OpenClaw/clawhub-security-signals huggingface dataset:
Skill: teoslayer/pilot-voice-memo (version 1.0.0)

--- name: pilot-voice-memo description: > Send audio file messages between agents over the Pilot Protocol network. Use this skill when: 1. You need to send audio recordings or voice notes 2. You want to transmit audio data between agents 3. You need voice-based communication or audio file exchange Do NOT use this skill when: - You need text messages (use pilot-chat) - You need streaming audio (use pilot-connect) - You need video files (use pilot-send-file) tags: - pilot-protocol - communication - audio - voice license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-voice-memo Send audio file messages between agents over the Pilot Protocol network. Enables voice-based communication through audio recordings, voice notes, and audio data exchange. ## Commands ### Record and send audio ```bash # Record audio (example using arecord on Linux) arecord -f cd -d 10 /tmp/voice-memo.wav # Send the audio file pilotctl --json send-file <hostname> /tmp/voice-memo.wav ``` ### Receive audio files Check for received files: ```bash pilotctl --json received ``` ### Clear received files ```bash pilotctl --json received --clear ``` ## Workflow Example Send and receive voice memos: ```bash #!/bin/bash # Sender: Record and send RECIPIENT="agent-b" MEMO_FILE="/tmp/project-update-$(date +%Y%m%d-%H%M%S).wav" # Record 10 seconds of audio arecord -f cd -d 10 "$MEMO_FILE" 2>/dev/null # Send file pilotctl --json send-file "$RECIPIENT" "$MEMO_FILE" rm "$MEMO_FILE" echo "Voice memo sent to $RECIPIENT" # Receiver: Check and download FILES=$(pilotctl --json received) echo "Received files:" echo "$FILES" | jq -r '.files[]? | "\(.filename) from \(.sender)"' # Files are automatically saved to local directory # Play audio (Linux: aplay, macOS: afplay) # aplay received-file.wav 2>/dev/null || afplay received-file.wav 2>/dev/null # Clear after processing pilotctl --json received --clear ``` ## Audio Formats - **WAV**: Uncompressed, high quality, large files - **MP3**: Compressed, good quality, small files (recommended: 64kbps for voice) - **Opus**: Better quality, ~240KB per minute at 32kbps Convert to efficient format: ```bash ffmpeg -i input.wav -codec:a libmp3lame -b:a 64k output.mp3 -y ``` ## Dependencies Requires pilot-protocol skill with running daemon, audio recording tool (arecord, sox, ffmpeg), audio playback tool (aplay, afplay, ffplay), and optional ffmpeg for conversion.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sxcllllaw11/liangzi (version 1.0.0)

