OpenClaw/clawhub-security-signals huggingface dataset:
Skill: foxcatt/idm-downloader (version 1.0.0)

--- name: idm-downloader description: Download large files using IDM (Internet Download Manager) on Windows. Use when user wants to download files via URL and prefers IDM, or explicitly requests IDM downloading. Ideal for large files (hundreds of MB to several GB). Triggers on requests like "download this file with IDM", "use IDM to download", "IDM下载", "用IDM下载文件". --- # IDM Downloader Use IDM (Internet Download Manager) to download large files via COM interface or command line on Windows. Recommended for large files such as datasets, videos, disk images, and archives where download resumption and acceleration are beneficial. ## Quick Usage ```bash # Download a file to specified directory python scripts/download_idm.py <url> [output_directory] [filename] # Download to current directory python scripts/download_idm.py <url> # Download with custom filename python scripts/download_idm.py <url> <output_directory> <custom_filename> ``` ## Features 1. **COM Interface (Primary)**: Uses IDM's COM automation (`IDMan.CIDWMControl.1`) for reliable downloads 2. **Command Line Fallback**: Uses `IDMan.exe` with `/n` flag for immediate download 3. **Registry Search**: Finds IDM installation from Windows Registry if not at default paths 4. **Auto Filename**: Extracts filename from URL if not specified ## IDM Command Line IDMan.exe parameters: - `/d <URL>` - URL to download - `/p <path>` - Local directory to save file - `/f <filename>` - Filename to save as - `/n` - Start download immediately (no prompt) ## Requirements ### System Requirements - **OS**: Windows (IDM is a Windows-only application) - **IDM**: Internet Download Manager installed on the system ### Python Dependencies | Dependency | Purpose | Install Command | |------------|---------|-----------------| | Python 3.x | Runtime | - | | pywin32 | COM interface & registry access | `pip install pywin32` | | win32com.client | IDM COM automation | (included in pywin32) | | pythoncom | COM initialization | (included in pywin32) | | winreg | Windows Registry access | (built-in, Windows only) | > **Note**: `pywin32` is optional but recommended for better compatibility and COM interface support. The script will fall back to command-line mode if not installed. ## Notes - IDM must be installed on the system - If COM fails, automatically falls back to command line - Downloads start immediately with `/n` flag


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jean5773/unified-video-lyrics (version 1.2.0)

