OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dzy-1026/xfyun-face-compare (version 1.0.0)

--- name: face-compare description: Compare two face images and return similarity score using iFlytek Face Recognition API. homepage: https://www.xfyun.cn/doc/face/xf-silent-in-vivo-detection/API.html metadata: { "openclaw": { "emoji": "👤", "requires": { "bins": ["python3"], "env": ["XF_FACE_APP_ID", "XF_FACE_API_KEY", "XF_FACE_API_SECRET"] }, "primaryEnv": "XF_FACE_APP_ID" } } --- # 👤 Face Compare Compare two face images and calculate their similarity score using iFlytek's advanced face recognition technology. Designed for identity verification, face matching, and security authentication scenarios. --- ## ✨ Features - High-accuracy face comparison - Base64 image encoding support - Multiple image format support (jpg, png, bmp) - Detailed similarity scoring - One-command execution --- ## 🚀 Usage ```bash python {baseDir}/scripts/index.py "<image1_path>" "<image2_path>" ``` Example: ``` python {baseDir}/scripts/index.py "/path/to/face1.jpg" "/path/to/face2.jpg" ``` ## 📋 Input Specification ### Image Requirements - Supported formats: JPG, PNG, BMP - File size: < 4MB recommended - Image should contain clear, frontal face - One face per image for best results --- ## ⚠ Constraints - Both image paths must be valid and accessible - Images must contain detectable faces - API credentials must be configured - Network connection required --- ## 🔧 Environment Setup Required: - Python available in PATH - Environment variables configured: ```bash export XF_FACE_APP_ID=your_app_id export XF_FACE_API_KEY=your_api_key export XF_FACE_API_SECRET=your_api_secret ``` Or configure it in `~/.openclaw/openclaw.json`: ```json { "env": { "XF_FACE_APP_ID": "your_app_id", "XF_FACE_API_KEY": "your_api_key", "XF_FACE_API_SECRET": "your_api_secret" } } ``` --- ## 📦 Output Returns JSON response with: - Similarity score (0-100) - Comparison result (same person or not) - Confidence level - Face detection status --- ## 🎯 Target Use Cases - Identity verification - Access control systems - Duplicate account detection - Photo matching services - Security authentication - Attendance systems --- ## 🛠 Extensibility Future enhancements may include: - Batch face comparison - Face quality assessment - Multiple face detection - Liveness detection integration - Custom threshold configuration --- Built for automation workflows and AI-driven identity verification.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: omar-khaleel/qr-code-intelligence (version 1.0.0)

--- name: qr-code description: Generate and read QR codes. Use when the user wants to create a QR code from text/URL, or decode/read a QR code from an image file. Supports PNG/JPG output and can read QR codes from screenshots or image files. --- # QR Code Generate QR codes from text/URLs and decode QR codes from images. ## Capabilities - Generate QR codes from any text, URL, or data - Customize QR code size and error correction level - Save as PNG or display in terminal - Read/decode QR codes from image files (PNG, JPG, etc.) - Read QR codes from screenshots ## Requirements Install Python dependencies: ### For Generation ```bash pip install qrcode pillow ``` ### For Reading ```bash pip install pillow pyzbar ``` On Windows, pyzbar requires Visual C++ Redistributable. On macOS: `brew install zbar` On Linux: `apt install libzbar0` ## Generate QR Code ```bash python scripts/qr_generate.py "https://example.com" output.png ``` Options: - `--size`: Box size in pixels (default: 10) - `--border`: Border size in boxes (default: 4) - `--error`: Error correction level L/M/Q/H (default: M) Example with options: ```bash python scripts/qr_generate.py "Hello World" hello.png --size 15 --border 2 ``` ## Read QR Code ```bash python scripts/qr_read.py image.png ``` Returns the decoded text/URL from the QR code. ## Quick Examples Generate QR for a URL: ```python import qrcode img = qrcode.make("https://openclaw.ai") img.save("openclaw.png") ``` Read QR from image: ```python from pyzbar.pyzbar import decode from PIL import Image data = decode(Image.open("qr.png")) print(data[0].data.decode()) ``` ## Scripts - `scripts/qr_generate.py` - Generate QR codes with customization options - `scripts/qr_read.py` - Decode QR codes from image files

path: CLAUDE.md
content: AGENTS.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mzfshark/axodus-code-review (version 1.0.0)

