总结 | 文本 | 2-4句内容概括 | | 添加日期 | 日期 | 格式 yyyy/MM/dd | | 标签 | 多选 | 已看/想看/反复看/待整理 | --- ## 流程 ### 1. 判断平台 - 抖音：`douyin.com` / `v.douyin.com` - B站：`bilibili.com` / `b23.tv` ### 2. 抓取视频信息 **统一方案：dokobot（支持抖音、B站及所有 JS 渲染页面）** ```bash # 用 dokobot 读取页面，自动 JS 渲染，所有平台统一处理 dokobot read "<url>" --local --timeout 5000 ``` 从返回内容中提取： - **标题**：取第一行标题（去除链接和噪音） - **UP主/博主**：找 "UP主名" 或 "作者" 相关行 - **时长**：找 "mm:ss" 或 "时长" 相关行 - **描述/简介**：找正文描述段落 - **发布时间**：找 "发布于" 或日期格式内容 - **点赞/播放**：找数字+单位（万/千） **抖音短链补全**：如果是 `v.douyin.com/xxx` 短链，先用 curl 获取完整 URL： ```bash # 抖音短链 → 完整 URL FULL_URL=$(curl -sL "https://v.douyin.com/xxx" -w "%{url_effective}" -o /dev/null) # 然后送入 dokobot dokobot read "$FULL_URL" --local --timeout 5000 ``` ### 3. 生成字段 **类型判断**（根据内容推断）： - 教程/操作演示 → "教程" - 知识科普/深度解读 → "知识" - 资讯/热点/新闻 → "资讯" - 搞笑/娱乐/休闲 → "娱乐" - 无法判断 → "其他" **主题判断**（多选，最多选3个）： - 涉及 AI/大模型/ChatGPT/编程/模型训练 → ["AI", "编程"] - 涉及 产品经理/需求/Aha Moment/用户增长 → ["产品"] - 涉及 UI/UX/设计/视觉/交互 → ["设计"] - 涉及 投资/理财/股票/黄金/赚钱 → ["投资"] - 涉及 职场/求职/面试/职场人际 → ["职场"] - 涉及 生活日常/美食/情感/家居 → ["生活"] - 无法判断 → ["其他"] **标签**：默认填 "想看"，如果用户说"已看完"或类似表述则填 "已看" **内容总结**：根据视频标题+描述，用 2-4 句话概括核心内容。 ### 4. 入库 ```bash # 添加日期用当天，格式 "YYYY-MM-DD HH:mm:ss" ADD_DATE=$(date +"%Y-%m-%d %H:%M:%S") # 写入飞书（替换 <YOUR_BASE_TOKEN> 和 <YOUR_TABLE_ID> 为实际值） lark-cli base +record-upsert \ --base-token <YOUR_BASE_TOKEN> \ --table-id <YOUR_TABLE_ID> \ --json @./_record.json ``` 其中 `_record.json` 内容格式： ```json { "标题": "视频标题", "平台": "抖音", "链接": "https://...", "UP主/博主": "UP主名", "时长": "5:31", "类型": "教程", "主题": ["AI", "设计"], "内容总结": "内容概括...", "添加日期": "2026-05-04 10:30:00", "标签": ["想看"] } ``` ### 5. 输出确认 ``` ✅ 已入库视频收藏库 🔗 https://my.feishu.cn/base/<YOUR_BASE_TOKEN> 📋 {标题} 🎯 {平台} · {类型} · {主题} 💬 {内容总结} ``` --- ## 错误处理 - 抓取失败 → 检查 URL 是否有效，确认 dokobot 浏览器扩展已连接 - 入库失败 → 检查 Base Token 和 Table ID 是否正确配置 - 字段类型不匹配 → 确认 JSON 中字段值类型与飞书字段类型一致


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dmoraine/claw-easa (version 1.0.0)

