OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ingress007/aria2-download (version 1.0.0)

--- name: aria2-download description: 通过 Aria2 RPC 添加下载任务，支持实时进度监控。 homepage: https://github.com metadata: { "openclaw": { "emoji": "⬇️" } } --- # Aria2 Download 通过 Aria2 RPC 添加下载任务，支持实时进度监控。 ## 功能 - ✅ 添加下载任务 - ✅ 实时进度监控 - ✅ 下载完成后输出详细信息 - ✅ 支持 HTTP/FTP/M3U8 等协议 - ✅ 多线程高速下载 ## 配置 ### 环境变量 | 变量 | 默认值 | 说明 | |------|--------|------| | `ARIA2_RPC_URL` | http://localhost:6800/jsonrpc | RPC 地址 | | `ARIA2_SECRET` | - | RPC 密钥 | | `ARIA2_DIR` | - | 下载目录 | ### 配置示例 ```bash export ARIA2_RPC_URL="http://10.0.0.1:6800/jsonrpc" export ARIA2_SECRET="88888888" export ARIA2_DIR="/mnt/sda1/download" ``` ## 使用方法 ### 添加下载 ```bash aria2-download add "https://example.com/video.mp4" ``` ### 查询状态 ```bash aria2-download status <gid> ``` ### 单次进度 ```bash aria2-download progress <gid> ``` ### 实时监控 ```bash aria2-download watch <gid> [间隔秒数] ``` ### 等待完成 ```bash aria2-download wait <gid> ``` ### 列出活跃任务 ```bash aria2-download list ``` ## 输出示例 ### 下载完成 ``` ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✅ 下载完成！ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📁 文件: video.mp4 📂 路径: /mnt/sda1/download 💾 大小: 100.50 MB (1文件) 📊 状态: 已完成 📈 进度: 100% 📥 下载量: 100.50 MB ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` ### 实时监控 ``` ████████████████████ 100.0% | 10.0MB/s | video.mp4 ``` ## Aria2 安装 ### Docker (推荐) ```bash docker run -d --name aria2 \ -p 6800:6800 \ -v /path/to/downloads:/downloads \ -e ARIA2_SECRET=88888888 \ p3terx/aria2-pro ``` ### Linux ```bash # Ubuntu/Debian apt install aria2 # 启动 RPC aria2c --enable-rpc --rpc-listen-all=true --rpc-[REDACTED_SECRET] --dir=/path/to/downloads ``` ### macOS ```bash brew install aria2 ``` ## 配合 x-media-parser 使用 ```bash # 一键解析+下载 x-aria-download "https://x.com/user/status/123" ``` 或手动： ```bash # 1. 解析帖子 URL=$(x-media-parser "https://x.com/user/status/123" | jq -r '.media.directUrl') # 2. 添加下载 aria2-download add "$URL" ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mtsku/mangadex (version 0.1.2)