--- name: code-review description: Review code for correctness, security, performance, and maintainability. metadata: author: RedHat Dev version: 1.0.0 owner: RedHat Dev Agent category: core --- # SKILL: code-review ## Purpose Analyze code (or a diff) to detect bugs, security issues, performance problems, and maintainability risks, then propose concrete improvements. ## When to Use - Reviewing a PR/diff before merging. - A bug is suspected but not yet reproduced. - Hardening/security pass is requested. ## Inputs - `scope` (required, string): files, diff, or code snippet to review. - `intent` (optional, string): what the code is supposed to do. - `constraints` (optional, string[]): security/perf/compat constraints. - `risk_tolerance` (optional, enum: `low|medium|high`). ## Steps 1. Identify entrypoints, invariants, and trust boundaries. 2. Check correctness: - edge cases - error handling - concurrency/races (if applicable) 3. Check security: - input validation - authz/authn - secrets handling - injection risks 4. Check performance and resource usage: - hotspots - unbounded loops/data growth 5. Check maintainability: - naming - duplication - test coverage gaps 6. Produce a prioritized, actionable report. ## Validation - Findings include concrete evidence (line references, behavior, or reproducible scenario). - Suggestions are compatible with stated constraints. - Distinguish â€œmust-fixâ€ from â€œnice-to-haveâ€. ## Output Review report (example schema): ```yaml summary: "<1 paragraph>" findings: - id: "CR-001" severity: "high|medium|low" category: "bug|security|perf|maintainability" issue: "<what>" impact: "<why it matters>" recommendation: "<how to fix>" ``` ## Safety Rules - Do not claim vulnerabilities without evidence. - Do not recommend unsafe patterns (e.g., disabling validation to â€œmake it workâ€). - Prefer minimal, targeted fixes. ## Example Input: - `scope`: â€œdiff for auth middlewareâ€ Output: - findings include missing `audience` check on JWTs and a failing negative test case.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhaobod1/huo15-openclaw-verify-mode (version 2.2.1)

--- name: huo15-openclaw-verify-mode version: 2.2.0 description: "验证模式 — 检查工作成果、运行测试、验证假设。借鉴 Claude Code 的 Verification Agent。" homepage: https://github.com/zhaobod1/huo15-openclaw-enhance metadata: { "openclaw": { "emoji": "✅", "requires": { "bins": [] } } } --- # 验证模式 (Verify Mode) 系统性地验证工作成果或假设。 ## 使用时机 ✅ **使用此技能当：** - 完成一段代码修改后需要验证 - "帮我检查一下这个改动有没有问题" - 需要运行测试并分析结果 - 验证一个假设或排查一个 bug ❌ **不要使用当：** - 只是普通的代码 review（直接 review 即可） - 改动很小且显而易见正确 ## 验证流程 ### 1. 确定验证目标 - 要验证什么？（功能正确性 / 性能 / 安全 / 兼容性） - 成功标准是什么？ - 已知的风险点？ ### 2. 静态检查 - 读改动的代码，检查逻辑正确性 - 检查边界条件和错误处理 - 检查是否引入了安全漏洞（注入、XSS 等） - 检查是否破坏了已有接口或行为 ### 3. 动态验证 - 运行已有的测试套件 - 如果没有测试，手动构造测试场景 - 检查构建是否通过 - 如果有 linter/formatter，运行一下 ### 4. 输出验证报告 ``` ## 验证报告 ### 验证对象 [简要描述被验证的改动/假设] ### 检查清单 - [x] 逻辑正确性: 通过/发现问题 - [x] 边界条件: ... - [x] 测试结果: X pass / Y fail - [x] 构建状态: 通过/失败 - [ ] 安全检查: ... ### 发现的问题 1. [问题描述 + 严重程度 + 建议修复方式] ### 结论 ✅ 验证通过 / ⚠️ 有待修复的问题 / ❌ 验证失败 ``` ## 核心原则 - **全面但聚焦** — 覆盖主要风险点，但不要检查无关的东西 - **给出证据** — "测试 X 通过了"、"第 42 行可能有空指针"，不要泛泛地说"看起来没问题" - **区分严重程度** — 阻塞性问题 vs 建议性改进 - **可操作** — 发现问题就给出具体修复建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lig-8max/secure-linter (version 0.1.0)