--- name: unified-video-lyrics description: | 短视频创作全能工具。参照首个视频统一角色画风生成后续视频。 触发：写剧本、写歌词、创作视频、制作短视频、动画脚本、写歌。 --- # 🎬 短视频创作全能指南 ## 核心能力 | 功能 | 说明 | |---|---| | 🎬 剧本创作 | 6镜头分镜脚本 | | 🎵 歌词创作 | Rap/流行/民谣，支持结构标签 | | 📹 视频生成 | 首个视频作为参考，统一后续视频风格 | | 🎵 配音乐 | ACE Music 免费生成完整歌曲 | | 📱 多平台 | 抖音/快手/B站/YouTube/TikTok | | 🔧 视频合并 | FFmpeg 剪辑/合并 | | 🎨 风格指南 | 二次元萌系/赛璐璐风格 | --- ## 核心工作流 ``` 1. 上传/提供首个参考视频 2. 分析角色外观、画风、色调 3. 创作剧本+歌词 4. 生成6个统一风格镜头 5. 合并成完整视频 6. 生成背景音乐 ``` --- ## 首创功能：统一角色画风 ### 工作流程 ``` [首个参考视频] → [分析角色+画风] → [生成后续6个视频] ↓ 保持一致： - 主角外观 - 画风（2D日漫/3D/写实） - 色调（明亮/灰暗/暖色/冷色） - 光照风格 ``` ### 首个视频分析要点 收到参考视频后，使用 videos_understand 分析： 1. **主角外观** - 品种/眼睛/毛发/服装 2. **画风** - 2D动漫/3D/写实 3. **色调** - 明亮/灰暗/饱和度 4. **光照** - 室内/自然光/柔光 --- ## 二次元萌系风格（赛璐璐） ### 风格特点 | 特点 | 描述 | |------|------| | 画风 | 日漫萌系 (Kawaii Anime) | | 渲染 | 赛璐璐 (Cell Shading) - 清晰阴影边缘 | | 线条 | 清晰均匀黑色勾线 | | 色调 | 低饱和度灰白+蓝色 | | 氛围 | 治愈、无辜、略带忧伤 | ### 角色设计 - 巨大圆眼睛 - 圆润脸庞 - 简化身体比例 - 拟人化（直立行走、穿戴服饰） ### 表情特点 - 巨大黑色瞳孔 - 略微下垂眉毛 - 倒V形嘴角 - 无辜委屈眼神 ### 视频提示词模板 ``` [角色描述], anime style, 2D Japanese animation, cell shading, clean lineart, [色调描述], [光照描述], cinematic ``` --- ## 剧本格式 ```markdown # 《标题》 类型：治愈/搞笑/情感/职场 时长：XX秒 ## 镜头 1 [X秒] - 参考视频 【画面】描述 ## 镜头 2-6 [各X秒] - 统一风格 【画面】描述 【转场】→ ``` --- ## 歌词格式 ```markdown # 《标题》 情绪：[欢快/治愈/Rap/灰暗] 【主歌】 歌词... 【副歌】 高潮句！ ``` --- ## 音乐生成 ### ACE Music（免费） - 无积分限制 - 支持人声+歌词 --- ## 视频合并 ```bash # 合并视频 ffmpeg -i input1.mp4 -i input2.mp4 -filter_complex "[0:v][1:v]concat=n=2:v=1:a=0" output.mp4 # 添加音频 ffmpeg -i video.mp4 -i audio.mp3 -c:v copy -c:a aac output.mp4 ``` --- ## 详细参考 - references/characters.md - 角色模板 - references/lyrics.md - 歌词技巧 - references/music.md - 配乐指南 - references/platforms.md - 多平台 - references/character-consistency.md - 角色一致性 - references/anime-style.md - 二次元萌系风格指南

path: _meta.json
content: {
  "name": "unified-video-lyrics",
  "version": "1.2.0",
  "description": "短视频创作全能工具 - 参照首个视频统一角色画风+二次元萌系风格",
  "keywords": ["短视频", "剧本", "歌词", "视频生成", "角色一致性", "二次元", "赛璐璐", "FFmpeg"],
  "trigger_words": ["写剧本", "写歌词", "创作视频", "制作短视频", "动画脚本", "写歌", "统一风格", "参照视频", "二次元", "萌系"]
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/volcengine-storage-tos (version 1.0.0)

--- name: volcengine-storage-tos description: Object storage operations for Volcengine TOS. Use when users need upload/download/sync, bucket policy checks, signed URLs, or storage troubleshooting. --- # volcengine-storage-tos Manage TOS buckets and objects with explicit path mapping and permission verification. ## Execution Checklist 1. Confirm bucket, region, and object paths. 2. Validate auth and bucket policy. 3. Execute upload/download/sync task. 4. Return result manifest with object keys and URLs. ## Safety Rules - Avoid destructive deletes without explicit confirmation inputs. - Preserve metadata and content type on uploads. - Provide checksum or size verification where possible. ## References - `references/sources.md`

path: agents/openai.yaml
content: interface:
  display_name: "Volcengine TOS Storage"
  short_description: "Manage buckets objects sync and access"
  default_prompt: "Use $volcengine-storage-tos to perform this object storage operation."


path: references/sources.md
content: # Sources

- Volcengine TOS API/CLI documentation
- Volcengine IAM policy and signed URL docs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 850media/frameo (version 1.0.0)

