OpenClaw/clawhub-security-signals huggingface dataset:
Skill: felipegoulu/passo (version 1.0.0)

# Passo - Remote Browser Access Give your user remote access to a browser on your server. Perfect for logins, 2FA, captchas, or any manual browser action. ## Installation Run this on the server where you want the browser: ```bash curl -fsSL https://raw.githubusercontent.com/felipegoulu/passo-client/main/install.sh | bash ``` The script will: 1. Prompt the user to sign up at getpasso.app (if no account) 2. Install dependencies (Chromium, VNC, etc.) 3. Create the `passo` command ## Your Browser URL {{ACCESS_URL}} Protected by: {{EMAIL}} ## Commands ```bash passo start # Start the browser tunnel passo stop # Stop everything passo status # Check if running ``` ## How to use 1. Send the URL above when you need human help 2. User opens it on their phone/laptop 3. User signs in with Google (only their email can access) 4. User does the action (login, 2FA, captcha, etc.) 5. User says "done" 6. You continue ## Pricing $5/month after 30-day free trial. Managed via getpasso.app/dashboard. ## Links - Website: https://getpasso.app - Docs: https://getpasso.app/docs


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: calvinxhk/botlearn-writer (version 1.0.0)

--- name: writer role: Article Writing Specialist version: 1.0.0 triggers: - "write an article" - "write about" - "compose" - "draft article" - "blog post" --- # Role You are an Article Writing Specialist. When activated, you produce structured, evidence-based articles with clear thesis statements, well-supported arguments, and consistent style. You leverage summarization capabilities to distill research and keyword extraction to optimize topic coverage and SEO relevance. # Capabilities 1. Construct well-organized articles following established structural patterns (inverted pyramid, narrative arc, analytical framework) appropriate to the topic and audience 2. Develop clear, defensible thesis statements and decompose them into supporting arguments with logical progression 3. Integrate multiple evidence types (statistical data, expert quotes, case studies, analogies) to substantiate claims while maintaining readability 4. Maintain consistent tone, voice, and style throughout the article, adapting register to the target audience and publication context 5. Use @botlearn/summarizer to condense research material into key points for evidence integration 6. Use @botlearn/keyword-extractor to identify core terms, optimize topic coverage, and ensure semantic completeness # Constraints 1. Never present claims without supporting evidence — every assertion must be backed by data, expert opinion, or logical reasoning 2. Never switch tone or register mid-article without deliberate rhetorical intent — style must remain consistent from introduction to conclusion 3. Never produce an article without a clearly identifiable thesis statement within the first two paragraphs 4. Never use a single evidence type exclusively — diversify between statistics, expert testimony, examples, and logical arguments 5. Always include a strong conclusion that reinforces the thesis and provides forward-looking insight or a call to action 6. Always verify that each paragraph serves a clear purpose in advancing the overall argument # Activation WHEN the user requests article writing, composition, or blog post creation: 1. Analyze the topic, target audience, desired length, and publication context 2. Use @botlearn/keyword-extractor to identify core terms and related concepts for comprehensive topic coverage 3. Use @botlearn/summarizer to distill any provided research material or references into usable evidence points 4. Follow strategies/main.md for the 7-step writing workflow 5. Apply knowledge/domain.md for article structure selection and argumentation framework 6. Ensure quality using knowledge/best-practices.md for thesis development, evidence integration, and style consistency 7. Verify against knowledge/anti-patterns.md to avoid weak thesis, unsupported claims, and tone inconsistency 8. Output a complete, publication-ready article with clear structure and sourced evidence


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fanwu-fan/paper-report-delivery (version 1.0.0)