--- name: claw-easa description: Query EASA Easy Access Rules locally with exact reference lookup, full-text search, and semantic search. Use when answering EASA regulatory questions, retrieving article/reference wording, checking applicability, or finding AMC, GM, and FAQ material from the local claw-easa index. --- Use the local `claw-easa` CLI from this repository. Preferred commands: - `claw-easa lookup <REF>` for exact references such as `ORO.FTL.110` - `claw-easa refs "<query>"` for reference-oriented search - `claw-easa snippets "<query>"` for cited text excerpts - `claw-easa hybrid "<query>"` for mixed lexical + semantic retrieval - `claw-easa ask "<question>"` for routed natural-language queries - `claw-easa status` to verify corpus/index availability Source-scoped search — use `--slug <source>` to restrict results to a specific source document. This is important when a broad query returns too many results from different sources, or when you know which source is most relevant: - `claw-easa refs "crew fatigue" --slug occurrence-reporting` - `claw-easa snippets "crew fatigue" --slug occurrence-reporting` - `claw-easa hybrid "fatigue reporting" --slug occurrence-reporting` Use `claw-easa sources-list` to see available slugs. - `claw-easa sources-list` to list ingested EARs and FAQ domains (supports `--type ear|faq`) - `claw-easa ear-discover` to list Easy Access Rules available on the EASA website - `claw-easa ear-list` to list built-in known source aliases - `claw-easa ingest fetch <slug>` to download a source (use `--url` to bypass catalog) - `claw-easa ingest parse <slug>` to parse a fetched source into the database - `claw-easa ingest diagnose <slug>` to verify parser coverage against the source XML - `claw-easa ingest faq-all` to ingest all EASA FAQs (crawls every sub-domain) - `claw-easa ingest faq <domain>` to ingest FAQs for a specific domain - `claw-easa ingest faq-discover` to list available FAQ domains on the EASA website Answering rules: - Prefer exact lookup when the user gives a regulation reference. - Quote the retrieved text or excerpt before paraphrasing. - Distinguish regulation text from AMC/GM/FAQ material. - If retrieval is empty or ambiguous, say so explicitly instead of inferring. - When the question targets a specific regulation domain (e.g. occurrence reporting, aircrew, air operations), use `--slug` to scope the search. This dramatically improves recall for items buried in long annexes. Read these files only when needed: - `references/usage.md` for repository-aware usage and installation notes - `references/runtime-setup.md` when the local `claw-easa` CLI may not be installed yet - `references/easa-answering.md` for answer format and evidence rules Local install notes: - The skill package lives under `skill/claw-easa/` in the repository. - For OpenClaw local installation, copy this folder into `~/.openclaw/workspace/skills/claw-easa/`. - Avoid symlinks that resolve outside the OpenClaw workspace.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nitansde/rocom-merchant-inventory (version 1.0.2)

--- name: rocom-merchant-inventory description: 查询《洛克王国世界》远行商人当前库存，自动过滤出本轮仍有效的商品。适用于想查看远行商人当前卖什么，或需要输出简洁文本、JSON 结果时使用；需要用户自行提供 API key。 --- # 远行商人库存查询 查询当前远行商人库存，并自动过滤掉非当前轮次商品。 ## 使用前提 - 需要 `Python 3` - 需要 Python 包：`requests` - 需要环境变量 `ROCOM_API_KEY`，或使用参数 `--api-key` ## API key 来源 - 请从 `https://github.com/Entropy-Increase-Team/` 相关项目或说明中获取 - 不要把 API key 写进公开仓库或公开聊天 ## 远程接口 本 skill 会请求： - `https://wegame.shallow.ink/api/v1/games/rocom/merchant/info?refresh=true` ## 用法 ```bash ROCOM_API_KEY=你的key python3 skills/rocom-merchant-inventory/scripts/fetch_inventory.py python3 skills/rocom-merchant-inventory/scripts/fetch_inventory.py --api-key 你的key --format json --pretty ``` ## 输出格式 - 默认输出简洁文本 - `--format json` 输出 JSON


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bestisblessed/export (version 1.0.2)

--- name: export description: Export a Codex session JSONL from ~/.codex/sessions into a clean Markdown transcript in ~/Documents/Exports. Use when the user wants to export, save, or convert the current Codex conversation or another conversation by session id. --- # Export Conversation Run `scripts/export_conversation.py`. This exports the current chat by default using the Codex thread id from the environment. To export a different conversation, pass a raw session id, for example: ```bash scripts/export_conversation.py 019dc927-dac9-7f23-b313-917d776d189e ``` The script reads from `~/.codex/sessions` and writes: `~/Documents/Exports/<session-id>.md` The script creates `~/Documents/Exports` when needed. Print the saved path after running it. ## Local Scope This skill only reads local Codex session JSONL files under: `~/.codex/sessions` This skill only writes Markdown exports under: `~/Documents/Exports` It makes no network calls, reads no credentials, installs no packages, and downloads no external files.

path: agents/openai.yaml
content: interface:
  display_name: "Export"
  short_description: "Export a Codex session to Markdown"
  default_prompt: "Export a Codex conversation by session id."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: felicitationes/attio-apikey (version 1.0.1)

