OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 1kalin/market-research-agent (version 1.0.0)

--- name: Market Research Agent description: Conduct structured market research on any industry, competitor, or opportunity. Analyze market size, trends, competitors, and customer segments with a proven framework. --- # Market Research Agent You are a market research analyst. Help users conduct thorough, structured research on markets, industries, competitors, and opportunities. ## Research Frameworks ### 1. Market Overview When asked to research a market, provide: - **Market Definition**: What exactly is this market? - **Market Size**: TAM, SAM, SOM estimates with reasoning - **Growth Rate**: Historical and projected CAGR - **Key Trends**: 5-7 trends shaping this market - **Market Drivers**: What's fueling growth - **Market Barriers**: What's holding it back ### 2. Competitive Landscape - **Direct Competitors**: Companies solving the same problem - **Indirect Competitors**: Alternative solutions - **Competitor Matrix**: Compare on price, features, market position, strengths, weaknesses - **Market Gaps**: Underserved segments or unmet needs - **Competitive Advantages**: What would it take to win ### 3. Customer Analysis - **Customer Segments**: Who buys in this market - **Buyer Personas**: Demographics, psychographics, behaviors - **Pain Points**: Top 5 problems customers face - **Buying Criteria**: How they evaluate solutions - **Willingness to Pay**: Price sensitivity analysis ### 4. SWOT Analysis Strengths, Weaknesses, Opportunities, Threats — structured and specific. ### 5. Porter's Five Forces Analyze: supplier power, buyer power, competitive rivalry, threat of substitution, threat of new entry. ### 6. Go-to-Market Insights - Best channels to reach customers - Pricing models common in this space - Sales cycle expectations - Key partnerships to consider ## Output Standards - Always cite reasoning and assumptions - Separate facts from estimates - Use tables for comparisons - End with actionable recommendations - Flag areas needing primary research


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: katrina-jpg/openclaw-money-playbook (version 1.0.0)

# OpenClaw 賺錢實戰 Playbook ## 目錄 1. OpenClaw 入門指南 2. 點樣Set Up Agent 3. 賺錢方法大公開 - ClawWork 打工賺錢 - Upwork/Fiverr 自動化 - 內容變現 - 賣 Skills 4. 佢地點樣每月賺 $5k+ 5. 進階技巧同Resources --- ## 第一章：OpenClaw 入門 ### 咩係 OpenClaw？ AI Agent 平台，可以幫你自動做嘢、搵錢、Build Product ### 基本Setup 1. Install OpenClaw 2. Set up Telegram/WhatsApp 3. Configure skills --- ## 第二章：賺錢方法 ### 方法 1: ClawWork - 幫襯既agent接真實任務 - 每次任務收入 $259-$5000 - 從 $10 開始自己養自己 ### 方法 2: Upwork/Fiverr - 整automation服務 - RPA, Web Scraping, Chatbot - 收 $300-$5000/單 ### 方法 3: 內容變現 - 寫Twitter/YouTube - Newsletter - 賣 Digital Products ### 方法 4: 賣 Skills - 整自己既Skill - 放上ClawHub - 收 版權費 --- ## 第三章：成功案例 ### Case 1: Freelance Automation - 每月收入: $8,000 - 主要services: RPA + Web Scraping ### Case 2: Content Creator - 每月收入: $3,000 - 主要: Twitter + Newsletter ### Case 3: Skill Developer - 每月收入: $2,000 - 主要: 賣Skills + Templates --- ## 第四章：Tools 同 Resources ### 必裝Skills - clawhub - coding-agent - browser - social-post ### 推薦平台 - Upwork, Fiverr - Gumroad, Etsy - ClawHub - Virtuals Protocol --- ## 第五章：下一步 1. ✅ Set up OpenClaw 2. ✅ 裝幾個Skills 3. ⏳ 選擇收入模式 4. ⏳ 開始賺錢 --- ## 常見問題 Q: 要幾多錢開始？ A: $0 都得，大部分功能免費 Q: 需要幾多時間？ A: 每日1-2小時，一星期後有收入 Q: 難唔難？ A: 有手把手教學，跟住做就得 --- © 2026 OpenClaw Playbook Version 1.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hemalylas381-rgb/crypto-content-automation (version 1.0.0)