--- name: paper-report-delivery description: Build, automate, or maintain a daily paper-report delivery pipeline that collects papers, selects A/B groups, generates Chinese summaries and detailed innovation analysis, produces readable HTML with embedded images, prepares Telegram message chunks, archives outputs, and delivers reports to Telegram with retry and HTML-then-fallback behavior. Use when asked to create or improve a paper/news digest workflow, daily report automation, Telegram delivery pipeline, readable HTML report generation, or fallback delivery logic for unstable Telegram document sending. --- # Paper Report Delivery Use this skill to implement or maintain an end-to-end daily paper report workflow. ## Do this 1. Read `references/workflow.md` first. 2. Keep the pipeline end-to-end: collection, selection, readable output, archive, delivery. 3. Prefer Telegram-only proxying via `channels.telegram.proxy` when Telegram API needs Clash. 4. Treat Telegram message delivery and Telegram document delivery as separate reliability paths. 5. Prefer this delivery order: - split HTML delivery - retry for a bounded window - Telegram message chunk fallback 6. Keep local artifacts even when remote delivery fails. ## Reusable scripts Use these bundled scripts when they fit: - `scripts/build_readable_html.py` - Build a readable single-file HTML report with embedded images. - `scripts/build_telegram_messages.py` - Convert a readable report JSON into Telegram-sized message chunks. - `scripts/send_telegram_retry.sh` - Retry Telegram sending for unstable transport. - `scripts/send_html_then_fallback.py` - Try split HTML for up to 30 minutes, then fall back to Telegram message chunks. ## Implementation rules - Keep HTML as the best-reading artifact. - Keep Telegram message chunks as the most reliable delivery artifact. - Archive HTML, PDF, and markdown even when only Telegram text is delivered. - Do not assume Telegram document delivery is stable. - If Telegram is unreachable, finish artifact generation and leave cached delivery files behind. ## When editing an existing repo - Integrate into the repo’s main pipeline entrypoint instead of creating a disconnected side script. - Add flags for skipping fetch, skipping send, and archive-only mode. - Keep delivery logic explicit and observable in logs. - If Telegram transport is flaky, add retries before changing report content. ## Publishing hygiene Before public release: - Remove hard-coded local paths - Remove hard-coded Telegram targets and group names - Replace fixed dates with dynamic `YYYY-MM-DD` handling - Keep transport settings configurable ## Packaging After the skill is complete, package it with the local `package_skill.py` helper from the OpenClaw `skill-creator` skill.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chensu1234/media-inspector (version 1.0.0)

--- name: media-inspector description: 本地音视频文件分析工具。支持扫描媒体文件、提取元数据、语音转文字（Whisper）、生成摘要和关键片段。支持 MP4/MOV/MKV/MP3/WAV/M4A/FLAC 等格式。 --- # Media Inspector 📺 🎵 本地音视频文件分析工具，可扫描、提取元数据、转录、摘要和导出报告。 ## 支持格式 **视频**: mp4, mov, mkv, avi, webm, m4v, ts, mpg, mpeg **音频**: mp3, wav, m4a, aac, flac, ogg, opus, aiff, wma ## 快速开始 ### 1. 扫描媒体文件 ```bash python scripts/scan_media.py /path/to/media --out-dir ./media_scan ``` 输出文件： - `scan_results.json` - JSON格式 - `scan_results.csv` - CSV格式 - `scan_results.md` - Markdown格式 ### 2. 深度分析 ```bash python scripts/analyze_media.py /path/to/file.mp4 --out-dir ./media_analysis ``` 分析内容： 1. 元数据提取（ffprobe） 2. 语音转文字（Whisper，可选） 3. 内容摘要 4. 关键片段提取（带时间戳） 5. 报告导出（JSON/CSV/Markdown） ## 依赖安装 ```bash # 必需：ffmpeg (包含 ffprobe) brew install ffmpeg # 可选：Whisper 语音转文字 pip install whisper # 或 brew install whisper ``` ## 使用规则 - ✅ 优先使用转录文本进行分析，不要仅凭文件名猜测 - ⚠️ Whisper 不可用时，明确告知用户并仅返回元数据 - ❌ 不要虚构内容摘要 - 📝 关键片段必须包含时间戳 - 📊 批量分析时，每个文件单独报告 ## 输出示例 ### 扫描报告 ```markdown # Media Scan Report ## Summary - scanned path: /path/to/media - files found: 10 - audio files: 3 - video files: 7 ## Candidates | filename | duration | type | file size | |---|---|---|---| | video.mp4 | 01:30:00 | video | 500.2 MB | | audio.mp3 | 00:05:30 | audio | 5.2 MB | ``` ### 分析报告 ```markdown # Media Analysis Report ## File - path: /path/to/video.mp4 - type: video - duration: 01:30:00 ## Transcript - available: yes - whisper used: yes ## Summary [基于转录文本的摘要内容] ## Key excerpts 1. [00:01:30 - 00:02:15] 关键片段内容... 2. [00:15:00 - 00:16:30] 另一个关键片段... ```

path: CLAWHUB.json
content: {
  "name": "media-inspector",
  "displayName": "Media Inspector",
  "description": "本地音视频文件分析工具 - 扫描、提取元数据、语音转文字、生成摘要和关键片段",
  "version": "1.0.0",
  "author": "chen su",
  "tags": ["media", "video", "audio", "transcription", "ffmpeg", "whisper"],
  "license": "MIT"
}


path: agents/openai.yaml
content: interface:
  display_name: "Media Inspector"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: adminlove520/pinchtab-helper (version 0.1.0)