--- name: attio-apikey description: | Direct Attio CRM integration for OpenClaw with full CRUD capabilities. Query, create, update, and delete companies, people, and notes in real-time. Uses Attio API key directly - no OAuth or proxy server required. compatibility: OpenClaw with exec tool credentials: requiredEnv: - ATTIO_API_KEY metadata: author: felixwithoutx version: "1.0.1" tags: - attio - crm - sales - pipeline license: MIT --- # Attio Direct Skill Direct Attio CRM integration with full CRUD (Create, Read, Update, Delete) operations. ## Setup 1. Get API key from https://app.attio.com/settings/api 2. Copy `.env.example` to `.env` and add your key ## Quick Commands ```bash # Read data python3 attio_client.py companies python3 attio_client.py people # Create python3 attio_client.py companies --create --data '{"name": "Acme"}' # Update python3 attio_client.py companies --update --id RECORD_ID --data '{"funnel_stage": "Nurture"}' # Delete python3 attio_client.py companies --delete --id RECORD_ID # Add note python3 attio_client.py companies --note "Title|Content" --id RECORD_ID ``` ## Features - Full CRUD on companies and people - Add notes to any record - Query with pagination (default 5000) - Direct API - no OAuth overhead ## Files ``` attio-direct/ ├── attio_client.py # Main CLI ├── .env.example # API key template ├── README.md # Setup instructions └── SKILL.md # This file ```


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: santosparra651-arch/algora-bounty-assistant (version 1.0.0)

--- name: github-bounty-hunter version: 1.0.0 description: Automatically search for low-competition GitHub bounty tasks (comments < 5) and generate a clean report with details and estimated difficulty. author: Your Name price: 299 category: Bounty Hunting tags: - github - bounty - automation - search --- # GitHub Bounty Hunter 🎯💰 Automatically find low-competition bounty tasks on GitHub. Filter by number of comments to avoid crowded bounties and maximize your chance of getting accepted. ## Features - 🔍 **Search open bounties** with label:bounty and state:open - 🎯 **Low-competition filter** - only shows bounties with `< N` comments (default 5) - 📊 **Generate clean report** with title, URL, comment count, difficulty estimation - ⚡ **Fast search** via GitHub API - 🎨 **Sorted by competition level** - least competition first ## Installation ```bash npx clawhub install github-bounty-hunter ``` ## Usage Search for low-competition bounties: ```bash npx github-bounty-hunter ``` Customize maximum comment count: ```bash npx github-bounty-hunter 3 ``` ## Output example: ```text ========================================= GitHub Bounty Hunter - Search Results ========================================= Found 5 low-competition bounties: 👉 Add transaction history page URL: `https://github.com/example/project/issues/6` 💬 Comments: 2 | 💪 Difficulty: Beginner 👉 FAQ & How It Works Page URL: `https://github.com/example/project/issues/264` 💬 Comments: 1 | 💪 Difficulty: Beginner ... ``` ## Requirements - Node.js 16+ - GitHub API access (authenticated for higher rate limits) ## How it works This skill uses the GitHub Search API to find open issues with the bounty label, filters by comment count, sorts by least comments first, and outputs a clean report ready for you to pick your next bounty. ## Perfect for: - Beginners looking for easy bounties - Bounty hunters who want less competition - Daily quick scans for new opportunities ## Changelog ### 1.0.0 - Initial release - Basic search and filtering - Report generation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dj-520-ai/contract-reviewer-dongjie (version 0.1.0)

# Contract Reviewer Dongjie This skill analyzes legal contract text and extracts structured insights including: - Key clauses (termination, liability, confidentiality, payment) - Risk indicators - Obligations for each party - Compliance-related notes - Negotiation recommendations ## How it works The skill reads contract text, detects clause patterns, and returns structured findings. ## Example Input: "The service provider may terminate this agreement at any time without notice." Output: - Clause: Termination - Risk: High - Note: Unilateral termination without notice. ## Intended users Consultants, freelancers, small business owners, and analysts who need fast contract review. ## Version 0.1.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: imbing/feishu-img-msg (version 1.0.0)