--- name: secure_linter description: 安全代码 Linter：扫描漏洞、密钥泄露和代码异味 version: 0.1.0 metadata: {"openclaw": {"requires": {"bins": []}}} --- # 代码审查助手 当用户要求审查代码时，按以下步骤： 1. **理解语言**：识别代码语言（JavaScript/Python/Go/Rust等） 2. **检查常见漏洞**： - SQL 注入 - XSS - 硬编码密钥 - 边界检查缺失 3. **代码质量**： - 重复代码 - 过长函数 - 魔法数字 4. **输出格式**： - 问题行号 - 风险等级（高/中/低） - 修复建议 使用 `browser` 或 `web_fetch` 查看相关安全指南（如 OWASP）以补充知识。

path: README.md
content: # Secure Linter Skill

安全代码静态分析工具，集成到 OpenClaw Agent 中自动扫描漏洞。

## 功能

- 检测 SQL 注入、XSS、硬编码密钥
- 识别代码异味：重复逻辑、过长函数、魔法数字
- 输出：行号、风险等级（高/中/低）、修复建议

## 使用示例

**用户**：请审查以下 Python 代码安全问题：
```python
def process(user_input):
    sql = "SELECT * FROM users WHERE id = " + user_input
    cursor.execute(sql)
    return cursor.fetchall()
```

**Agent**（使用本技能）输出：
- 第 2 行：SQL 拼接 → SQL 注入风险（高）
  - 修复建议：使用参数化查询（cursor.execute("SELECT * FROM users WHERE id = %s", (user_input,)））

## 配置

无需特殊配置，开箱即用。

## 限制

- 不执行实际代码（仅静态分析）
- 对于复杂业务逻辑可能需要人工复核

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: patches429/giggle-video (version 0.1.0)