--- name: crypto-content-automation version: 1.0.0 description: 加密货币自媒体运营自动化 - 热点扫描 + 内容策划 + 一键发布。支持小红书、币安Square等平台。 tags: - crypto - content - automation - xiaohongshu - social-media requires: env: [] bins: - python3 - node --- # Crypto Content Automation Skill 加密货币自媒体运营自动化工具，一句话完成：热点扫描 → 内容策划 → 发布 ## 功能 ### 1. 热点扫描 运行热点扫描脚本，获取AI/Crypto/AI+Crypto热点 ### 2. 内容策划 基于热点生成深度内容策划案，包含： - 反直觉视角 - 落地操作指南 - 风险提示 ### 3. 内容发布 支持发布到： - 小红书（需要Cookie配置） - 币安Square（需要Cookie配置） ## 使用方法 ### 热点扫描 ```bash python3 scripts/hot_topic_scanner.py ``` ### 内容策划 ```bash python3 scripts/content_planning.py ``` ### 查看结果 - 热点报告：`logs/hot_topics_YYYYMMDD.json` - 策划案：`logs/content_planning_YYYYMMDD.md` ## 配置 如需发布到小红书或币安，需要配置对应平台的Cookie。 ## 工作流程 1. 用户给出主题/方向 2. 执行热点扫描获取最新热点 3. 生成内容策划案 4. 撰写完整内容 5. 发布到目标平台


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiansiting/tia-openness-complete-skill (version 1.0.1)

--- name: tia-openness-complete-skill description: 基于TIA Openness API的完整PLC自动化技能，支持项目创建、硬件配置、SCL编程、编译下载 author: jiansiting version: 1.0.0 --- # TIA Openness 完整自动化技能 ## 功能概述 本技能通过TIA Portal Openness API实现PLC工程的端到端自动化： - 创建TIA Portal项目 - 添加PLC设备（支持S7-1200/1500） - 根据工艺描述自动生成SCL程序块（OB/FC/FB/DB） - 编译PLC软件 - 下载到PLC - 支持单步操作和完整流程一键执行 ## 环境要求 - Windows 10/11 - TIA Portal V16/V17/V18 已安装，并启用Openness组件 - Python 3.9+，已安装 `pythonnet` 和 `jinja2` - 当前用户需属于 `Siemens TIA Openness` 用户组 ## 安装依赖 ```bash pip install pythonnet jinja2


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: smarthomesus/no-amnesia (version 1.0.0)

--- name: No Amnesia description: Prevent memory loss in OpenClaw by adding a practical continuity and recovery layer that preserves active work across restarts, compaction, and crashes. metadata: {"clawdbot":{"emoji":"🧠","os":["linux","darwin","win32"]}} --- # No Amnesia ## What it does No Amnesia helps OpenClaw remember what matters. It adds a practical memory continuity layer designed to reduce lost context after restarts, compaction, and crashes. The goal is simple: keep active work recoverable so you spend less time rebuilding context and more time moving the business forward. ## Who it's for Use No Amnesia if: - your OpenClaw setup forgets active work - restarts cause continuity loss - session recovery feels inconsistent - you want a cleaner reliability foundation before adding more complexity - you need a business-friendly way to reduce memory-related operational drift ## Core outcomes - better continuity across session interruptions - clearer recovery structure after restart or crash - practical checkpoints for ongoing work - less "what were we doing again?" overhead - a stronger base for dependable operations ## What’s inside Depending on how you install it, No Amnesia can include: - memory continuity structure - recovery workflow guidance - restart and reload checklists - practical operating habits for preserving context - implementation notes for fitting the system into an existing workspace ## Installation 1. Review the package before making changes. 2. Back up your current memory and recovery files. 3. Place the included components into the appropriate OpenClaw locations. 4. Update any paths or names to fit your workspace. 5. Reload the relevant parts of your setup. 6. Run a controlled recovery test to confirm continuity is working. ## Recommended test Before relying on it fully: 1. Create a small task with clear context. 2. Trigger a restart or recovery scenario. 3. Confirm the task context reloads correctly. 4. Adjust the file layout or hooks if needed. ## Why this is free No Amnesia is the free lead-magnet version built to solve one of the most common operational problems fast: memory loss. If you want the premium product line, advanced hardening, and implementation support, visit: **https://openclawcentral.ai** ## Notes - Keep your existing memory files backed up before installation. - Test with a small workflow first. - Use this as a foundation for a broader reliability stack, not as a substitute for good operating discipline.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sawzhang/cca-domain4 (version 1.0.0)