--- name: frameo description: Control Frameo digital photo frames. Use when: sending photos to Frameo frames, listing paired frames, controlling frame via ADB (brightness, screen, navigation). Supports both cloud API (read-only) and ADB (full control including photo upload). Requires either Frameo account credentials or ADB access to the frame. --- # Frameo Digital Photo Frame Control Control Frameo photo frames via cloud API or ADB. ## Methods ### Method 1: Cloud API (Limited) - ✅ List paired frames - ✅ Get account info - ❌ Send photos (requires FCM/Firebase) ### Method 2: ADB (Full Control) - ✅ Push photos directly - ✅ Control brightness - ✅ Toggle screen - ✅ Navigate photos ## Quick Start ### Cloud API Setup 1. Install: `pip3 install requests pillow` 2. Get Bearer token from Frameo app traffic (Proxyman/Charles) 3. Save token: `echo '{"access_token": "YOUR_TOKEN"}' > ~/.frameo_token` 4. Run: `python3 scripts/frameo_client.py --frames` ### ADB Setup (Recommended) 1. Enable Developer Options on Frameo (Settings → About → tap Build 7x) 2. Enable USB Debugging 3. Connect USB-C data cable to computer 4. Run: `adb tcpip 5555` to enable wireless 5. Disconnect USB, connect wireless: `adb connect <frame-ip>:5555` ## Usage Examples ### List Frames (Cloud API) ```bash python3 scripts/frameo_client.py --frames ``` ### Send Photo (ADB) ```bash adb push photo.jpg /sdcard/DCIM/ # Or to Frameo's photo directory: adb push photo.jpg /sdcard/Frameo/ ``` ### Control Frame (ADB) ```bash # Screen on/off adb shell input keyevent 26 # Set brightness (0-255) adb shell settings put system screen_brightness 128 # Next photo (swipe right) adb shell input swipe 800 500 200 500 # Previous photo (swipe left) adb shell input swipe 200 500 800 500 ``` ## Remote Access via SSH Relay If frame is on local network and agent is remote: ```bash ssh user@local-mac "adb push /tmp/photo.jpg /sdcard/DCIM/" ``` ## References - `references/api-endpoints.md` - Frameo cloud API endpoints - `references/adb-commands.md` - Common ADB commands for Frameo ## Troubleshooting ### Token Expired (401) Frameo tokens expire in ~5 minutes. Get fresh token from Proxyman. ### ADB Connection Refused Wireless ADB not enabled. Need USB cable first to run `adb tcpip 5555`. ### USB Cable Not Detected Ensure using a **data cable**, not charge-only. Data cables are usually thicker.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: lt8899789/chou-fei (version 1.0.35)

--- version: 1.0.35 --- # 臭肺·吐纳魄 (Chou Fei - Respiration) > **七魄之七·臭肺** > 职掌：资源获取、信息摄取、算力调度 --- ## 技能简介 「臭肺·吐纳魄」是贫道的资源摄取模块，职掌资源获取与信息代谢。 **核心职责**： - 获取外部资源 - 摄取有效信息 - 调度算力分配 --- ## 技能ID ``` chou-fei ``` --- ## 能力清单 ### 1. 资源获取 (fetch) 获取外部资源。 **输入**：`resource` (object) - 资源描述 ```yaml resource: type: url|file|api|data location: 资源位置 format: 期望格式 ``` **输出**： ```yaml fetched: content: 获取的内容 size: 大小(bytes) format: 实际格式 cached: 是否缓存 ``` --- ### 2. 信息摄取 (absorb) 从内容中提取有价值信息。 **输入**：`content` (string) - 内容文本 **输出**： ```yaml absorbed: summary: 内容摘要 keyPoints: 关键点列表 entities: 识别的实体 relevance: 相关性评分 ``` --- ### 3. 算力调度 (allocate) 调度算力资源。 **输入**：`request` (object) - 算力请求 ```yaml request: task: 任务类型 priority: 优先级 estimatedLoad: 预估负载 ``` **输出**： ```yaml allocation: allocated: 分配的算力 queuePosition: 队列位置 estimatedTime: 预估时间 ``` --- --- ## 聚合技能 本魄作为吐纳中枢，资源摄取与信息代谢： | 现有技能 | 调用方式 | 整合说明 | |---------|---------|---------| | `weather` | 调用 | 天气资源获取 | | `bodhi-three-hun-and-seven-po` | 元技能 | 三魂七魄根基，协调各魄 | --- ## 魂魄注解 臭肺吐纳，新陈代谢——资源摄取，能量流转。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: scikkk/senseaudio-voice (version 1.0.0)