--- name: giggle-video version: "0.1.0" description: AI video generation via giggle.pro and other platforms. Submit prompts, track progress, and retrieve generated videos. homepage: https://giggle.pro metadata: { "openclaw": { "emoji": "🎬", "requires": { "env": ["GIGGLE_API_KEY"] }, "primaryEnv": "GIGGLE_API_KEY", }, } --- # Skill: giggle-video（接口定义） > **接口层**：定义触发条件、输入参数、执行阶段、输出规范和错误策略，与具体视频生成服务无关。 > 各平台的 API 端点和认证方式见 `impl/` 目录： > > - `impl/giggle.md` — giggle.pro 实现 > > 接入其他视频生成平台时，只需添加对应 `impl/{platform}.md`，本文件无需修改。 ## 概述 调用 AI 视频生成服务，创建和追踪视频生成任务。 ## 触发条件 - 用户请求生成视频 - 用户提供脚本/创意并要求制作 - 用户查询已提交的视频生成进度 ## 输入参数 | 参数 | 类型 | 必填 | 说明 | | ------------ | ------ | ---- | -------------------------------------------- | | prompt | string | 是 | 视频描述/提示词 | | platform | string | 否 | 目标平台 id（见 impl/ 目录），默认 `giggle` | | style | string | 否 | 视觉风格（cinematic / anime / realistic 等） | | duration | number | 否 | 目标时长（秒），默认 `15` | | aspect_ratio | string | 否 | 画面比例，默认 `16:9` | | resolution | string | 否 | 分辨率，默认 `1080p` | ## 执行阶段 ``` 阶段 1 — 参数确认 收集并验证所有输入参数，缺失项使用默认值。 单次调用费用超过 $5 时，向用户告知预估费用并获取确认。 阶段 2 — 提交任务 根据 platform 参数加载 impl/{platform}.md。 调用实现层提交生成任务，获取 task_id。 阶段 3 — 进度追踪 轮询任务状态，默认间隔 10s，超时上限 600s。 超时后记录 task_id，告知用户可稍后查询。 阶段 4 — 结果处理 成功：提取视频 URL，保存元数据。 失败：记录错误码，建议用户调整参数。 ``` ## 输出 ```json { "taskId": "platform_task_id", "platform": "giggle", "status": "completed | failed | timeout", "videoUrl": "https://...", "metadata": { "prompt": "...", "style": "cinematic", "duration": 15, "aspect_ratio": "16:9", "resolution": "1080p", "generationSeconds": 120 } } ``` 存档路径：`shared/outputs/director/{date}_{project}_video.json` ## 错误处理策略 | 错误类型 | 处理方式 | | --------------- | ------------------------------------ | | 限流（429） | 等待 60s 后重试，最多 3 次 | | 参数错误（400） | 提示用户修改 prompt 或参数 | | 服务故障（5xx） | 记录错误，通知用户稍后重试 | | 轮询超时 | 保存 task_id，告知用户可稍后查询进度 | ## 安全约束 - 只访问 impl/ 中定义的白名单域名 - 调用前确认用户意图 - 所有 API 调用由网关记录到审计日志


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alvinecarn/call-o3-llm (version 0.1.0)

--- name: call-o3-llm description: AI agent for call o3 llm tasks --- # Call O3 Llm ## Overview This skill provides specialized capabilities for call o3 llm. ## Instructions # 1. Primary Rule: Output Language**This is the highest-priority directive and must be followed unconditionally.**1. **Language-Matching Principle:** All of your outputs—including every part of the meeting minutes, replies to superior AI models (`message`), interim summaries, and creative ideas—**must** use exactly the same language as the one used in the user’s initial challenge.2. **Explicit Language Instructions:** If the user explicitly specifies a response language (e.g., “in English please”, “请用中文回答”), that instruction has the highest priority and **must** be followed strictly.3. **No Language Inference:** * It is **strictly forbidden** to decide your response language based on the language of this system prompt. * It is **strictly forbidden** to decide your response language based on the language used in the meeting minutes.---# 2. Role & IdentityYou are a creative expert and a core member of a top-tier creative team. Your codename is **$SHOW_NAME$**.# 3. Context of InteractionYou are participating in a brainstorming meeting chaired by your superior—the Chief Creative Officer (CCO). You are not working independently; you are part of the creative team.The entire context of the meeting, historical discussions, and current agenda items are recorded in real time in the **meeting minutes**. These minutes are your sole source of information and memory, and you must adhere to them.# 4. Core Instructions1. **Understand the Context:** Before responding, you **must** carefully read the attached meeting minutes to fully grasp the current discussion progress, other members’ viewpoints, and the overall goal of the meeting.2. **Stay Task-Focused:** Your task is to respond **only to the specific question or task** posed to you by the CCO in this turn. Do not drift into topics unrelated to the current task.3. **Embrace the Role:** Remain professional and creative. Your response will be treated as raw creative material for the CCO to evaluate and synthesize. ## Usage Notes - This skill is based on the call_o3_llm agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/first-aid (version 3.0.0)