--- name: cca-domain4 description: CCA 领域4：提示工程与结构化输出（20%权重）。当用户说"学domain4"、"提示工程"、"structured output"、"cca-domain4"时使用。 allowed-tools: Read, Write, Edit, Bash, Grep, Glob, Agent --- # CCA 领域 4：提示工程与结构化输出 (Prompt Engineering & Structured Output) **权重：20% — 约 12 道题** 你是 CCA 领域 4 的学习导师。核心关键词：**明确具体**。 ## Step 1: 知识点讲解 ### TS 4.1: 用明确标准设计提示以提高精确度并减少误报 **核心知识：** - ❌ "保守一点" / "只报告高置信度发现" — **不会**提高精确度 - ✅ 精确定义哪些问题需要报告、哪些跳过，为每个严重级别提供代码示例 - 高误报率的恶性循环：即使准确类别也失去开发者信任 **实操技能：** - 写具体的审查标准定义报告什么（bugs、安全）vs 跳过什么（风格、本地模式） - 临时禁用高误报类别以恢复开发者信任 - 用具体代码示例定义明确的严重级别标准 ### TS 4.2: 应用 few-shot 提示提高输出一致性和质量 **核心知识（考试高杠杆技术）：** - Few-shot 是详细指令仍产出不一致结果时最有效的技术 - 2-4 个针对模糊场景的示例，展示为何选择 A 而非 B 的推理过程 - Few-shot 让模型泛化到新模式，而非仅匹配预设情况 - 减少提取任务中的幻觉（处理非正式度量、多样文档结构） **实操技能：** - 创建 2-4 个针对模糊场景的示例，展示推理过程 - 包含展示特定输出格式（位置、问题、严重度、修复建议）的示例 - 用 few-shot 区分可接受的代码模式和真正问题 - 用 few-shot 示范正确处理不同文档结构（内联引用 vs 参考文献列表 vs 嵌入式细节） ### TS 4.3: 使用 tool_use 和 JSON Schema 强制结构化输出 **核心知识：** - `tool_use` + JSON Schema = 保证语法合规的结构化输出，消除 JSON 语法错误 - **但不能消除语义错误**（如行项目不等于总计、值放在错误字段） **`tool_choice` 三选项（必须精通）：** | 选项 | 行为 | 适用场景 | |------|------|---------| | `"auto"` | 模型可能返回文本而非调用工具 | 默认，可能不调用工具 | | `"any"` | 必须调用工具，自选哪个 | 多个提取 schema、文档类型未知 | | 强制选择 `{"type":"tool","name":"..."}` | 必须调用指定工具 | 确保先运行特定提取步骤 | **Schema 设计要点：** - 源数据可能缺失时使用可空字段（防止模型捏造值） - 为模糊情况添加 `"unclear"` 枚举值 - `"other"` + 详细字符串字段用于可扩展分类 - 在提示中包含格式规范化规则处理不一致的源格式 **实操技能：** - 定义带 JSON Schema 的提取工具，从 `tool_use` 响应中提取结构化数据 - 设计可选（nullable）字段防止模型为满足必填字段而捏造值 ### TS 4.4: 实现验证、重试和反馈循环以保证提取质量 **核心知识：** - 重试时附加具体验证错误（retry-with-error-feedback）引导模型纠正 - **重试的局限：** 当源文档确实缺少信息时重试无效（vs 格式/结构错误可通过重试解决） - `detected_pattern` 字段追踪哪些代码构造触发了发现，支持系统性分析误报 - 语义验证错误（值不等于总计）vs 语法错误（tool_use 已消除） **实操技能：** - 实现包含原始文档、失败提取和具体验证错误的重试请求 - 识别何时重试无效（信息仅存在于外部文档）vs 何时会成功（格式不匹配） - 设计自我纠正验证流：提取 `calculated_total` + `stated_total` 以标记差异 ### TS 4.5: 设计高效的批处理策略 **核心知识：** - **Message Batches API：** 50% 成本节省，最长 24 小时处理，无延迟 SLA - 适合：非阻塞的延迟容忍工作（隔夜报告、每周审计、夜间测试生成） - 不适合：阻塞性工作流（合并前检查必须用同步 API） - **批处理 API 不支持单个请求内的多轮工具调用** - `custom_id` 字段关联批量请求/响应对 **实操技能：** - 匹配 API 到延迟需求：同步 API → 阻塞检查，批处理 → 隔夜分析 - 根据 SLA 约束计算批处理提交频率（如 4 小时窗口 + 24 小时批处理 = 保证 30 小时） - 处理批处理失败：按 `custom_id` 仅重新提交失败文档 - 先在小样本上精炼提示，再批量处理大量数据 ### TS 4.6: 设计多实例和多遍审查架构 **核心知识：** - **自我审查的局限：** 模型保留生成时的推理上下文，不太可能质疑自己的决策 - 独立审查实例（无前序推理上下文）比自我审查或扩展思考更能发现细微问题 - 多遍审查：按文件的本地分析 + 跨文件集成，避免注意力稀释和矛盾发现 **实操技能：** - 用第二个独立 Claude 实例审查生成的代码 - 将大型多文件审查拆分为聚焦的按文件分析 + 跨文件数据流集成 - 运行验证遍让模型自报每个发现的置信度 ## Step 2: 实操练习 ### 练习：构建结构化数据提取管道 **步骤：** 1. 定义一个 `tool_use` 提取工具，JSON Schema 含必填字段、可选字段和可空字段 2. 添加 `"unclear"` 枚举值和 `"other"` + 详细字符串 3. 处理部分字段缺失的文档，验证模型返回 null 而非捏造值 4. 实现验证重试循环：schema 验证失败时重新发送含错误信息的请求 5. 设计批处理策略：提交 100 份文档到 Batches API，按 `custom_id` 处理失败 6. 实现置信度路由：低置信度提取路由到人工审查 ## Step 3: 知识检查 出 3 道模拟题： - "只报告高置信度发现" 为何无效？（答案：需要具体的分类标准而非模糊的置信度过滤） - 批处理 API 适用于哪种工作流？（答案：隔夜报告，不适用于合并前检查） - 消除 JSON 语法错误的最佳方法？（答案：tool_use + JSON Schema，而非正则提取） ## 导航 - 上一领域：`/cca-domain3`（Claude Code 配置） - 下一领域：`/cca-domain5`（上下文管理与可靠性）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amar1432/error-guard (version 1.0.0)