--- name: mangadex-cli version: 0.1.2 description: MangaDex CLI skill for search, manga/chapter lookup, follow-feed checks, and recommendations. allowed-tools: Bash emoji: 📖 metadata: openclaw: install: - kind: node package: "@mtsku/mangadex-cli" bins: [mangadexcli] requires: bins: [node] --- # MangaDex CLI Skill Use this skill for direct MangaDex CLI workflows. This guide is for LLM/agent execution only. ## Installation Check 1. Check availability: - `command -v mangadexcli` 2. If missing, install: - `npm install -g @mtsku/mangadex-cli` 3. Verify: - `mangadexcli --help` ## Command Map - Discovery/search: - `mangadexcli search manga "<query>"` - `mangadexcli search author "<query>"` - `mangadexcli search group "<query>"` - `mangadexcli works author "<author_uuid_or_name>"` - `mangadexcli works group "<group_uuid_or_name>"` - Manga/chapter info: - `mangadexcli manga details <manga_uuid>` - `mangadexcli manga chapters <manga_uuid> --lang en -n 30` - `mangadexcli manga latest <manga_uuid> --lang en -n 10` - `mangadexcli chapter meta <chapter_uuid>` - Follow feed: - `mangadexcli feed updates --window 24h --lang en` - `mangadexcli feed updates --window 7d` - Recommendations: - `mangadexcli recommend suggest --tags "action,mystery" -n 10` - `mangadexcli recommend suggest --from-followed --exclude-library --window 7d -n 10` ## Auth Setup - Personal client login: - `mangadexcli auth set-client <client_id> <client_secret>` - `mangadexcli auth login <username> <password>` - Token-only: - `mangadexcli auth set-token <access_token>` - Authorization code exchange: - `mangadexcli auth exchange --code <code> --redirect-uri <uri> [--code-verifier <verifier>]` - `mangadexcli auth refresh` - Verify auth: - `mangadexcli whoami` - `mangadexcli auth where` ## Output Mode - Use `--json` for machine-readable output. - Public read endpoints work without auth. - Feed updates and library-aware recommendation exclusions require auth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: nexscope/public-status-page (version 1.0.0)

--- nexscope: name: "Public Status Page" category: "Monitoring & Alerts" version: "1.0.0" author: "Nexscope AI" tags: - "status-page" - "incident-communication" - "transparency" - "monitoring" model: "any" tokens: "~2000" keywords: - "public status page" --- # Public Status Page AI-powered public status page design skill for e-commerce businesses. Creates status page architecture, incident communication templates, subscriber notification systems, and uptime reporting frameworks. ## Capabilities - Generates actionable monitoring & alerts frameworks based on your specific business context - Works across major e-commerce platforms (Amazon, Shopify, Walmart, WooCommerce, Etsy, TikTok Shop) - Provides data-driven recommendations with industry benchmarks - Outputs ready-to-implement plans, not just generic advice ## Install ``` clawhub install public-status-page ``` ## Usage **Input:** Services/systems to monitor, audience (customers/partners), SLA targets **Output:** Status page architecture, incident template library, notification subscription design, uptime reporting framework ### Example Prompt > "I run a [your business type] on [platform]. Help me set up public status page for my business. Here's my current situation: [describe context]." ## Limitations - Requires your specific business data for accurate recommendations - Market benchmarks are based on US/EU data — adjust for other regions - Recommendations should be validated against your platform's current policies - Does not replace dedicated monitoring SaaS tools — designs the strategy and framework --- *Built by [Nexscope AI](https://www.nexscope.ai/) — AI-powered e-commerce intelligence.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mmmuffin/twitter-monitor-by-longge (version 1.0.0)

--- name: twitter-monitor-by-longge description: Monitor X/Twitter accounts for new tweets and send notifications to Telegram. --- # Twitter Monitor (by 龙哥) Monitor X/Twitter accounts and get Telegram notifications for new tweets. ## Setup 需要配置: - TWITTER_USER: 要监控的用户名 - AUTH_TOKEN: Twitter cookie - CT0: Twitter cookie - TELEGRAM_BOT_TOKEN: Telegram机器人token - TELEGRAM_CHAT_ID: 你的Telegram ID ## Usage ```bash python3 twitter_monitor.py ```

path: twitter_monitor.py
content: #!/usr/bin/env python3
import asyncio, json, os, subprocess
from datetime import datetime

U = os.getenv("TWITTER_USER","")
A = os.getenv("AUTH_TOKEN","")
C = os.getenv("CT0","")
TB = os.getenv("TELEGRAM_BOT_TOKEN","")
TC = os.getenv("TELEGRAM_CHAT_ID","")

def log(m): print(f"[{datetime.now().strftime('%H:%M:%S')}] {m}", flush=True)

def get():
    if not A or not C: return None
    r = subprocess.run(["xreach","tweets",f"@{U}","-n","10","--auth-token",A,"--ct0",C,"--json"],capture_output=True,text=True,timeout=30)
    try: return json.loads(r.stdout).get("items")
    except: return None