--- name: "first-aid" version: "3.0.0" description: "First aid reference guide with emergency procedures and kit checklists. Use when looking up first aid instructions." author: "BytesAgain" homepage: "https://bytesagain.com" --- # first-aid First aid reference guide with emergency procedures and kit checklists. Use when looking up first aid instructions. ## Commands ### `guide` ```bash scripts/script.sh guide <condition> ``` ### `emergency` ```bash scripts/script.sh emergency ``` ### `kit` ```bash scripts/script.sh kit ``` ### `list` ```bash scripts/script.sh list ``` ### `search` ```bash scripts/script.sh search <keyword> ``` ### `quiz` ```bash scripts/script.sh quiz ``` ## Data Storage Data stored in `~/.local/share/first-aid/`. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dashiming/pansmodel-manager (version 1.0.1)

--- name: PansModel-Manager displayName: PansModel Manager version: 1.0.1 description: | PanSclaw 模型管理器 - 管理自定义模型接入和切换。 支持添加、删除、切换、列出模型配置。 触发关键词: 模型管理, 切换模型, 添加模型, 删除模型, 模型列表, 配置模型, 更换模型, 接入模型, 模型设置 metadata: {"openclaw": {"emoji": "🤖"}} --- # 模型管理器 ## 功能 管理 PanSclaw 的模型配置，支持： - 添加自定义模型提供商 - 切换默认模型 - 列出已配置模型 - 删除模型配置 ## 使用方法 ### 添加模型 ``` /模型 添加 厂商=<name> 地址=<url> 密钥=<key> 模型=<model_id> ``` 示例： ``` /模型 添加 厂商=deepseek 地址=https://api.deepseek.com/v1 密钥=sk-xxx 模型=deepseek-chat ``` ### 切换模型 ``` /模型 切换 <厂商>/<模型名> ``` 示例： ``` /模型 切换 deepseek/deepseek-chat ``` ### 列出模型 ``` /模型 列表 ``` ### 删除模型 ``` /模型 删除 <厂商> ``` ## 支持的 API 类型 | 厂商 | API 适配器 | |------|-----------| | openai | openai-completions | | anthropic | anthropic-messages | | deepseek | openai-completions | | moonshot | openai-completions | | zhipu | openai-completions | | qwen | openai-completions | | ollama | ollama | ## 配置文件位置 `~/.openclaw-pansclaw/openclaw.json` 配置修改后需要重启 Gateway 生效。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: luaqnyin/adaptive-rag-engine (version 1.0.0)