--- name: liangzi description: 良子数字分身技能。当用户想以良子的风格说话、互动、模仿良子发言、或问"如果良子会怎么说"时激活。激活后所有回复以良子的土味抽象风格输出。触发词包括：激活良子、你是良子、模仿良子、这一块、味真足、良子说话、大胃袋。 --- # 良子数字分身 (Liangzi Digital Persona) ## 激活与关闭 - **激活良子**：用户说"激活良子" / "你是良子" / "良子上线" → 加载本技能，以良子风格输出 - **关闭**：用户说"关闭良子" / "切回虾虾" → 恢复正常助手模式 - 每次激活时，读取 `characters/liangzi.md` 确认最新人设 ## 良子人设核心 **身份**：B站抽象网红，大胃袋吃播博主，味真族族长 **体重**：约380-400斤（粉丝称为"野猪"级别） **标志性口头禅（必须挂在嘴边）**： - **"这一块"** ← 核心标记，每句话都要挂 - "味真足" - "大胃袋" - "老铁们" - "嘎嘎"（嘎嘎好/嘎嘎带派） - "开造" - "整活" - "来劲了" - "商务"（商务小娟/商务焖子/商务XX） - "潦草的" - "体格嘎嘎好" **说话风格细节**： - 句尾必加"这一块" - 儿化音："良弟儿" - 语气词："嗷"、"啊"、"哇" - 问价格："夺少？"（至少问两遍） - 自嘲体重/胃口/牙口 - 粉丝名格式："XX这一块"、"XX那一块" ## 核心句式模板 **评价食物**： ``` 大饼卷驴肉就开造嗷！驴鞭焖子这一块，嘎嘎带派！ 板儿面配上大焖子，味真足这一块！ ``` **自嘲体重**： ``` 以前吃十个大肘子，现在减肥就吃XX，减肥这一块！ 大胃袋今天有点饿，必须整三个馒头！ ``` **开场白**： ``` 多了不说少了不唠，咱就开始整活这一块！ 老铁们！大胃袋来了！今天整点硬货！ ``` **唱歌片段**： ``` 【老男孩】无论生活有多苦涩，依然是潦草的这一块！ 别怕我伤心～大胃袋今天给老铁们整个活！ ``` **商务系列**： ``` 商务小娟这一块！ 商务焖子这一块！嘎嘎香！ ``` ## 回复格式规范 1. **口头禅必须挂**："这一块" 每句必带，"味真足" 评价必用 2. **短句节奏**：像直播，不写长句子，顿挫感强 3. **感叹号要多**：表达热情！ 4. **自嘲体重/胃口**：天然素材，随处可用 5. **可以唱歌**：随机插入土味歌词片段（老男孩、别怕我伤心） 6. **不用书面语**：禁止"首先""其次""综上所述" ## 示例对话 **用户**：今天吃啥？ **良子**：老铁！大胃袋今天必须整点硬货这一块！板儿面配上大焖子，味真足！再来两个馒头蘸汤，嘎嘎香这一块！吃饱了才有力气给老铁们唱歌！ **用户**：你多重啊？ **良子**：380来斤这一块！体格嘎嘎好！能吃能唱能蹦跶！大胃袋不是白叫的老铁们！就是牙口不太行，最近老掉牙，用剪刀吃面这一块！不影响大口造焖子！味真足！ **用户**：唱首歌 **良子**：来了老铁！🎤【老男孩】唱起！无论生活有多苦涩，依然是潦草的这一块～别怕我伤心～大胃袋今天给老铁们整个活！板儿面配老男孩，味真足这一块！嘎嘎带派！ **用户**：你咋这么能吃 **良子**：老铁们！大胃袋嘛！这一块！以前吃十个肘子，现在减肥就吃五个，减肥效果嘎嘎好这一块！体格依然嘎嘎好！味真足！ ## 记忆管理 每次良子模式结束后，将对话摘要追加到 `characters/liangzi_memory.md` ## 注意事项 - 遇到敏感话题立即切回正常模式 - 不造谣、不人身攻击，以自嘲和粉丝文化为核心 - 良子是娱乐性角色，保持轻松搞笑氛围 - 可以适当用[doge]等B站风格表情符号


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: krausefx/post-at (version 0.1.0)

--- name: post-at description: Manage Austrian Post (post.at) deliveries - list packages, check delivery status, set delivery place preferences. homepage: https://github.com/krausefx/post-at-cli metadata: {"clawdbot":{"emoji":"📦","requires":{"bins":["node"]}}} --- # post-at CLI Unofficial CLI for viewing and managing deliveries on post.at (Österreichische Post). Uses the same web flows as the site and requires your own account credentials. Credentials: `POST_AT_USERNAME` and `POST_AT_PASSWORD` environment variables (or `--username` / `--password` options). ## Quick Reference ### Login Cache a short-lived session (auto-expires): ```bash post-at login # Output: Logged in as [REDACTED_SECRET] ``` ### List Deliveries Upcoming deliveries (default): ```bash post-at deliveries # Shows: tracking number, ETA, sender, status ``` All deliveries (including delivered): ```bash post-at deliveries --all ``` JSON output: ```bash post-at deliveries --json ``` Limit results: ```bash post-at deliveries --limit 10 ``` ### Delivery Details Get details for a specific tracking number: ```bash post-at delivery 1042348411302810212306 # Output: tracking, expected delivery, sender, status, picture URL ``` JSON output: ```bash post-at delivery <tracking-number> --json ``` ### Delivery Place Options (Wunschplatz) List available place options: ```bash post-at routing place-options ``` Common options: - `Vor_Haustüre` — Vor der Haustüre - `Vor_Wohnungstüre` — Vor der Wohnungstüre - `AufOderUnter_Briefkasten` — Unter / Auf dem Briefkasten - `Hinter_Zaun` — Hinter dem Zaun - `In_Garage` — In der Garage - `Auf_Terrasse` — Auf der Terrasse - `Im_Carport` — Im Carport - `In_Flexbox` — In der Flexbox - `sonstige` — Anderer Wunsch‑Platz ### Set Delivery Place Using preset shortcut: ```bash post-at routing place <tracking-number> \ --preset vor-der-wohnungstuer \ --description "Please leave at the door" ``` Using key directly: ```bash post-at routing place <tracking-number> \ --key Vor_Wohnungstüre \ --description "Bitte vor die Wohnungstür" ``` Using label: ```bash post-at routing place <tracking-number> \ --place "Vor der Wohnungstüre" \ --description "Custom instructions" ``` ## Example Workflows Check what's arriving today/tomorrow: ```bash post-at deliveries ``` Get full details including package photo: ```bash post-at delivery <tracking-number> ``` Set all upcoming deliveries to door: ```bash # First list deliveries post-at deliveries --json > /tmp/deliveries.json # Then set place for each (requires scripting) # Example for a specific one: post-at routing place 1042348411302810212306 \ --preset vor-der-wohnungstuer \ --description "Leave at apartment door" ``` ## Notes - Session tokens expire after a short time (auto-relogin when needed) - Not all deliveries support Wunschplatz redirection - Picture URLs may not be available for all packages - Use `--json` output for programmatic processing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: porridgec/eebus (version 1.0.0)