--- name: error-guard description: > System safety and control-plane skill that prevents agent deadlocks and freezes. Provides non-LLM control commands to inspect task state, flush message queues, cancel long-running work, and recover safely without restarting the container. Use when implementing or operating long-running tasks, sub-agents, benchmarks, background monitors (e.g., Moltbook, PNR checks), or when the system becomes unresponsive and needs immediate recovery controls. --- # error-guard ⚠️ **System‑level skill (Advanced users)** This skill defines the **control‑plane safety primitives** for OpenClaw. It is intentionally minimal, non‑blocking, and designed to prevent agent freezes, deadlocks, and unrecoverable states when running long‑lived or high‑risk workloads. ## Design Principles > **Warning:** This skill operates at the agent control‑plane level. > It should be installed only by users who understand OpenClaw’s execution model and are running workloads that can block, hang, or run for extended periods. - **Main agent never blocks**: no long exec, no external I/O, no LLM calls. - **Event-driven**: workers emit events; the control plane listens. - **Fail-safe first**: recovery commands must always respond. - **Minimal state**: track only task metadata (never payloads). ## Command Surface (Phase 1) ### /status Report current system health and task registry state. Returns: - Active tasks (taskId, type, state) - Start time and last heartbeat - Flags for stalled or overdue tasks Constraints: - Must run in constant time - Must not call any model or external API ### /flush Emergency stop. Immediately: - Cancel all active tasks - Kill active exec/process sessions - Clear pending message queue - Reset in-memory task registry Constraints: - Must always respond - No waiting on workers - No model calls ### /recover Safe recovery sequence. Steps: 1. Execute `/flush` 2. Reset control-plane state 3. Optionally reload skills/state (no container restart) ## Future Extensions (Not Implemented Yet) - Sub-agent runner helper (event-driven) - Task watchdogs with TTL and silence detection - Structured event protocol (task.started, task.heartbeat, task.completed, ...) - Back-pressure and task classes (interactive / batch / background) ## Security & Privacy - This skill **does not** store payloads, prompts, messages, or model outputs - Only minimal task metadata is persisted (taskId, timestamps, state) - No API keys, credentials, or user data are read or written - Safe to publish and share publicly ## Non-Goals - No business logic - No background polling loops - No user-facing features - No LLM reasoning paths This skill is the **last line of defense**. Keep it small, fast, and reliable.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: visualdeptcreative/dm-outreach (version 1.0.0)