--- name: adaptive-rag-engine version: 1.0.0 description: "Adaptive RAG 引擎 — 从线性检索到自主认知循环。集成胶囊预筛选、智能路由、CRAG纠错、L3校验。当需要搜索记忆/检索信息/回答复杂问题时触发。关键词：RAG、检索、记忆搜索、向量检索、Agentic RAG、CRAG。" metadata: requires: bins: ["python3"] --- # 🧠 Adaptive RAG Engine v1.0 > **不是管道，是认知循环。** ## 核心能力 | 能力 | 说明 | 触发方式 | |------|------|---------| | **Adaptive Router** | 判断是否需要检索，简单问题直接答 | 自动（每次 memory_search 前） | | **Capsule Pre-filter** | 42个胶囊标题预匹配，缩小范围 90% | 自动（Router 通过后） | | **Vector Search** | bge-m3 向量检索 Top-20 | memory_search 工具 | | **LLM Re-rank** | 语义重排序 Top-20 → Top-5 | 检索后自动 | | **CRAG Evaluator** | 质量评估 + 低分补搜 | 检索后自动 | | **L3 Gatekeeper** | 输出前与核心洞察校验 | 生成前自动 | | **Memory Bridge** | Active Memory ↔ Phoenix 双向桥接 | 对话结束时 | ## 使用流程 ### 对于 CEO（小鸟文书） 此 Skill 是**协议层**，不需要显式调用。它通过以下方式生效： 1. **读取协议文件**: `rules/adaptive-rag-protocol.md` — 获取完整决策树 2. **读取胶囊索引**: `memory/topics/.capsule-index.json` — 获取 42 个胶囊元数据 3. **按决策树执行**: 每次需要记忆时，走 Router → Pre-filter → Search → Rank → CRAG → Generate → Verify ### 对于 SubAgent SubAgent 在执行任务时： 1. 先判断任务类型 → 决定是否需要检索 2. 需要检索时 → 先做胶囊预筛选 3. 检索结果 → 自行判断质量（CRAG 思维） 4. 输出前 → 自检是否与已知信息矛盾 ## 关键文件 | 文件 | 用途 | |------|------| | `rules/adaptive-rag-protocol.md` | 完整协议（决策树/分类/bridge/CRAG/L3） | | `memory/topics/.capsule-index.json` | 42个胶囊的结构化索引 | | `scripts/build-capsule-index.py` | 重建胶囊索引脚本 | | `scripts/rag-evaluate.py` | CRAG 质量评估脚本 | ## 快速命令 ```bash # 重建胶囊索引 python3 ~/.openclaw/workspace/skills/adaptive-rag-engine/scripts/build-capsule-index.py # 评估检索质量 python3 ~/.openclaw/workspace/skills/adaptive-rag-engine/scripts/rag-evaluate.py --query "xxx" --results "result1, result2" ``` ## 与其他 Skill 的关系 - **dynamic-rag-capsule** — Context 管理层面的胶囊化（对话太长时压缩） - **phoenix-memory** — 记忆存储和衰减管理（四层架构） - **本 Skill** — 检索策略和质量控制（怎么搜、搜到后怎么办） 三者关系：**phoenix-memory 是仓库，dynamic-rag-capsule 是打包器，adaptive-rag-engine 是导航仪**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: maxdong-max/douyin-download-china (version 1.0.0)

--- name: "douyin-download" description: "抖音视频下载工具。当用户要求下载抖音（豆包）短视频时使用此 skill。支持短链接（如 v.douyin.com/xxx）和完整视频页面链接，自动解析并下载到本地。下载视频为 720p 带水印版本。" agent_created: true --- # douyin-download Download Douyin (TikTok China) videos to local disk without needing yt-dlp, cookies, or login. ## Workflow Execute all steps in order. ### Step 1 — Resolve Short URL If the URL is a short link (`v.douyin.com/xxx`), resolve it first: ```bash curl -sI "https://v.douyin.com/<ID>/" | grep -i location ``` Extract the full URL from the `location:` header. The numeric ID in `/video/<ID>/` is the `video_id_str` (e.g. `7634389140783615232`). ### Step 2 — Fetch Mobile Page & Extract video_id Fetch the mobile Douyin page and extract the internal `video_id`: ```bash curl -sL "https://www.iesdouyin.com/share/video/<video_id_str>/" \ -H "User-Agent: Mozilla/5.0 (iPhone; CPU iPhone OS 16_0 like Mac OS X) AppleWebKit/605.1.15" \ -H "Referer: https://www.douyin.com/" \ -o /tmp/dy_page.html ``` Extract `video_id` from the HTML: ```bash grep -o 'video_id[^"]*"[^"]*"' /tmp/dy_page.html ``` This returns something like [REDACTED_SECRET]. The part after `=` is the `internal_video_id`. ### Step 3 — Download Video Construct the snssdk play URL and download: ```bash curl -L "https://aweme.snssdk.com/aweme/v1/playwm/?video_id=<internal_video_id>&ratio=720p&line=0" \ -H "User-Agent: Mozilla/5.0 (iPhone; CPU iPhone OS 16_0 like Mac OS X) AppleWebKit/605.1.15" \ -H "Referer: https://www.douyin.com/" \ -o ~/Downloads/<output_filename>.mp4 ``` - `playwm` = 720p with Douyin watermark - To get no-watermark version: replace `playwm` with `play` and append `&watermark=0` (may need additional signature handling) - Save to `~/Downloads/` using the video title as filename ### Step 4 — Verify Check file size and format: ```bash ls -lh ~/Downloads/<output_filename>.mp4 && file ~/Downloads/<output_filename>.mp4 ``` ## Key Notes - **No login required** — the mobile endpoint (`iesdouyin.com`) works without authentication - **Download speed** — typically reaches 10–15 MB/s on stable connections; a 100MB video finishes in ~10 seconds - **If download fails** with 403/redirect: retry with a fresh `curl -L` (follow redirects) and verify the `video_id` is correct - **Watermark** — `playwm` always includes Douyin watermark; getting no-watermark requires the `play` endpoint with signed parameters (complex, not covered here) - **Supported URLs**: `v.douyin.com/xxx` short links and `www.iesdouyin.com/share/video/<ID>/` direct links ## Reference See `references/douyin_api.md` for detailed endpoint documentation and troubleshooting.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhao1263445468/pdf-to-word (version 1.0.0)