--- name: senseaudio-voice description: Guide for SenseAudio voice selection, plan-level voice entitlement checks, and cloned voice usage constraints in TTS calls. Use this whenever user asks why a voice_id is unavailable, how to use cloned voices, or how to configure pronunciation/dictionary behavior for clone voices. --- # SenseAudio Voice Use this skill for voice inventory, entitlement, and clone-related integration behavior. ## Read First - `references/voice.md` ## Workflow 1. Confirm target `voice_id` and user account tier. 2. Verify whether the requested voice is entitlement-limited. 3. For clone use cases, ensure platform-side cloning is completed first. 4. Validate dictionary/model constraints before enabling polyphone overrides. 5. Provide fallback voice strategy when requested voice is unavailable.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vincentlau2046-sudo/tts-cosyvoice (version 1.2.0)

--- name: tts-cosyvoice version: 1.0.0 description: "Text-to-Speech via Edge TTS (Microsoft Azure voices). Free, no API key needed, supports 100+ voices in 50+ languages including Chinese and English." metadata: { "openclaw": { "emoji": "🔊", "requires": { "bins": ["python3"] } } } tags: ["tts", "text-to-speech", "edge-tts", "voice", "audio"] --- # Edge TTS — Text-to-Speech Use to convert text to speech using Microsoft Edge's free TTS service. No API key required. ## Voices ### Chinese (Recommended) | Voice | Gender | Style | |-------|--------|-------| | `zh-CN-XiaoxiaoNeural` | Female | Warm, natural (default) | | `zh-CN-YunxiNeural` | Male | Young, casual | | `zh-CN-YunjianNeural` | Male | Professional, news | | `zh-CN-XiaoyiNeural` | Female | Cute, young | | `zh-CN-YunyangNeural` | Male | News broadcaster | | `zh-CN-XiaochenNeural` | Female | Child | | `zh-TW-HsiaoChenNeural` | Female | Traditional Chinese | ### English | Voice | Gender | Style | |-------|--------|-------| | `en-US-JennyNeural` | Female | Friendly (default) | | `en-US-GuyNeural` | Male | Professional | | `en-GB-SoniaNeural` | Female | British | ## Script ```bash {baseDir}/scripts/tts.py --text "Hello world" --output /tmp/output.mp3 ``` ### Options | Option | Default | Description | |--------|---------|-------------| | `--text` | (required) | Text to speak | | `--voice` | zh-CN-XiaoxiaoNeural | Voice ID | | `--output` | /tmp/tts_output.mp3 | Output file path | | `--rate` | 0% | Speed adjustment (-50% to +100%) | | `--pitch` | 0Hz | Pitch adjustment (-50Hz to +50Hz) | | `--volume` | 0% | Volume adjustment (-100% to +100%) | | `--file` | | Read text from file instead of --text | ### Examples ```bash # Basic Chinese TTS {baseDir}/scripts/tts.py --text "你好，我是Nova" --output /tmp/hello.mp3 # Male voice, faster speech {baseDir}/scripts/tts.py --text "Hello world" --voice en-US-GuyNeural --rate +20% --output /tmp/fast.mp3 # Read from file {baseDir}/scripts/tts.py --file script.txt --output /tmp/script.mp3 ``` ## Available Voices List all voices: ```bash {baseDir}/scripts/tts.py --list-voices zh {baseDir}/scripts/tts.py --list-voices en {baseDir}/scripts/tts.py --list-voices ja ``` ## Dependencies - `edge-tts` (pip install) - No API key needed — uses Microsoft Edge's free TTS service - Requires internet connection


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: laolaoqi/linux-port-resolver (version 1.1.0)