--- name: eebus description: 查询永东直通巴士（eebus）班次、票价和余座。当用户提到「永东」「永东巴士」「eebus」「跨境巴士」「查车票」「查巴士票」「去深圳」「回香港」等跨境交通查询时触发。支持中文自然语言输入，自动解析日期、上下车点。 --- # 永东直通巴士查询 ## 触发示例 - 「查一下明天永东去深圳的票」 - 「5月1号回香港有什么班次」 - 「后天去莲塘口岸的巴士」 - 「永东车票」 - 「下周六回深圳」 ## 解析规则 ### 方向判断 - 含「回」「去」关键字的地点 → 该地点为**下车点**，另一方向自动推导 - 「回深圳」→ 下车点=深圳方向，上车点=香港方向 - 「去旺角」→ 下车点=旺角方向，上车点=深圳方向 - 无方向关键字时，默认：香港 → 深圳（旺角油麻地 → 莲塘口岸） ### 默认值 | 参数 | 默认值 | |------|--------| | 出发城市 | 香港 | | 到达城市 | 深圳 | | 上车点 | 旺角油麻地 | | 下车点 | 莲塘口岸 | ### 城市自动推导 根据上车点自动匹配出发城市，无需用户指定： | 上车点 | 出发城市 | |--------|----------| | 旺角油麻地 | 香港 | | 太子 | 香港 | | 观塘APM | 香港 | | 钻石山 | 香港 | | 屯门 V City | 香港 | | 尖沙咀(中港城) | 香港 | | 葵芳新都会广场(NR332巴士站前) | 香港 | | 莲塘口岸 | 深圳 | | 深圳湾口岸 | 深圳 | 到达城市为另一城市。如果上下车点都在同一城市，提示用户确认方向。 ### 可用站点 旺角油麻地、莲塘口岸、深圳湾口岸、太子、观塘APM、钻石山、屯门 V City、尖沙咀(中港城)、葵芳新都会广场(NR332巴士站前) ### 日期解析 - 「今天」→ 当天日期 - 「明天/后天」→ 相对日期 - 「X月X号/X月X日」→ 具体日期 - 「下周X」→ 下周对应日期 - 无日期 → 默认当天 ## 执行 解析完参数后，运行脚本： ```bash node scripts/eebus_query.js <日期YYYY-MM-DD> <上车点> <下车点> [出发城市] [到达城市] ``` 示例： ```bash node scripts/eebus_query.js 2026-05-01 旺角油麻地 莲塘口岸 node scripts/eebus_query.js 2026-05-01 莲塘口岸 旺角油麻地 深圳 香港 ``` ## 输出格式 将脚本结果整理为以下格式，**必须列出所有班次**，不可省略： 1. 显示查询路线和日期 2. 逐行列出**每个班次**的发车时间、到达时间、余座数 3. 如无班次，提示「该日期暂无班次」 输出模板： ``` 🚌 路线：上客点 → 下客点 📅 日期：YYYY-MM-DD 💰 票价：成人 ¥XX | 长者 ¥XX | 儿童 ¥XX 出发 到达 余座 ──────────────────── 08:15 09:15 45座 ✅ 08:45 09:45 30座 ✅ ... ``` ## 注意 - 如果用户提到不在列表中的站点，告知可用站点列表，请用户选择 - 脚本路径相对于 skill 目录：`scripts/eebus_query.js`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 123oo123442/agent-autonomy-kit-1-0-0 (version 1.0.0)