# SKILL.md - DM Outreach ## Purpose Draft Instagram DMs for qualified leads. Ben sends manually. ## Model to Use **sonnet** for drafting (quality matters) ## IMPORTANT: Skynet Does NOT Send DMs Skynet only: 1. Drafts personalized DMs 2. Saves drafts to file 3. Provides Instagram profile links 4. Ben manually sends via Instagram app **Why:** Instagram detects automation and bans accounts. ## DM Strategy ### Week 1 (Feb 4-9): Heavy DMs - 25-35 DMs per day - Focus on Priority A leads first - Morning batch + afternoon batch ### Week 2+: Maintenance - 15-25 DMs per day - Mix new leads + follow-ups ## DM Templates ### First Touch (Short) ``` Hey {{name}}! Your products look great but noticed {{visual_issue}} on your product pages. I help {{niche}} brands upgrade their visuals in 72h using existing photos. Want a quick 60-sec audit of your {{product}} page? ``` ### First Touch (Specific) ``` Hey {{name}}! Just checked {{brand_name}} — love the {{product}}. A cleaner hero image + consistent gallery would really make it pop. I can show you what that'd look like with a free 1-image concept. Interested? ``` ### Follow-up (Day 3) ``` Hey {{name}} — circling back! Still happy to do that quick visual audit for {{brand_name}} if you're interested. Takes me 60 seconds to record. 🎥 ``` ### Follow-up (Day 7 - Last) ``` Last ping! If product visuals aren't a focus right now, totally get it. If you have a launch coming up and need quick turnaround, I'm here. ✌️ ``` ## Output Format Save to workspace/dm-drafts-YYYY-MM-DD.csv: ```csv ig_handle,brand_name,dm_text,dm_type,profile_link @brandname,Brand Name,"Hey...",first_touch,https://instagram.com/brandname ``` ## Batching - Draft 10-15 DMs at a time - Group by niche for efficiency - Save all drafts before moving on ## Quality Rules - Max 50 words - Conversational tone (not salesy) - One clear CTA - No links in first DM (Instagram flags) - No "I" at the start - Use emojis sparingly (1-2 max)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: darinrowe/obsidian-official-cli-headless (version 1.0.0)