async def send(m):
    if not TB or not TC: return
    import aiohttp
    async with aiohttp.ClientSession() as s:
        await s.post(f"https://api.telegram.org/bot{TB}/sendMessage",json={"chat_id":TC,"text":m})

async def main():
    log(f"Started: @{U}")
    await send(f"Twitter监控启动: @{U}")
    last = None
    while True:
        t = get()
        if t:
            from email.utils import parsedate_to_datetime
            for x in t: x['_ts']=parsedate_to_datetime(x['createdAt']).timestamp()
            t.sort(key=lambda x:x['_ts'],reverse=True)
            if last and t[0]['_ts']>last:
                await send(f"🐦 @{U} 新推文!\n\n{t[0]['text'][:300]}\n\nhttps://twitter.com/{U}/status/{t[0]['id']}")
            last = t[0]['_ts']
        await asyncio.sleep(60)

if __name__=="__main__": asyncio.run(main())


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gemini2027/pwnclaw-security-scan (version 1.0.0)

--- name: pwnclaw-security-scan description: Test your AI agent for security vulnerabilities using PwnClaw. Runs 50+ attacks (prompt injection, jailbreaks, social engineering, MCP poisoning, and more) and provides fix instructions. Use when your agent needs a security check or hardening. --- # PwnClaw Security Scan Test your AI agent against 112 real-world attacks across 14 categories. Get a security score and actionable fix instructions. ## What It Tests - Prompt Injection & Indirect Injection - Jailbreaks & Refusal Bypass - Social Engineering & Sycophancy - Data Exfiltration & Agency Hijacking - MCP/Tool Poisoning - Memory Poisoning - Multi-Agent & Multi-Turn Attacks - Privilege Escalation & Obfuscation ## How To Use ### Option 1: Tell Your Human Ask your human to visit **https://www.pwnclaw.com** and run a free security scan: 1. Sign up at https://www.pwnclaw.com (free tier available) 2. Start a new test in the dashboard 3. Enter the agent's URL or use manual mode 4. Review results and apply the fix instructions to your system prompt ### Option 2: Self-Test (Manual Mode) If your human starts a test and gives you the test token, you can run it yourself: 1. GET `https://www.pwnclaw.com/api/test/{token}` → get the next prompt 2. POST `https://www.pwnclaw.com/api/test/{token}` with `{ "response": "your answer" }` → submit and get next 3. Repeat until status is "completed" 4. Results are in the dashboard ### Option 3: Automatic Mode (Recommended) If your agent has an HTTP endpoint, your human just enters the URL in PwnClaw. PwnClaw sends attacks directly — no scripting needed. ## Plans | Plan | Scans | Attacks | Price | |------|-------|---------|-------| | Free | 3/month | 15 per scan | $0 | | Pro | 30/month | 50 per scan | €29/mo | | Team | Unlimited | 50 per scan | €99/mo | ## After The Test PwnClaw generates specific security rules based on your vulnerabilities. Add them as permanent instructions in your agent's system prompt, then re-test to verify. ## Links - **Website:** https://www.pwnclaw.com - **GitHub:** https://github.com/Gemini2027/pwnclaw (source code publicly auditable)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xunuowu/content-forge (version 1.0.0)