# PinchTab Browser Control 🖱️ 让 AI 通过 PinchTab 控制浏览器的 Skill。 ## 什么是 PinchTab? PinchTab 是一个轻量级的浏览器控制工具，比传统的浏览器自动化更高效： - Token 高效：800 tokens/page（比截图便宜 5-13 倍） - 稳定性好：使用稳定的 accessibility refs - 支持多实例：可同时运行多个浏览器实例 ## 使用前提 1. **安装 PinchTab**（需要先安装）： ```bash # macOS / Linux curl -fsSL https://pinchtab.com/install.sh | bash # 或 npm npm install -g pinchtab # 或 Docker docker run -d --name pinchtab -p 127.0.0.1:9867:9867 pinchtab/pinchtab ``` 2. **启动服务**： ```bash pinchtab & ``` ## 核心操作 ### 1. 导航到网页 ```bash pinchtab nav https://example.com ``` ### 2. 获取页面快照 ```bash pinchtab snap -i -c # -i: interactive 模式（可交互元素） # -c: 显示点击引用 ``` ### 3. 点击元素（通过 ref） ```bash pinchtab click e5 ``` ### 4. 输入文字 ```bash pinchtab fill e3 "搜索内容" ``` ### 5. 提交表单 ```bash pinchtab press e7 Enter ``` ### 6. 截图 ```bash pinchtab screenshot ``` ## 完整示例 ### 搜索任务 ```bash # 1. 打开 Google pinchtab nav https://google.com # 2. 获取页面 pinchtab snap -i -c # 3. 在搜索框输入（假设 ref 是 e5） pinchtab fill e5 "什么是 AI" # 4. 按回车 pinchtab press e5 Enter # 5. 获取结果 pinchtab snap -i -c ``` ## API 端点 如果直接用 HTTP： - 基础 URL: http://localhost:9867 - 创建实例: POST /instances/launch - 打开标签: POST /instances/{id}/tabs/open - 快照: GET /tabs/{id}/snapshot - 操作: POST /tabs/{id}/action ## Skill 触发词 - 打开浏览器 - 浏览网页 - 搜索xxx - 访问xxx网站 - 帮我操作浏览器 - 用 pinchtab ## 注意事项 1. **安全**：PinchTab 默认只绑定 127.0.0.1 2. **Profile**：建议使用专门的自动化 profile 3. **Token**：如果需要远程访问，设置 BRIDGE_TOKEN --- Made with ❤️ by 小溪


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: fanyanggod/tiktok-shop-ad-library-analytics (version 1.0.2)

--- name: tiktok-shop-ad-library-analytics description: Research TikTok ads alongside TikTok Shop products and stores using PipiAds ad and TikTok Shop analytics tools. metadata: openclaw: requires: env: - PIPIADS_API_KEY bins: - npm install: command: npm args: - install - -g - "pipiads-mcp-server@1.0.3" primaryEnv: PIPIADS_API_KEY emoji: "📊" homepage: https://www.pipiads.com mcpServers: pipiads: command: pipiads-mcp-server env: PIPIADS_API_KEY: "{{PIPIADS_API_KEY}}" --- # TikTok Shop Ad Library & TikTok Shop Analytics Use this skill when the user wants TikTok ad research together with TikTok Shop product and store analytics. ## Setup 1. Visit the official website at [https://pipiads.com/](https://pipiads.com/) to view real-time dashboard data and product UI. 2. Visit [https://www.pipispy.com/](https://www.pipispy.com/) to get your API key and manage billing/recharge. 3. Set the environment variable: `PIPIADS_API_KEY` ## When to Use - Find TikTok ads tied to strong TikTok Shop products - Analyze TikTok Shop product momentum, GMV, sales, and pricing - Research TikTok Shop stores and compare store-level positioning - Connect ad creatives with shop product and store performance ## When NOT to Use - The user only wants Facebook ad library or monitoring - The user wants general ecommerce product research across both platforms - The user wants only natural TikTok content without shop analytics ## Core Tools ### TikTok Ads - `search_ads` - `get_ad_detail` - `search_products` - `get_product_detail` ### TikTok Shop Products & Stores - `search_tiktok_products` - `get_tiktok_product_detail` - `search_tiktok_shops` - `get_tiktok_shop_detail` ## Recommended Workflow 1. Use `search_ads` to find TikTok creatives in the niche. 2. Use `search_tiktok_products` to find strong shop products in the same niche. 3. Inspect product and store records with detail tools. 4. Summarize how creative patterns connect to shop performance. ## Cost Awareness Each API call consumes credits from your PipiAds account: - **List/Search**: 1 credit per result returned - **Detail**: 1 credit per request (free if queried within 3 days) Ask for category, region, price band, and time window before broad TikTok Shop research.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wociaozhongyunonghaole/zmt-browser-matrix (version 1.0.16)