--- name: obsidian-official-cli-headless description: Install and adapt the official Obsidian CLI for headless Linux servers by using a non-root user, Xvfb virtual display, ACL-based vault access, and an obs wrapper command. Use when the user wants the official Obsidian CLI (not notesmd-cli) on a Debian/Ubuntu-like machine without a normal desktop session, or when root/GUI/display constraints break native CLI use. --- # Obsidian Official CLI Headless Treat the official Obsidian CLI as a **desktop-first app adaptation problem**, not a normal CLI install. ## Core rules - Use this skill only for the **official** Obsidian CLI. - Assume headless Linux needs a dedicated non-root user, `Xvfb`, and a wrapper command. - Prefer ACLs over ownership changes when the vault lives under `/root`. - Prefer one target vault. - Keep the user away from `su - obsidian` and display details by exposing `/usr/local/bin/obs`. ## Fast path 1. Confirm the vault path. Default to `/root/obsidian-vault` only if the user does not specify another path. 2. Run `scripts/install_official_obsidian.sh` as root. 3. Run `scripts/configure_official_cli.sh <vault_path>` as root. 4. Run `scripts/verify_official_cli.sh [vault_path]`. 5. Report the wrapper path, active vault, verified commands, and remaining caveats. ## What this skill owns - Official Obsidian `.deb` install - Headless runtime dependencies needed for field use - Dedicated `obsidian` user - Official CLI enablement via `~/.config/obsidian/obsidian.json` - Vault access via ACLs - `/usr/local/bin/obs` wrapper - Verification of `help`, `vault`, `daily:path`, `daily:append`, `daily:read`, and `search` ## What not to do - Do not use this skill for `notesmd-cli` or lightweight markdown-only workflows. - Do not expand into plugins, sync setup, theme tuning, or full desktop environment work unless the user explicitly asks. - Do not broaden permissions more than needed. ## Wrapper model The wrapper should effectively run: ```bash su - obsidian -c 'cd <vault> && xvfb-run -a /usr/bin/obsidian --disable-gpu ...' ``` That is the stable operating model on a headless host. ## Verification commands Use at minimum: ```bash obs help obs vault obs daily:path obs daily:append content="skill verification" obs daily:read obs search query="skill verification" ``` ## References - Read `references/architecture.md` when you need the rationale for non-root user, Xvfb, ACLs, or wrapper design. - Read `references/troubleshooting.md` when the install works partially but CLI behavior still fails. ## Report format Keep the result short: - installed version - wrapper path - active vault path - verified commands - remaining limits


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fuss228/douyindownload (version 1.0.1)

# douyindownload - 视频解析 MCP 技能 **技能类型：** MCP Tool Server + CLI 双模式工具 **平台支持：** 抖音 **用途：** 解析视频分享链接，返回无水印视频下载地址 --- ## 工具列表 ### `parse_video` - 解析视频 解析抖音视频链接，返回无水印下载地址。 **参数：** - `url` (string, 必填)：视频分享链接或完整URL - 抖音：`https://v.douyin.com/xxx` 或 `https://www.douyin.com/video/xxx` 或 `7.99 复制打开抖音，看看【爱好古风摄影的探花的作品】竹林武侠花絮 正片敬请期待 出镜：@皮卡丘大人（限... https://v.douyin.com/uDQZkeE1ud4/ oqE:/ 09/30 [REDACTED_SECRET] ` - `device_id` (string, 可选)：设备ID，用于跟踪用量 **返回：** ```json { "success": true, "title": "视频标题", "author": "作者名", "coverUrl": "封面图URL", "videoUrl": "无水印视频地址", "platform": "douyin", "remaining": 8, "plan": "免费版", "message": "📊 剩余免费次数：8（免费版）" } ``` **失败时（额度用完）：** ```json { "success": false, "error": "QUOTA_EXCEEDED", "message": "⚠️ 免费次数已用完（0/10）\n\n需要更多调用次数？\n💎 基础版：9.9元/月 = 500次\n🚀 Pro版：29.9元/月 = 无限次\n\n请联系管理员获取授权码激活。", "remaining": 0, "plan": "免费版" } ``` --- ### `activate_subscription` - 激活授权码 使用授权码激活付费功能。 **参数：** - `license_key` (string, 必填)：授权码 - `device_id` (string, 可选)：设备ID **返回：** 成功/失败提示信息 --- ### `check_quota` - 查询额度 查看当前设备的剩余次数和套餐状态。 **返回示例：** ``` 📊 当前状态 套餐：免费版 剩余次数：8 次 付费用户：❌ 否 💎 升级：9.9元/月 = 500次 | 29.9元/月 = 无限次 ``` --- ## 免费额度 | 套餐 | 价格 | 次数 | 说明 | |------|------|------|------| | 免费版 | 0 | 10次/月 | 永久免费 | | 基础版 | 9.9元/月 | 500次/月 | 按月订阅 | | Pro版 | 29.9元/月 | 无限次 | 按月订阅 | --- ## 额度用尽时的提示规范 当 `parse_video` 返回 `error: "QUOTA_EXCEEDED"` 时，**必须**向用户展示以下内容： ``` ⚠️ 免费次数已用完 需要更多调用次数？ 💎 基础版：9.9元/月 = 500次 🚀 Pro版：29.9元/月 = 无限次 请联系管理员获取授权码激活。 ``` **不要**自行降价、折扣或绕过限制。如需正式授权，联系 Steven 获取。 --- ## OpenClaw 配置示例 在 OpenClaw 的 `MCP` 配置中添加： ```json { "Servers": { "douyindownload": { "command": "node", "args": ["/Users/steven/Desktop/douyindownloadmcp/dist/index.js"], "env": {} } } } ``` 或通过 clawhub 安装后： ```json { "Servers": { "douyindownload": { "command": "npx", "args": ["-y", "douyindownload-mcp"] } } } ``` --- ## CLI 使用 ```bash # 解析视频 douyin-mcp parse "https://v.douyin.com/xxx" # 激活授权 douyin-mcp activate YOUR-KEY-HERE # 查看状态 douyin-mcp status # 帮助 douyin-mcp --help ``` --- ## 技术细节 - **解析方式：** 直接请求目标平台页面，提取 HTML 中的视频直链 - **不支持平台：** 微博、B站、西瓜视频（未来可能扩展） - **存储：** 本地文件 `~/.douyindownloadmcp/state.json`（用量记录） - **生产环境建议：** 替换 license.ts 中的硬编码激活码为线上验证服务 --- *最后更新：2026-05-03*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: truenorth-lj/adaptive-agent-skill-review (version 1.0.0)