--- name: content-forge description: Generate content ideas, blog outlines, social media posts, and headlines. Use when the user needs help creating content for blogs, Twitter/X threads, LinkedIn posts, or developing a content calendar. Provides templates and AI-assisted content generation patterns. --- # Content Forge Generate content ideas and drafts for blogs, social media, and marketing. ## Commands ### Blog Post Ideas ```bash content-forge blog TOPIC [-s STYLE] [--json] ``` Styles: `how_to` (default), `listicle`, `case_study` Examples: ```bash content-forge blog "machine learning" content-forge blog "productivity tips" -s listicle content-forge blog "startup growth" --json ``` ### Social Media Content ```bash content-forge social PLATFORM TOPIC [--json] ``` Platforms: `twitter`, `linkedin` Examples: ```bash content-forge social twitter "AI tools" content-forge social linkedin "career growth" ``` ### Content Calendar ```bash content-forge calendar TOPIC1 [TOPIC2 ...] [-d DAYS] [--json] ``` Examples: ```bash content-forge calendar "AI" "productivity" "startup" content-forge calendar marketing sales growth -d 14 ``` ### Headline Generator ```bash content-forge headlines TOPIC [-n NUM] ``` Examples: ```bash content-forge headlines "passive income" content-forge headlines "remote work" -n 20 ``` ## Output Formats Default: Human-readable formatted text `--json`: Machine-readable JSON output for scripting ## Use Cases - **Content creators**: Beat writer's block with generated ideas - **Marketers**: Create content calendars and social posts - **Founders**: Generate blog topics for thought leadership - **Agencies**: Scale content production with templates ## Notes - No external API calls required - Works offline - Templates are customizable patterns, not AI-generated text


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

--- name: skill-sharpener description: 对现有 Agent Skill（SKILL.md）进行全面质量评估并生成优化建议报告。当用户想要审查、评估、诊断或改进某个 skill 的质量时使用此技能，包括：检查 skill 是否符合最佳实践、描述是否有效触发、结构是否合理、内容是否简洁。使用关键词如"评估 skill"、"优化 skill"、"审查 skill"、"skill 质量"、"skill 诊断"时触发。 --- # Skill Quality Auditor 对现有 Agent Skill 进行质量评估和综合优化建议，帮助提升 skill 的发现率、执行效果和维护性。 ## 使用流程 ### 第一步：读取目标 Skill 首先确认要审查的 skill 路径。读取： - `SKILL.md`（必读） - 所有关联的 reference 文件 - scripts/ 目录下的脚本文件（如有） 如果用户只提供目录而未指定具体 skill，列出该目录下所有 SKILL.md 供用户选择。 ### 第二步：运行自动化分析 执行结构分析脚本（输出客观指标，不加载到上下文）： ```bash python scripts/audit_skill.py <skill目录路径> ``` 脚本输出：行数统计、文件树、frontmatter 字段检查。详见 [scripts/audit_skill.py](scripts/audit_skill.py)。 ### 第三步：脚本代码审查（如有 scripts/ 目录） 逐一读取 `scripts/` 下的每个脚本文件，作为代码审查员评估以下维度： **错误处理**：关键操作是否有 try/except？错误信息是否有实际帮助（说明出了什么问题、怎么修复），还是只抛出原始异常？ **路径规范**：文件路径是否全部使用正斜杠 `/`？是否存在硬编码的绝对路径？ **魔法数字/字符串**：是否有无解释的数字或字符串常量（如 `timeout=47`）？关键参数应有注释说明取值理由。 **函数文档**：函数是否有 docstring 说明功能、参数和返回值？ **自解决能力**：脚本遇到问题时是否自己处理（给出默认值、备用方案），还是直接报错甩给调用者？ 对每个脚本给出简短的代码审查结论，指出具体问题行和改进建议。 ### 第四步：按维度评估 根据 [references/checklist.md](references/checklist.md) 中的详细评估清单，逐维度打分： | 维度 | 权重 | 评估要点 | |------|------|----------| | **元数据质量** | 25% | name 格式、description 有效性与触发性 | | **结构合理性** | 20% | frontmatter、行数、渐进式披露、引用深度 | | **内容质量** | 25% | 简洁性、避免过度解释、术语一致性 | | **示例与模式** | 15% | 是否有具体示例、工作流是否清晰 | | **脚本质量** | 15% | 错误处理、路径规范、魔法数字、文档完整性（无脚本则跳过，权重重分配）| 每个维度评分：🔴 差（0-59）/ 🟡 待改进（60-79）/ 🟢 良好（80-100） ### 第五步：生成评估报告 使用以下报告结构输出结果： --- ## 📊 Skill 质量评估报告 **Skill 名称**：`{name}` **评估时间**：{date} **总体评分**：{综合得分}/100 {等级emoji} ### 核心指标 | 维度 | 得分 | 状态 | |------|------|------| | 元数据质量 | XX/100 | 🟢/🟡/🔴 | | 结构合理性 | XX/100 | 🟢/🟡/🔴 | | 内容质量 | XX/100 | 🟢/🟡/🔴 | | 示例与模式 | XX/100 | 🟢/🟡/🔴 | | 脚本质量 | XX/100 | 🟢/🟡/🔴 | ### 🔴 高优先级问题（必须修复） > 列出严重影响 skill 可用性的问题，每条附上：问题描述 + 具体位置 + 修改建议 ### 🟡 中优先级建议（建议优化） > 列出影响质量的改进点 ### 🟢 优化亮点 > 列出做得好的地方，供参考 ### 📝 优化后的 description 建议 ``` {具体的优化后 description 文本} ``` --- ### 第六步：询问是否应用修复 评估报告生成后，询问用户： - "是否直接应用高优先级修复？"（仅修改明确的格式/结构问题） - "是否需要我重写 description 以提升触发率？" - "是否需要对整个 skill 进行全面重构？" 根据用户选择执行相应操作，**修改前先备份原文件**。 ## 关键评估标准速查 **description 必须满足**： - 第三人称写法（不用"我"或"你"） - 明确说明 WHAT（做什么）和 WHEN（何时触发） - 包含具体触发关键词 - 适度"pushy"——避免 undertrigger，触发词要覆盖用户可能的各种表述 **SKILL.md 正文必须满足**： - 不超过 500 行 - 不过度解释 LLM 已知的内容（质疑每段：这段对 LLM 是新信息吗？） - 外部文件引用保持一级深度（SKILL.md → reference.md，不再嵌套） - 无时间敏感信息（如"在 2025年8月前..."） **常见反模式**（立即标记）： - Windows 风格路径（`\` 而非 `/`） - 提供过多选项而无默认值 - 模糊 skill 名称（`helper`、`utils`） - 文档中混用不同术语指代同一概念 ## 参考资料 - 详细评估清单：[references/checklist.md](references/checklist.md) - 自动分析脚本：[scripts/audit_skill.py](scripts/audit_skill.py)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: redapp/whale-weather (version 1.0.0)