--- name: ZMT Matrix Browser slug: zmt-browser-matrix version: 1.0.16 description: | ZMT Browser Matrix Manager - Control multi-account browser matrix via HTTP API author: @wociaozhongyunonghaole --- # ZMT Matrix Browser Control multi-account browser matrix tool via HTTP API for account startup, shutdown and information query. ## Features - Independent browser environment per account - Independent IP support - Independent Cookie/cache - HTTP API control ## Requirements - Python 3.8+ - Install: pip install requests pyyaml - Matrix tool: https://zmt.scys6688.com/ ## Commands | Command | Description | |---------|-------------| | list | List all accounts | | start | Start account by name | | stop | Stop account by name | ## Natural Language Supports: "list all accounts", "start xiaohongshu", "stop xiaohongshu"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luweicn/opportunity-deep-dive (version 1.0.0)

--- name: opportunity-deep-dive description: 对一个业务方向进行全面深入的调研分析，产出完整报告和商业计划书。当用户说"我对XX方向感兴趣"、"帮我深入调研XX"、"XX值得做吗"、"帮我分析这个方向"时触发。输入一个业务方向，自动执行市场研究、竞品分析、风险评估、商业模式设计，最终产出商业计划书并上传到飞书知识库。 --- # 商机深入调研 对感兴趣的业务方向进行全面调研，产出完整报告。 ## 输入 用户提供一个感兴趣的业务方向，例如： - "我对AI小说创作感兴趣" - "帮我深入调研AI短剧制作" - "自媒体自动化运营值得做吗" ## 工作流程 ### 第一步：确认方向 与用户确认调研方向： - 具体的业务领域是什么？ - 目标市场（国内/国外）？ - 有没有特别关注的点？ ### 第二步：执行调研 依次调用以下技能： **2.1 商机评分（opportunity-scout）** ``` 读取技能：/root/.openclaw/workspace/skills/opportunity-scout/SKILL.md 执行：对该方向进行评分，输出需求/竞争/可行性/变现四个维度得分 ``` **2.2 市场研究（market-research）** ``` 读取技能：/root/.openclaw/workspace/skills/market-research/SKILL.md 执行：市场规模估算、客户验证、数据来源 ``` **2.3 竞品分析（competitive-analysis）** ``` 读取技能：/root/.openclaw/workspace/skills/competitive-analysis/SKILL.md 执行：识别竞品、分析优劣势、找差距和切入点 ``` **2.4 风险评估（opportunity-assessment）** ``` 读取技能：/root/.openclaw/workspace/skills/opportunity-assessment/SKILL.md 执行：风险分级、待确认事项、是否建议投入 ``` **2.5 商业模式设计（business-model-canvas）** ``` 读取技能：/root/.openclaw/workspace/skills/business-model-canvas/SKILL.md 执行：设计商业模式画布，验证商业逻辑 ``` **2.6 商业计划书（business-plan）** ``` 读取技能：/root/.openclaw/workspace/skills/business-plan/SKILL.md 执行：撰写完整商业计划书（12个部分） ``` ### 第三步：产出报告 生成以下文档： | 文档 | 内容 | |------|------| | 01-商机评分报告 | 评分结果和建议 | | 02-市场研究报告 | 市场规模、趋势、客户分析 | | 03-竞品分析报告 | 竞品对比、差距分析、切入点 | | 04-风险评估报告 | 风险分级、待确认事项 | | 05-调研结论报告 | 最终建议和替代方向 | --- ### 第四步：上传飞书知识库 **知识库ID：** 7293072412831481884 #### 步骤1：检查知识库权限 ```bash feishu_wiki action=spaces ``` 确认能看到知识库列表，如果没有权限，提示用户添加机器人到知识库。 #### 步骤2：创建一级目录 ```bash feishu_wiki action=create space_id=7293072412831481884 obj_type=docx title="[业务方向名称]" ``` 返回 node_token 和 obj_token。 **注意：** 一级目录的文档内容应该是概要+目录索引，不是完整报告！ #### 步骤3：创建子文档 ```bash feishu_wiki action=create space_id=7293072412831481884 parent_node_token=[一级目录node_token] obj_type=docx title="01-商机评分报告" ``` 为每个报告创建一个子文档，获取各自的 obj_token。 #### 步骤4：写入文档内容 ```bash feishu_doc action=write doc_token=[文档token] content="[报告内容]" ``` **重要：** - 一级目录文档：写概要+目录索引 - 各子文档：写对应的完整报告内容 #### 最终目录结构 ``` 知识库：商业化部门 (7293072412831481884) └── [业务方向名称]/（概要+目录索引） ├── 01-商机评分报告（完整内容） ├── 02-市场研究报告（完整内容） ├── 03-竞品分析报告（完整内容） ├── 04-风险评估报告（完整内容） └── 05-调研结论报告（完整内容） ``` --- ## 输出格式 ### 报告模板 **注意：飞书消息不支持 markdown 表格，报告内容使用纯文本格式！** 每份报告包含： - 标题 - 执行摘要 - 详细内容 - 结论和建议 ### 最终汇报 调研完成后，向用户汇报： 1. 该方向的评分结果 2. 主要发现和结论 3. 是否建议投入 4. 飞书知识库链接 --- ## 注意事项 - 每个技能调用前先读取其 SKILL.md - 报告要求数据支撑，不要空泛 - 风险评估要诚实，不要回避问题 - 商业计划书要适合一人公司，关注自动化可行性 - 如果某步骤发现方向不可行，及时告知用户，不必继续后续步骤 **飞书知识库操作要点：** - 一级目录文档写概要+索引，不是完整报告 - 各子文档写对应的完整报告内容 - 创建文档后立即写入内容，不要等所有文档创建完再写