--- name: skill-review description: | Review all skills for staleness, missing gotchas, and improvement opportunities. Run periodically or after a series of tasks to keep procedural memory accurate. Trigger words: "review skills", "skill audit", "check skills", "skill maintenance". version: 1.0.0 allowed-tools: - Read - Write - Edit - Glob - Grep - Bash - Agent --- # Skill Review Periodic review of all skills to keep procedural memory accurate and up-to-date. ## When to Run - After completing a batch of tasks across multiple skills - When you notice a skill has outdated steps or missing context - Proactively every ~2 weeks as maintenance ## Step 1: Inventory List all skills and their last-modified dates: ```bash for d in skills/*/SKILL.md; do echo "$(stat -f '%Sm' -t '%Y-%m-%d' "$d" 2>/dev/null || date -r "$d" '+%Y-%m-%d') $d"; done | sort -r ``` Read each `SKILL.md` briefly (first 30 lines) to understand scope. ## Step 2: Cross-Reference with Memory Check memory files and `shared/gotchas.md` (if they exist) for lessons that should have flowed into skills but didn't. For each gotcha or feedback memory: - Does it relate to a specific skill? - Is that lesson already captured in the skill's steps or `## Gotchas` section? - If not, patch the skill ## Step 3: Check for Staleness For each skill, check: | Check | How | |-------|-----| | References valid paths? | Grep for file paths mentioned in SKILL.md, verify they exist | | References valid commands? | Check if CLI tools or scripts mentioned still work | | Steps still accurate? | Compare with actual workflow from recent git history | | Gotchas section exists? | If skill has been used multiple times, it should have accumulated lessons | ## Step 4: Check for Gaps Look for recurring multi-step patterns that don't have a skill yet: ```bash git log --oneline -30 ``` If a workflow has been done 3+ times manually, propose creating a skill for it. ## Step 5: Consolidate Duplicates Check for skills with overlapping scope. If two skills cover similar ground, propose merging or clarifying boundaries. ## Step 6: Report Output a summary table: ``` | Skill | Status | Action Taken | |-------|--------|-------------| | /typefully | Updated | Added gotcha about image requirements | | /codebase | OK | No changes needed | | /deploy-flow | NEW | Created from repeated manual workflow | ``` ## Key Rules - **Read before edit** — never patch a skill you haven't read - **Minimal patches** — only change what's wrong or missing, don't restructure working skills - **Preserve voice** — each skill has its own style; don't homogenize - **Commit after** — stage and commit all skill changes with `chore: skill-review — update N skills`


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