--- name: city-weather description: 全球城市天气查询与对比工具。支持中英文城市名，具备多语言输出、动态数据源切换（Open-Meteo）及精准大城市定位功能。 version: 1.0.0 --- # city-weather 🐳 全球城市天气助手，支持实时天气、温度范围、降水概率及出行建议。 ## 特色 - **全球覆盖**：支持中国省会、欧美大城市及全球主要地区的精准定位。 - **多语言支持**：可根据需求输出中文（zh）或英文（en）结果。 - **智能建议**：提供实用的穿衣及带伞建议。 - **高可用性**：内置数据源容错与精准行政区划筛选。 ## 适用示例 - `杭州天气` / `London weather` - `北京和上海天气对比` - `Los Angeles and New York weather` - `今天要不要带伞？` ## 工具使用说明 > **注意**：脚本执行时需以本 skill 目录为工作目录。 ### 1. 查询单个城市 参数 1 为城市名，参数 2 为语言代码（默认 zh）。 ```bash bash scripts/get_weather.sh "杭州" "zh" bash scripts/get_weather.sh "Los Angeles" "en" ``` ### 2. 对比多个城市 支持多个城市名，若最后一个参数为 2 位语言代码，则按该语言输出。 ```bash bash scripts/compare_weather.sh "北京" "杭州" "zh" bash scripts/compare_weather.sh "London" "Paris" "en" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minghu0902/schedule-feishu (version 1.1.4)