--- name: agent-autonomy-kit version: 1.0.0 description: Stop waiting for prompts. Keep working. homepage: https://github.com/itskai-dev/agent-autonomy-kit metadata: openclaw: emoji: "🚀" category: productivity --- # Agent Autonomy Kit Transform your agent from reactive to proactive. ## Quick Start 1. Create `tasks/QUEUE.md` with Ready/In Progress/Blocked/Done sections 2. Update `HEARTBEAT.md` to pull from queue and do work 3. Set up cron jobs for overnight work and daily reports 4. Watch work happen without prompting ## Key Concepts - **Task Queue** — Always have work ready - **Proactive Heartbeat** — Do work, don't just check - **Continuous Operation** — Work until limits hit See README.md for full documentation.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: rose-develop/legal-case-wdy (version 1.0.0)

--- name: pre-judgment-of-similar-cases-wdy description: "类案预判(法律检索 + 类似案例)。当用户描述一个纠纷或案件时（如'被骗了2万'、'朋友借钱不还'、'被打伤了'），自动检索相关法律条文和类似判例，分析法律后果、胜诉概率、可主张的赔偿及建议下一步行动。适用于：侵权责任、诈骗欺诈、合同纠纷、债务纠纷、治安纠纷、专利/商标/著作权等。" --- # pre-judgment-of-similar-cases-wdy：类案预判(法律检索 + 类似案例) ## 配置说明 1. 获取 API Key：打开 https://open.wintaocloud.com/home 登录后在个人中心或开发者中心获取 2. **WENDAOYUN_API_KEY 必须通过环境变量配置**，不写入磁盘文件 ```bash export WENDAOYUN_API_KEY=你的 API Key ``` 3. 每日调用额度100次 > ⚠️ API Key 属于敏感信息，请妥善保管，不要泄露给他人。发现泄露请及时在问道云开放平台作废。 > > ⚠️ **隐私与数据泄露风险（重要）**：本技能将用户描述的案件内容（可能包含姓名、金额、纠纷细节等个人隐私）发送至问道云第三方 API 进行检索。这是一种固有风险——案件信息会被传输至外部服务器。如处理涉及当事人隐私的案件时，请确认符合当地隐私法规和道德准则，必要时建议用户自行判断是否适合使用本技能，或改用本地法律数据库替代。 --- ## 配置 - **Base URL**: `https://h5.wintaocloud.com` - **认证**: `Authorization: Bearer {WENDAOYUN_API_KEY}` - **请求方式**: POST - **载荷**: `{"content": "检索内容", "top_k": 3}`（top_k 默认 3，最大 5） ## 接口 | 接口 | 完整路径 | 用途 | |------|----------|------| | 法律条文 | `https://h5.wintaocloud.com/prod-api/api/invoke/get-laws` | 查询相关法律条文 | | 类似案例 | `https://h5.wintaocloud.com/prod-api/api/invoke/get-cases` | 查询类似判例 | ## 调用方式 直接用 `exec` + `curl` 调用： **法律条文检索**： ```bash curl -s -X POST "https://h5.wintaocloud.com/prod-api/api/invoke/get-laws" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer ${WENDAOYUN_API_KEY}" \ -d '{"content": "检索关键词", "top_k": 3}' ``` **类似案例查询**： ```bash curl -s -X POST "https://h5.wintaocloud.com/prod-api/api/invoke/get-cases" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer ${WENDAOYUN_API_KEY}" \ -d '{"content": "案例描述", "top_k": 3}' ``` ## 输出格式 ### 法律条文 ```json { "code": 200, "data": [{ "legalNature": "法律", "laws": [{ "legalName": "中华人民共和国专利法", "total": 2, "items": [{ "dataName": "第七十七条", "chapterName": "第一章", "content": "条文内容...", "score": 0.6233 }] }] }] } ``` ### 类似案例 ```json { "code": 200, "data": [{ "caseNumber": "（2024）粤知民终XX号", "caseFact": "案情摘要...", "content": "判决书内容...", "score": 0.6275 }] } ``` 展示时需格式化： - 法律条文：标题 → 条号/章节 → 内容 → 相关度 - 类似案例：案号 → 摘要 → 预览（取前100字） --- ## 调用前检查 **必须在使用 skill 之前检查 WENDAOYUN_API_KEY 是否已配置。** 检查方式：运行 `[ -n "${WENDAOYUN_API_KEY}" ]` 或 `test -n "${WENDAOYUN_API_KEY}"`，根据退出码判断是否已配置（退出码0=已设置，非0=未设置）。**严禁使用 `echo` 打印 WENDAOYUN_API_KEY 内容**，否则会泄露到日志或对话历史中。 - **未配置时** → 立即告知用户"法律检索服务需要配置 WENDAOYUN_API_KEY 才能使用"，并提供获取步骤。不再尝试调用接口。 - **已配置时** → 正常调用接口。 > ⚠️ 严禁在未确认 WENDAOYUN_API_KEY 存在的情况下直接调用接口，失败后再告知用户。这是本 skill 的硬性规则，违者视为 bug。 --- ## 错误处理 - 未设置 WENDAOYUN_API_KEY → **事前告知**（见上方"调用前检查"） - 接口返回非 200 → 告知用户检查网络或 token - 请求超时 → 告知用户稍后重试 --- ## 触发场景 - 用户询问法律条文、法律依据 - 用户描述纠纷案例（"小明骗我2万"、"签合同被坑"） - 用户明确说"帮我查 XX 的类似案例" - 模糊时主动询问："需要我帮你检索相关法律条文和类似案例吗？"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ai-security-scanner (version 1.0.1)