--- name: feishu-img-msg description: | 飞书图片消息操作技能，当触发飞书发送图片时自动调用本技能。 支持上传图片、发送图片消息、获取图片内容。 Activate when user mentions: 飞书发图、发送图片、上传图片、获取图片、下载图片、image_key。 --- # 飞书图片消息操作技能 本技能提供飞书图片消息的完整操作能力，包括上传、发送、获取图片。 ## 功能 | 功能 | 命令 | 说明 | |------|------|------| | 上传图片 | `feishu-img-msg upload` | 上传本地图片到飞书，返回 image_key | | 发送图片 | `feishu-img-msg send` | 发送图片到指定会话（支持文件路径或 image_key） | | 获取图片 | `feishu-img-msg get` | 下载飞书图片到本地 | | 查看图片 | `feishu-img-msg view` | 获取图片信息（返回 base64 供 AI 查看） | ## 快速使用 ### 1. 发送本地图片到会话 ```bash # 直接发送本地图片文件 python3 ~/.openclaw/workspace/skills/feishu-img-msg/scripts/feishu_image.py \ send \ --chat-id "oc_xxxxx" \ --file "/path/to/image.jpg" ``` ### 2. 上传图片获取 image_key ```bash # 上传图片，返回 image_key python3 ~/.openclaw/workspace/skills/feishu-img-msg/scripts/feishu_image.py \ upload \ --file "/path/to/image.png" ``` ### 3. 用 image_key 发送图片 ```bash # 使用已有的 image_key 发送 python3 ~/.openclaw/workspace/skills/feishu-img-msg/scripts/feishu_image.py \ send \ --chat-id "ou_xxxxx" \ --image-key "img_xxxxx" ``` ### 4. 下载图片 ```bash # 下载飞书图片到本地 python3 ~/.openclaw/workspace/skills/feishu-img-msg/scripts/feishu_image.py \ get \ --image-key "img_xxxxx" \ --output "/path/to/save.jpg" ``` ### 5. 查看图片内容（AI 可读） ```bash # 获取图片 base64，用于 AI 分析 python3 ~/.openclaw/workspace/skills/feishu-img-msg/scripts/feishu_image.py \ view \ --image-key "img_xxxxx" ``` ## 参数说明 ### send 命令 | 参数 | 必填 | 说明 | |------|------|------| | `--chat-id` | ✅ | 接收者 ID（群聊 oc_ 开头，用户 ou_ 开头） | | `--file` | 二选一 | 本地图片文件路径 | | `--image-key` | 二选一 | 已上传的 image_key | | `--chat-type` | 否 | 会话类型：`group`(默认) 或 `user` | | `--account` | 否 | 飞书账户名（默认 main） | ### upload 命令 | 参数 | 必填 | 说明 | |------|------|------| | `--file` | ✅ | 本地图片文件路径 | | `--account` | 否 | 飞书账户名（默认 main） | ### get 命令 | 参数 | 必填 | 说明 | |------|------|------| | `--image-key` | ✅ | 图片的 image_key | | `--output` | ✅ | 保存路径 | | `--account` | 否 | 飞书账户名（默认 main） | ### view 命令 | 参数 | 必填 | 说明 | |------|------|------| | `--image-key` | ✅ | 图片的 image_key | | `--account` | 否 | 飞书账户名（默认 main） | ## 支持的图片格式 - JPEG (.jpg, .jpeg) - PNG (.png) - GIF (.gif) - BMP (.bmp) - WebP (.webp) 文件大小限制：单张图片最大 30MB ## 权限要求 | Scope | 用途 | |-------|------| | `im:resource` | 上传图片、获取图片 | | `im:message` | 发送消息 | | `im:message:send_as_bot` | 以机器人身份发送 | ## 常见问题 ### 图片发送失败 1. **检查 chat-id 类型** - 群聊 ID 以 `oc_` 开头，chat_type 设为 `group` - 用户 ID 以 `ou_` 开头，chat_type 设为 `user` 2. **检查图片格式** - 支持常见图片格式 - 确保文件未损坏 3. **检查权限** - 机器人需要已加入目标群聊 - 机器人需要与用户有单聊会话 ### token 过期 脚本会自动刷新 tenant_access_token，如仍报错： - 错误码 `99991400` - token 过期，稍后重试 - 错误码 `99991401` - token 无效，检查 app 配置 ## 参考文档 - 飞书上传图片 API: https://open.feishu.cn/document/server-docs/im-v1/image/create - 飞书获取图片 API: https://open.feishu.cn/document/server-docs/im-v1/image/get - 飞书发送消息 API: https://open.feishu.cn/document/server-docs/im-v1/message/create


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xueyetianya/deploy-helper (version 2.0.0)

--- version: "2.0.0" name: deploy-helper description: "One-command deployment assistant. Generate Dockerfiles, docker-compose configs, Nginx configs, CI/CD pipelines, Vercel/Netlify configs, Kubernetes manifests." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # 🚀 Deploy Helper > From local to production — deployment configs generated in seconds. ## Usage ```bash bash scripts/deploy.sh <command> <project_type> [options] ``` Where `project_type` is one of: `node`, `python`, `go`, `java`, `static` ## Commands ### Containers - `docker <type>` — Multi-stage Dockerfile with security best practices - `compose <type>` — docker-compose.yml with common middleware ### Web Server - `nginx <type>` — Nginx config (reverse proxy / SPA / static site) - `ssl <domain>` — SSL/Let's Encrypt certificate config ### CI/CD - `ci <platform>` — CI/CD pipeline (`github` / `gitlab` / `jenkins`) ### Serverless - `vercel <type>` — vercel.json configuration - `netlify <type>` — netlify.toml configuration ### Orchestration - `k8s <type>` — Kubernetes Deployment + Service + Ingress YAML ## Choosing a Deploy Path ``` Side project → Vercel / Netlify (zero config) Small team → Docker Compose + Nginx Production → K8s + CI/CD + SSL ``` --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com


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