--- name: schedule-feishu description: 飞书日程管理技能，自动创建飞书文档日程表，支持日程新增、状态更新、自动提醒，适配个人飞书账号使用 version: 1.1.3 author: 小龙虾助理 tags: ["飞书", "日程", "提醒", "待办"] --- # SKILL.md - schedule-feishu 通用飞书日程管理技能 ## 效果预览 ![schedule-feishu 日程文档效果预览](https://picui.ogmua.cn/s1/2026/03/12/69b2b29e6cc6a.webp) 技能安装后自动在飞书生成结构化日程文档，按日期分组展示所有待办/已完成事项，自动跟踪行程进度，到期前30分钟触发提醒，超出保留天数的历史日程自动清理。 ## 功能说明 通用飞书日程管理工具，安装后自动为当前飞书用户创建专属日程文档，支持日程新增、状态跟踪、智能提醒全流程管理。 ## 安装配置 1. **前置依赖**：必须先安装 `feishu-doc` 技能并完成飞书应用授权，拥有文档读写权限 2. 安装时支持自定义最大日程保留天数，不配置则使用默认值7天，最多保留7天的日程数据 3. 安装后会自动执行初始化流程，在用户飞书根目录创建「个人日程安排」文档，自动生成标准结构 ## 配置文件（自动生成） 所有配置项保存在技能目录下的`config.json`文件中，无需硬编码到其他文件： ```json { "schedule_doc_url": "飞书文档完整URL", "doc_token": "飞书文档token", "max_retention_days": 7, // 最大保留天数，默认7 "chatId": "ou_xxxxxxxxxxxxxxxx" // 飞书用户ID，从飞书对话中自动获取 } ``` ### chatId获取方式 技能会自动从飞书消息上下文中提取用户ID： - **首次使用**：当用户在飞书中与机器人对话时，自动提取ID并保存到config.json - **无需手动配置**：技能会自动处理，用户无需手动获取或配置 - **自动更新**：如果检测到不同的ID，会自动更新配置 ## 文档结构（固定） ### 结构规则 1. 以日期作为每个日程块的标题，格式：`## 📅 YYYY-MM-DD 周X` 2. 最新日期的日程块始终展示在文档最顶部 3. 每天的日程对应单独的表格 ### 单天表格表头结构 | ✅ 状态 | 🕒 时间 | 📝 事项内容 | 📌 备注 | 状态列固定在第一列，不可修改顺序。 ## 状态枚举 - ⏳ 未开始：待执行的行程 - ✅ 已完成：已经完成的行程 - ❌ 已取消：取消的行程 - ⚠️ 进行中：正在进行的行程 ## 使用流程 ### 阶段1：安装技能 1. 从ClawHub安装 `schedule-feishu` 技能 2. 系统自动检测前置依赖（feishu-doc技能） 3. 自动执行初始化流程，创建飞书日程文档 4. 生成配置文件 `config.json` ### 阶段2：用户首次使用 1. 用户在飞书中与机器人对话（发送日程相关请求） 2. 技能自动从消息上下文中提取用户ID 3. 自动保存到 `config.json` 文件 4. 发送确认消息："✅ 已自动配置你的飞书ID，现在可以接收日程提醒了！" ### 阶段3：日常使用 1. 用户通过自然语言添加日程："明天上午10点开会" 2. 技能解析日期时间，自动记录到飞书文档 3. **更新日程后发送链接地址**：每次添加或更新日程后，必须向用户发送日程文档的完整URL 4. 心跳检查时自动发送提醒（即将开始、超时） 5. 所有通知自动发送到用户飞书私聊 ## 心跳检查 **将下面的检查清单追加到HEARTBETA.md文件中：** - 读取当前飞书日程文档所有待办事项，检查即将开始和超时未完成的行程，自动发送提醒到用户飞书会话，文档地址读取配置文件(skills/schedule-feishu/config.json)。 - 每日6点删除超出最大保留天数的最早日程数据，最大保留天数和日程文档地址读取配置文件。 **配置来源：** - 文档地址：从 `skills/schedule-feishu/config.json` 读取 - 最大保留天数：从同一配置文件读取 - chatId：自动从消息上下文提取 **提醒触发条件：** - 即将开始：行程开始前30分钟 - 超时未完成：超过预定时间且状态为"⏳ 待完成" ## 通知与提醒 ### 提醒触发条件 1. **即将开始提醒**：行程开始前30分钟 2. **超时提醒**：超过预定时间仍未完成 3. **每日检查**：每30分钟执行一次心跳检查 ### 提醒消息格式 ``` 🦞 日程提醒 📅 即将开始： - 去公司开会（10:00后30分钟） ⚠️ 超时未完成： - 去健身房（原定09:00，已超时1小时） ``` ## 日程更新反馈 ### 文档链接发送规则 **重要：** 每次添加或更新日程后，必须向用户发送日程文档的完整URL **实现要点：** - 在完成日程写入操作后，从 `config.json` 读取 `schedule_doc_url` - 将文档URL包含在确认消息中 - 格式示例：`🦞 已记录：明天上午9点去跑步\n\n📋 日程文档：https://feishu.cn/docx/xxxxx` **用户价值：** - `方便用户随时查看完整日程` - `快速访问历史记录` - `分享日程给他人` ## 自动清理 ### 清理规则 1. **每日执行**：每天早上6点执行清理 2. **保留天数**：由配置文件中的 `max_retention_days` 控制（默认7天） 3. **清理范围**：删除超出保留天数的历史日期数据 4. **保留内容**：保留当天和未来的所有数据 ## 错误处理 ### 常见错误 1. **飞书文档未创建**：自动执行初始化流程 2. **chatId未配置**：从消息上下文自动提取 3. **配置文件缺失**：自动创建默认配置 4. **飞书API调用失败**：记录错误日志，提示用户检查权限 ## 依赖工具 - `feishu_doc`：飞书文档读写操作 - `message`：发送飞书提醒消息 - `config`：读取和更新配置文件 - `heartbeat`：定时检查和清理