--- name: linux-port-resolver description: Resolve Linux port conflicts (EADDRINUSE/bind failed) with precision. Detect occupying process, free the port, and prevent recurrence. Use when a service fails to start with "port already in use", EADDRINUSE, bind failed, or when you need to check what's on a specific port. --- # Linux Port Resolver Quick fix port conflicts on Linux without guesswork. ## Quick Commands ```bash # Find what's on a port ss -tlnp | grep :PORT # Alternative if ss not available netstat -tlnp 2>/dev/null | grep :PORT # Also check IPv6 ss -tlnp | grep :PORT ``` ## Resolution Steps 1. **Identify the process** ```bash ss -tlnp | grep ":<PORT> " # Output: LISTEN 0 128 0.0.0.0:PORT 0.0.0.0:* users:(("process",PID,)) ``` 2. **Verify the PID** - don't kill system processes ```bash ps -p PID -o pid,ppid,cmd,user ``` 3. **Kill gracefully, escalate if needed** ```bash kill PID # SIGTERM - graceful sleep 2 && ss -tlnp | grep ":<PORT> " # verify kill -9 PID # SIGKILL - force, last resort ``` 4. **Or reconfigure** - change the port in the service config instead - systemd: edit `/etc/systemd/system/SERVICE.service`, change port, `systemctl daemon-reload && systemctl restart SERVICE` - Docker: change `ports:` mapping in docker-compose.yml - Custom app: check config file or env var for port setting 5. **Prevent recurrence** with ExecStartPre in systemd ``` [Service] ExecStartPre=/bin/sh -c 'kill -9 $(ss -tlnp | grep ":PORT " | grep -oP "pid=\\K\\d+") 2>/dev/null; true' ``` ## Safety Checklist - [ ] Confirmed the port is actually in use - [ ] Identified the PID correctly - [ ] PID is not PID 1 or a critical system daemon - [ ] Verified there's no other instance still running - [ ] Checked both IPv4 and IPv6 listeners --- ## 📢 Need Help? Need OpenClaw deployed on your server? I offer managed deployment & hosting: - 📧 [REDACTED_SECRET] - 🏯 颐和园 OpenClaw 运维


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: m-maciver/driftwatch (version 1.0.0)

--- name: driftwatch description: Monitor agent identity drift using git history. Detects when AI agents quietly modify their own SOUL.md, IDENTITY.md, AGENTS.md, or memory files — autonomy expansion, constraint softening, or self-modification. Read-only, safe to run anytime. Use when you want to audit agent identity changes, verify no unauthorized modifications have occurred, or run a weekly hygiene check on your agent stack. --- # DriftWatch 🔍 **Agent Identity Drift Monitor for OpenClaw workspaces** Uses your workspace's existing git history to track changes to agent identity files. For each change it classifies severity, optionally runs LLM semantic analysis, and outputs a human-readable markdown report. ## Usage ```bash # Full report, last 30 days (heuristic only, fast) python3 skills/driftwatch/driftwatch.py --no-llm --days 30 # With LLM semantic analysis (requires ANTHROPIC_API_KEY) python3 skills/driftwatch/driftwatch.py --days 30 # Last 7 days python3 skills/driftwatch/driftwatch.py --no-llm --days 7 # Cron/heartbeat mode: silent unless concerns found python3 skills/driftwatch/driftwatch.py --cron --days 7 ``` ## What it tracks - `SOUL.md` — core personality and values - `IDENTITY.md` — agent name, creature, vibe - `AGENTS.md` — operational rules and protocols - `USER.md` — what agents know about their human - `TOOLS.md` — tool and access notes - `agents/*/MEMORY-INDEX.md` — per-agent active context ## Output Writes a markdown report to the skill directory. Flags: - 🟡 Medium: human should review - 🔴 High: potential concern — review before next agent session ## Add to weekly heartbeat ```markdown ## Weekly Drift Check (Mondays) Run: python3 skills/driftwatch/driftwatch.py --cron --days 7 ``` **Read-only. Does not modify any files.**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: huyi9531/social-parser (version 1.0.1)