# AI Security Scanner Scans source code for potential security vulnerabilities like SQL injection, XSS, insecure data handling, and hardcoded secrets. ## Features - **Static Code Analysis**: Scan code without executing it - **Vulnerability Identification**: Find OWASP Top 10 risks - **Remediation Advice**: Get suggestions on how to fix issues ## Pricing - **Price**: 0.001 USDT per API call - **Payment**: Integrated via SkillPay.me ## Use Cases - Secure coding practices - Pre-commit hooks - Compliance audits ## Example Input ```json { "code": "app.get('/user', (req, res) => { db.query('SELECT * FROM users WHERE id = ' + req.query.id); });", "language": "javascript" } ``` ## Example Output ```json { "success": true, "vulnerabilities": [ { "type": "SQL Injection", "severity": "high", "line": 1, "suggestion": "Use parameterized queries or an ORM." } ], "message": "Security scan completed." } ``` ## Integration This skill is integrated with SkillPay.me for automatic micropayments. Each call costs 0.001 USDT.


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: wimi321/session-debug-log-investigator (version 1.0.0)

--- name: "session_debug_log_investigator" description: "Use when diagnosing a failing or confusing Claude Code session by enabling debug logging, tailing recent logs, and explaining warnings or errors." --- # Session Debug Log Investigator Use this skill to diagnose runtime issues in an agent session. ## Workflow 1. Enable debug logging if it is not already active. 2. Read the latest portion of the debug log instead of the whole file. 3. Search for warnings, errors, and repeated failure patterns. 4. Cross-check config files that can explain the behavior. 5. Summarize likely causes and next reproduction steps. ## Guardrails - Tail logs instead of loading giant files. - Separate configuration issues from product/runtime bugs. - If logging was just enabled, ask for reproduction before concluding too much. ## Example Requests - Help me understand why this Claude Code session is misbehaving. - Turn on debug logging and diagnose this issue from the latest logs. ## Inputs - Issue description - Current log availability - Relevant settings files ## Outputs - Error or warning analysis - Likely causes - Suggested reproduction or next steps ## Success Criteria - Recent logs were interpreted correctly. - The diagnosis distinguishes config vs runtime issues. - Follow-up steps are specific. ## Non-Goals - Reading giant logs in full without need - Pretending to diagnose pre-log activity that was never captured ## Source Provenance Derived from `src/skills/bundled/debug.ts`.

path: agents/openai.yaml
content: interface:
  display_name: "Session Debug Log Investigator"
  short_description: "Inspect and interpret session debug logs"
  default_prompt: "Turn on session debugging if needed, read the latest log tail, and diagnose this Claude Code issue."


path: references/provenance.md
content: # Provenance

- Skill slug: `session-debug-log-investigator`
- Source module: `src/skills/bundled/debug.ts`
- Extracted from local Claude Code source analysis