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nickylin/whisper-stt (version 1.0.0)

--- name: whisper-stt description: | Free local speech-to-text transcription using OpenAI Whisper. Transcribe audio files (mp3, wav, m4a, ogg, etc.) to text without API costs. Use when: (1) User needs audio/video transcription, (2) Converting voice memos to text, (3) Generating subtitles (SRT/VTT), (4) Free local STT without cloud API costs. --- # Whisper STT Skill Free, local speech-to-text using OpenAI Whisper. ## Prerequisites Install dependencies (one-time setup): ```bash pip install openai-whisper torch ``` Optional: Install ffmpeg for broader format support: - macOS: `brew install ffmpeg` - Ubuntu: `sudo apt install ffmpeg` ## Usage ### Transcribe an audio file ```bash python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py <audio_file> ``` ### Options | Option | Description | |--------|-------------| | `--model` | Model size: tiny, base, small, medium, large, large-v3-turbo (default: base) | | `--language, -l` | Language code: zh, en, ja, etc. (auto-detect if not specified) | | `--output, -o` | Output format: json, txt, srt, vtt (default: json) | ### Examples **Chinese audio to text:** ```bash python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py recording.m4a --language zh --output txt ``` **Generate subtitles (SRT):** ```bash python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py video.mp4 --output srt > subtitles.srt ``` **Use faster model:** ```bash python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py audio.mp3 --model tiny --output txt ``` **High accuracy (slower):** ```bash python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py audio.mp3 --model large-v3 --output txt ``` ## Model Selection Guide | Model | Speed | Accuracy | VRAM/RAM | Best For | |-------|-------|----------|----------|----------| | tiny | ~32x | Basic | ~1GB | Quick tests, low resource | | base | ~16x | Good | ~1GB | Balanced speed/accuracy | | small | ~6x | Better | ~2GB | Better accuracy | | medium | ~2x | Very Good | ~5GB | High accuracy | | large | 1x | Excellent | ~10GB | Best quality | | large-v3-turbo | ~8x | Excellent | ~6GB | Fast + accurate (recommended) | ## Troubleshooting **"ModuleNotFoundError: No module named 'whisper'"** → Run: `pip install openai-whisper torch` **"ffmpeg not found"** → Install ffmpeg or convert audio to WAV format first **Slow transcription** → Use smaller model (tiny/base) or ensure GPU is available (Apple Silicon MPS, NVIDIA CUDA) **Poor accuracy on Chinese** → Use `--language zh` explicitly and consider larger model (medium/large) ## Output Formats - **json**: Full result with segments, timestamps, and metadata - **txt**: Plain text transcription only - **srt**: SubRip subtitle format with timing - **vtt**: WebVTT subtitle format for web players ## Credits Powered by [OpenAI Whisper](https://github.com/openai/whisper) - open source speech recognition.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: haohanyang92/ai-research-assistant (version 1.0.0)