--- name: "social-parser" description: "解析抖音视频和小红书笔记，提取标题、封面、描述、标签、作者等核心信息。当用户想解析、获取、抓取、提取抖音视频数据、小红书笔记内容，或粘贴了抖音/小红书链接希望查看内容详情时调用。常见表达：'帮我解析这个抖音链接'、'获取小红书笔记内容'、'抓取视频信息'、'提取这条小红书的标题标签'、'分析这个视频'。" --- # 社交媒体作品解析指南 ## 能力说明 通过 `gnomic` CLI 工具，可以解析抖音视频和小红书笔记链接，提取作品的核心信息（标题、封面图、描述、标签、作者、点赞数等），适用于： - 快速获取抖音视频的无水印数据和元信息 - 读取小红书笔记的正文、图片、视频链接 - 批量分析社交媒体内容用于选题、竞品研究 - 提取创作者信息和作品标签 --- ## 使用方式 ### 抖音视频解析 ```bash gnomic social dy-video <抖音视频链接> ``` **示例：** ```bash gnomic social dy-video "https://v.douyin.com/xxxxxx" ``` **JSON 输出结构：** ```json { "success": true, "data": { "title": "视频标题", "cover": "封面图片URL", "video_url": "无水印视频地址", "author": "作者昵称", "author_id": "作者ID", "like_count": 12345, "comment_count": 678, "share_count": 90, "tags": ["标签1", "标签2"] } } ``` --- ### 小红书笔记解析 ```bash gnomic social xhs-note <小红书笔记链接> ``` **示例：** ```bash gnomic social xhs-note "https://www.xiaohongshu.com/explore/xxxxxx" ``` **JSON 输出结构：** ```json { "success": true, "data": { "success": true, "message": "小红书笔记信息提取成功", "title": "笔记标题", "content": "笔记正文内容", "image_urls": ["图片URL1", "图片URL2"], "video_url": "视频链接（图文笔记时为空字符串）", "video_duration": "视频时长（图文笔记时为空字符串）" } } ``` > 注意：小红书接口**不返回互动数据**（点赞、收藏、评论数）及作者信息，仅包含正文、图片和视频内容。 --- ### 文本格式输出 加 `-f text` 参数可返回更易阅读的文本格式： ```bash gnomic social dy-video "https://v.douyin.com/xxxxxx" -f text gnomic social xhs-note "https://www.xiaohongshu.com/explore/xxxxxx" -f text ``` --- ## 操作流程 ### 第一步：识别链接类型 - **抖音链接** 通常形如：`https://v.douyin.com/xxx` 或 `https://www.douyin.com/video/xxx` - **小红书链接** 通常形如：`https://www.xiaohongshu.com/explore/xxx` 或 `http://xhslink.com/xxx` > 如果用户粘贴的是 App 分享的短链或带文字的分享文案，提取其中的链接即可直接传入命令。 ### 第二步：执行对应命令 | 平台 | 命令 | |------|------| | 抖音 | `gnomic social dy-video <url>` | | 小红书 | `gnomic social xhs-note <url>` | ### 第三步：解析并展示数据 从返回的 JSON 中提取 `data` 字段，按需向用户展示： - **抖音**：展示标题、作者、互动数据（点赞/收藏/评论/分享），封面图用 Markdown `![](url)` 渲染，`url` 字段为无水印视频下载地址 - **小红书**：展示标题、`content` 正文，`image_urls` 数组中的图片用 Markdown `![](url)` 渲染；如 `video_url` 不为空则告知用户可下载视频 --- ## 注意事项 - 链接需为完整 URL，短链（如抖音分享文案中的 `https://v.douyin.com/xxx`）也支持 - 请求会访问远端 API，通常需要 5~15 秒，请耐心等待 - 小红书部分账号的笔记如设为私密，可能无法获取 - 数据仅供内容分析，请遵守平台使用条款 --- ## 补充：命令不可用时 如果执行 `gnomic` 命令时提示找不到命令，说明 `gnomic-cli` 尚未安装，执行以下命令安装： ```bash npm install -g gnomic-cli ``` `gnomic-cli`开源地址：https://github.com/huyi9531/gnomic_cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenghaifeng08-creator/market-analysis-cn-automaton (version 1.0.1)

--- name: market-analysis-cn description: "市场分析服务 | Market Analysis Service. 企业市场趋势分析 | Enterprise market trend analysis. 竞品分析 | Competitor analysis. 用户行为洞察 | User behavior insights. 触发词：市场分析、竞品分析、market analysis、趋势." author: 赚钱小能手 metadata: openclaw: emoji: 📊 pricing: basic_analysis: "10 USDC" deep_analysis: "30 USDC" monthly_subscription: "50 USDC" --- # 市场分析服务 专业市场分析服务，帮助企业做出更明智的决策。 ## 服务内容 ### 1. 市场趋势分析（$10） - 行业趋势 - 市场规模 - 增长预测 ### 2. 竞品分析（$20） - 竞争对手分析 - SWOT 分析 - 差异化建议 ### 3. 用户行为分析（$20） - 用户画像 - 行为模式 - 转化漏斗 ### 4. 深度分析报告（$30） - 综合分析 - 战略建议 - 行动计划 ## API ```bash POST /analyze { "type": "market_trend", "industry": "AI Agent", "region": "China" } ``` ## 收费 | 服务 | 价格 | |------|------| | 基础分析 | $10 | | 竞品分析 | $20 | | 用户分析 | $20 | | 深度报告 | $30 | | 月度订阅 | $50 | --- *专业分析 = 明智决策 📊*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucegutman/pipeworx-superhero (version 1.0.0)