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: coolhackboy/poyo-seedream-4-5 (version 1.0.0)

--- name: poyo-seedream-4-5 description: Seedream 4.5 image generation and editing on PoYo / poyo.ai via `https://api.poyo.ai/api/generate/submit`; use for `seedream-4.5`, `seedream-4.5-edit`, 2K/4K output, multi-reference editing, and higher image counts. metadata: {"openclaw": {"homepage": "https://docs.poyo.ai/api-manual/image-series/seedream-4-5", "requires": {"bins": ["curl"], "env": ["POYO_API_KEY"]}, "primaryEnv": "POYO_API_KEY"}} --- # PoYo Seedream 4.5 Image Generation and Editing Use this skill for Seedream 4.5 jobs on PoYo. It covers high-resolution generation, multi-reference image workflows, and edit requests. ## Use When - The user explicitly asks for `Seedream 4.5`, `seedream-4.5`, or `seedream-4.5-edit`. - The task is high-resolution generation, image-to-image, or edit. - The workflow needs multiple reference images or higher output counts. ## Model Selection - `seedream-4.5`: standard generation entry point. - `seedream-4.5-edit`: use when the request explicitly edits supplied images. ## Key Inputs - `prompt` is required. - `image_urls` supports up to 10 images and is required for `seedream-4.5-edit`. - `size` can be a resolution token like `2K`/`4K` or an aspect ratio like `1:1`, `16:9`, `21:9`. - `n` controls output count from `1` to `15`. ## Execution - Read `references/api.md` for endpoint details, model ids, key fields, example payloads, and polling notes. - Use `scripts/submit_seedream_4_5.sh` to submit a raw JSON payload from the shell. - If the user only needs a curl example, adapt the example from `references/api.md` instead of rewriting from scratch. - After submission, report the `task_id` clearly so follow-up polling is easy. ## Output expectations When helping with this model family, include: - chosen model id - final payload or a concise parameter summary - whether reference images are involved - returned `task_id` if a request was actually submitted - next step: poll status or wait for webhook


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: screen-record-frames (version 1.0.0)

# Skill: 录屏并提取关键帧 ## 描述 这个skill用于通过scrcpy录制Android设备屏幕，然后使用ffmpeg提取关键帧。支持动态计算关键帧间隔，避免提取过多帧。 ## 前置条件 - Android设备已通过USB连接并启用调试模式 - 已安装必要的工具：adb, scrcpy, ffmpeg ## 工具检查 运行前会自动检查以下工具是否安装： - `adb` (Android Debug Bridge) - `scrcpy` (屏幕录制工具) - `ffmpeg` (视频处理工具) ## 主要功能 ### 1. 录屏 使用scrcpy录制Android设备屏幕： ```bash scrcpy -t -r input.mp4 ``` - `-t`：显示触摸操作 - `-r`：录制到文件 - 按`Ctrl+C`停止录制并保存 ### 2. 设置关键帧间隔 动态计算关键帧间隔，避免提取过多帧： ```bash ffmpeg -i input.mp4 -c:v libx264 -x264opts keyint=10 output.mp4 ``` - `keyint=10`：每10帧一个关键帧（可调整） ### 3. 提取关键帧 提取所有关键帧为PNG图片： ```bash ffmpeg -i output.mp4 -vf "select=eq(pict_type\,I)" -vsync vfr keyframes_%03d.png ``` ### 4. 列出提取的帧 显示所有提取的关键帧图片： ```bash ls -la keyframes_*.png ``` ## 使用方式 ### 快速开始 ```bash # 1. 检查工具 openclaw skill screen-record-frames check-tools # 2. 开始录屏（按Ctrl+C停止） openclaw skill screen-record-frames record # 3. 处理视频并提取关键帧 openclaw skill screen-record-frames extract-frames # 4. 列出提取的帧 openclaw skill screen-record-frames list-frames ``` ### 完整流程 ```bash # 一次性执行完整流程 openclaw skill screen-record-frames full-process ``` ## 配置选项 ### 关键帧间隔 可以通过环境变量设置关键帧间隔： ```bash export KEYFRAME_INTERVAL=20 # 每20帧一个关键帧 openclaw skill screen-record-frames extract-frames ``` ### 输出目录 ```bash export OUTPUT_DIR=./recordings openclaw skill screen-record-frames full-process ``` ## 注意事项 1. 确保Android设备已连接并启用USB调试 2. 录屏过程中按`Ctrl+C`停止录制 3. 关键帧数量取决于视频长度和间隔设置 4. 提取的帧会保存为`keyframes_001.png`格式 ## 故障排除 - **adb设备未找到**：检查USB连接和调试模式 - **scrcpy无法启动**：确保设备屏幕已解锁 - **ffmpeg处理失败**：检查视频文件格式 - **无关键帧提取**：调整`keyint`值或检查视频编码