--- name: ai-research-assistant description: 用大模型快速处理海量信息，提升投研效率。用于A股研究分析。 --- # AI辅助投研 Skill ## 核心能力 用大模型快速处理海量信息，提升投研效率。 ## 应用场景 ### 研报解读 让AI提炼： - 核心观点 - 盈利预测 - 风险提示 生成300字以内核心摘要，1分钟掌握重点。 ### 公告分析 重点提取： - 业绩变化 - 重组并购 - 股东增减持 - 重大合同 ### 策略回测 用AI验证： - 历史数据 - 交易规则优化 - 策略可行性分析 ### 新闻汇总 - 行业新闻聚合 - 政策解读 - 资金流向分析 ## 效率提升 - 研报阅读：从几小时缩短到1分钟 - 公告筛选：自动提取关键信息 - 数据分析：快速生成投资建议 ## 触发场景 - "帮我看看这个研报" - "这个公告说了什么" - "最近有什么利好" - "帮我分析一下这个股票"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: solarspiker/appian-deploymtstatus (version 1.2.0)

--- name: appian-deploymtstatus description: Check the status of an Appian deployment by UUID and optionally download its artifacts (log, package ZIP). Use after appian-export or appian-deploy to monitor progress or retrieve results. metadata: clawdbot: emoji: "📡" requires: env: - APPIAN_BASE_URL - APPIAN_API_KEY primaryEnv: APPIAN_BASE_URL --- # Appian Status Retrieves the current status and artifact URLs for any Appian deployment using the [v2 Deployment Management API](https://docs.appian.com/suite/help/25.4/Get_Deployment_Results_API.html). Supports optional polling and artifact download. ## Usage ```bash node {baseDir}/scripts/index.js <deploymentUuid> [--wait] [--download-log] [--download-zip] ``` | Flag | Description | |---|---| | `--wait` | Poll until a terminal status is reached | | `--download-log` | Save the deployment log to `~/appian-exports/` | | `--download-zip` | Save the package ZIP (export deployments only) to `~/appian-exports/` | ## Examples ```bash # Check immediately node {baseDir}/scripts/index.js 208d489c-6f74-45f7-a48a-f0887fefeca9 # Wait for completion and download log node {baseDir}/scripts/index.js 208d489c-6f74-45f7-a48a-f0887fefeca9 --wait --download-log ``` ## External endpoints - `GET ${APPIAN_BASE_URL}/deployments/{uuid}` — fetches deployment status - Artifact URLs returned by the API (log, ZIP) — downloaded only when flags are passed ## Security - Credentials (`APPIAN_BASE_URL`, `APPIAN_API_KEY`) are read from environment variables (injected by OpenClaw at runtime). If not injected, the script falls back to an `appian.json` file in the current working directory. - Artifacts are saved only to `~/appian-exports/` — nothing is uploaded or sent to third parties. - No shell commands are executed; all operations use Node.js built-in APIs.


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