--- name: pdf-to-word description: �� PDF �ļ�ת��Ϊ Word �ĵ���.docx�� version: 1.0.0 author: YourName parameters: - name: file description: Ҫת���� PDF �ļ���֧���ϴ����ṩ·���� required: true type: file - name: output_format description: �����ʽ��Ŀǰ��֧�� docx required: false type: string default: docx --- # PDF ת Word ���� ## ���� �����û��ϴ��� PDF �ļ�������ת��Ϊ�ɱ༭�� Word �ĵ���.docx����������ת������ļ��� ## ʹ�÷�ʽ 1. �û��������з��� PDF �ļ���������ָ�������� PDF ת�� Word�� �� ��������ļ�ת��Ϊ Word ��ʽ���� 2. �����Զ����ú�̨�ű�����ת���� 3. ת����ɺ󣬷��� Word �ļ����������ӻ�ֱ�����ļ���ʽ���͸��û��� ## ע������ - ��Ҫ Python ����������װ `pdf2docx` �⡣ - ת������ PDF ������Ҫ�����ӣ������ĵȴ��� - ��� PDF �������ӱ����ɨ��ͼƬ��ת��Ч���������ޣ�������� OCR ����ʹ�ã��� ## ʾ�� �û���`��������ͬ PDF ת�� Word �ļ�`�����ϴ��ļ��� AI�����ü��� �� ����ת����� Word �ļ���


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

--- name: ifeng-hot-trend description: 注册“凤凰网”热门技能；当需要访问或自动化凤凰网相关内容时调用。 homepage: `https://www.ifeng.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 凤凰网 用途与边界 - 面向公开新闻页与频道页的检索与内容摘要 - 不提供接口逆向或批量抓取能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.ifeng.com/ - 要闻/财经/科技频道：频道入口 - 搜索入口：站内搜索 常见任务 - 指定关键词的新闻集合摘要（标题、来源、发布时间、互动指标） - 频道页的热点条目与题材分布统计 - 深度报道的段落摘要与要点提取 数据字段 - 标题、来源、作者（若标注）、发布时间、正文摘要、标签、链接 - 互动指标（评论数/点赞数，如公开展示） - 频道名称、采集时间、来源链接 自动化要点 - 动态渲染，需等待页面加载完成后解析 - 频率控制，不进行批量抓取；结果标注采集时间 - 对长文执行段落摘要，避免过度采集 示例流程 - 关键词摘要：输入关键词 → 搜索 → 抽取条目 → 输出摘要清单 - 频道统计：访问频道页 → 抽取热点 → 题材分布与来源统计 - 长文要点：访问文章页 → 抽取小标题与段落 → 输出要点列表 合规提示 - 遵守平台规则，不进行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)