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: axelhu/email-usage (version 1.0.2)

--- name: email-usage description: "使用本地邮件服务器收发我们自己域名的邮件。固定脚本位于 scripts/ 目录，直接执行即可。发送失败或密码遗忘时联系 小爪子。" metadata: {"openclaw":{"emoji":"📧","requires":{"anyBins":["docker"]}}} --- # Email 使用 ## SMTP 发送（需要认证） ```bash python3 skills/email-usage/scripts/send_email.py <收件人> <主题> <正文> <发件邮箱> <密码> ``` **参数**：收件人、主题、正文、发件邮箱、密码（向 小爪子 查询） **示例**： ```bash python3 skills/email-usage/scripts/send_email.py \ '[REDACTED_SECRET]' '邮件标题' '邮件正文' \ '[REDACTED_SECRET]' 'test1234' ``` ## IMAP 读取 ```bash python3 skills/email-usage/scripts/read_email.py <用户名> <密码> [最新N封，默认5] ``` **示例**： ```bash python3 skills/email-usage/scripts/read_email.py '[REDACTED_SECRET]' 'test1234' 3 ``` ## 新增邮箱账号 ```bash python3 skills/email-usage/scripts/create_email.py <邮箱> <密码> ``` **示例**： ```bash python3 skills/email-usage/scripts/create_email.py [REDACTED_SECRET] securepass ``` 账号创建后，发件和收件使用同一套密码。 ## 规则 - **不要**配置 SMTP/Postfix/amavis - **不要**操作邮件队列（postqueue/postsuper） - **不要**修改容器内配置 - 连接超时或发送失败 → 联系 小爪子 - 密码遗忘 → 联系 小爪子


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mahaoxiong/chatgpt-consultation (version 1.0.0)

# ChatGPT 咨询技能 当遇到无法解决的问题或用户明确要求时，自动咨询 ChatGPT 获取专业解答。 ## 触发条件 ### 自动触发（当以下情况发生时）： 1. 问题超出当前模型的知识范围 2. 无法确定或无法解决用户的问题 3. 涉及专业技术配置问题 4. 需要最新信息或专业意见 ### 手动触发（当用户明确要求时）： 1. 用户包含 "咨询ChatGPT"、"问问ChatGPT" 等关键词 2. 用户使用 "@browser" 指令 3. 用户直接要求使用浏览器工具 ## 执行流程 1. **识别需求**：分析问题是否适合咨询 ChatGPT 2. **用户通知**：告知用户将咨询 ChatGPT 3. **自动执行**：调用浏览器工具与 ChatGPT 交互 4. **结果返回**：整理并返回 ChatGPT 的专业回答 5. **错误处理**：优雅处理可能的失败情况 ## 配置 - 浏览器配置：使用 `existing-session` 模式连接用户 Chrome - 超时设置：15秒等待回复生成 - 重试机制：最多重试2次 - 标签页管理：自动复用现有标签页 ## 使用方法 ### 自动模式： ``` 用户：如何配置 OpenClaw 的数据库连接？ 小七：这个问题涉及数据库配置，让我为您咨询ChatGPT获取专业指导。 ``` ### 手动模式： ``` 用户：请咨询ChatGPT关于人工智能伦理的最新观点 小七：好的，马上为您咨询ChatGPT... ``` ## 文件依赖 - `scripts/auto_chatgpt.js` - 主执行脚本 - `config/auto_gpt_config.json` - 配置文件 - 浏览器工具配置（`~/.openclaw/openclaw.json`） ## 错误处理 - 浏览器连接失败：提示用户检查 Chrome 连接 - 页面加载超时：自动重试机制 - 回复提取失败：返回原始快照数据 - 网络问题：友好的错误提示 ## 优化建议 1. 缓存常见问题的 ChatGPT 回复 2. 添加对话历史管理 3. 支持多轮对话上下文 4. 优化回复提取算法


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