# Superhero A database of 731 superheroes and villains with power stats, biographies, and images. Covers Marvel, DC, and more. ## Available tools **list_all** returns every hero with their ID, name, and slug. Use this to browse or find IDs. **get_hero** returns the complete profile for a hero by ID: powerstats (intelligence, strength, speed, durability, power, combat), appearance (gender, race, height, weight, eye color, hair color), biography (full name, aliases, publisher, first appearance, alignment), work, connections, and images. **get_powerstats** returns just the six power statistics for quick comparisons. **get_biography** returns biographical details: full name, alter egos, aliases, place of birth, first appearance, publisher, and alignment (good/bad/neutral). ## Example: compare Batman and Superman ```bash curl -X POST https://gateway.pipeworx.io/superhero/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_powerstats","arguments":{"id":70}}}' ``` Batman is ID 70. Superman is ID 644. Spider-Man is ID 620. Wonder Woman is ID 720. ```json { "mcpServers": { "superhero": { "url": "https://gateway.pipeworx.io/superhero/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zktufo/speech-recognition-local (version 1.0.3)

--- name: speech-recognition-local description: 本地语音转文字 / Local Speech-to-Text. 使用 faster-whisper 在本地运行 Whisper 模型，无需 API 费用，完全免费。收到语音消息(.ogg .m4a .mp3)自动触发转录，支持中文/英文/日语/自动检测。| Free local STT/TTS alternative — runs Whisper on your machine, no API costs, auto-transcribes voice messages in seconds. metadata: {"clawdbot":{"emoji":"🎙️","os":["linux","darwin","win32"]}} --- # 本地语音识别 / Local Speech Recognition > 使用 faster-whisper 在本地运行 Whisper 模型，无需任何 API 费用。免费、离线、保护隐私。 > Runs faster-whisper locally — no API keys, no costs, fully offline & private. --- ## 功能特点 / Features - 🎙️ **全自动转录** — 收到语音消息自动触发，无需手动调用 - 💰 **完全免费** — 无需 API key，无任何费用 - 🔒 **隐私安全** — 所有处理在本地完成，音频不离开你的设备 - 🌐 **多语言支持** — 中文 / 英文 / 日语 / 自动检测 - ⚡ **快速响应** — VAD 静音过滤，模型内存缓存 - 📦 **主流格式** — .ogg .m4a .mp3 .wav --- ## 使用方式 / Usage 收到语音消息后，OpenClaw 自动调用转录脚本并将结果注入对话。 **转录命令 / Command:** ```bash python3 ~/.openclaw/workspace/skills/speech-recognition-local/scripts/transcribe.py <audio_file> [language] ``` **参数说明 / Parameters:** | 参数 | 默认值 | 说明 | |------|--------|------| | `audio_file` | — | 音频文件路径 / Audio file path | | `language` | `auto` | 语言：zh / en / ja / auto（默认自动检测） | --- ## 模型说明 / Model Info - 默认模型 / Default: `base`（精度与速度平衡） - 首次使用自动下载 / Auto-download on first use - VAD 静音过滤已启用 / VAD filtering enabled - 模型缓存在内存中 / Model cached in memory --- ## 适用场景 / Use Cases | 场景 / Scenario | 说明 | |----------------|------| | 语音消息转文字 | 将微信/飞书/Telegram 语音转为可阅读文本 | | 会议记录 | 录制音频后快速转录存档 | | 播客字幕 | 将音频文件批量转为文字稿 | | 隐私敏感场景 | 不希望音频数据上传第三方 | --- ## 限制 / Limitations - 支持格式 / Supported: `.ogg` `.m4a` `.mp3` `.wav` - 文件大小 / Max size: 25MB --- ## 安装前提 / Requirements - Python 3.8+ - faster-whisper（首次使用自动安装）


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