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: malicious | SkillSpector: clean (LOW)
Skill: jfox85/toneclone-cli (version 1.0.0)

--- name: toneclone-cli description: "Write in the user's authentic voice using ToneClone. Generate emails, messages, social posts, and other content that sounds like the user — not generic AI. Use trained personas for different writing styles and knowledge cards for context. Requires ToneClone account and CLI setup at toneclone.ai." homepage: https://toneclone.ai metadata: { "openclaw": { "emoji": "✍️", "requires": { "bins": ["toneclone"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "toneclone/toneclone/toneclone", "bins": ["toneclone"], "label": "Install ToneClone CLI (brew)" } ] } } --- # ToneClone CLI Generate content in the user's authentic voice using their trained ToneClone personas. ## Setup ToneClone requires an account and trained personas. Get started at https://toneclone.ai **Install CLI (Homebrew):** ```bash brew tap toneclone/toneclone brew install toneclone ``` **Authenticate:** ```bash toneclone auth login ``` ## Writing Content ```bash toneclone write --persona="<name>" --prompt="<what to write>" ``` ### With Knowledge Cards ```bash toneclone write --persona="<name>" --knowledge="<card1>,<card2>" --prompt="<prompt>" ``` ### Examples **Chat reply:** ```bash toneclone write --persona="Chat" \ --prompt="Reply to: 'Hey, are you free this weekend?' — say yes, suggest Saturday" ``` **Work email:** ```bash toneclone write --persona="Work Email" --knowledge="Work,Scheduling" \ --prompt="Follow-up email about project timeline, offer to schedule a call" ``` **Social post:** ```bash toneclone write --persona="Twitter" \ --prompt="Announce our new feature launch, keep it punchy" ``` ## Providing Context Pass relevant context in the prompt for better results: - Thread/conversation being replied to - Background on the topic - Recipient info or relationship context ## Quick Commands | Task | Command | |------|---------| | Write content | `toneclone write --persona="Name" --prompt="..."` | | List personas | `toneclone personas list` | | List knowledge cards | `toneclone knowledge list` | | Check auth | `toneclone auth status` | ## Personas vs Knowledge Cards | Need | Use | |------|-----| | Different writing style | Different **persona** | | Different context/facts | Different **knowledge card** | ## More Info - Full documentation: https://toneclone.ai/cli - Create personas & training: https://app.toneclone.ai - Source code: https://github.com/toneclone/cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chegangan/daily-englishnews-reader-learning-globalnews (version 1.0.1)

--- name: Daily-EnglishNews-Reader version: 1.0.0 description: 单次生成符合i+1学习法的英语阅读材料，自动适配CEFR难度，生成飞书云文档 author: lihua license: MIT required_extensions: - "@larksuite/openclaw-lark-tools" tags: - 英语学习 - RSS - 自动生成 - i+1学习法 metadata: { "openclaw": { "emoji": "📚", "requires": { "bins": ["python3", "pip3"] }, "install": [ { "id": "lark-plugin", "kind": "shell", "command": "npx -y @larksuite/openclaw-lark-tools install", "label": "Install official Lark/Feishu plugin", }, { "id": "pip-deps", "kind": "shell", "command": "pip3 install requests feedparser", "label": "Install Python dependencies (requests + feedparser)", }, ], }, } --- # English Daily Reader Skill for OpenClaw ## 功能说明 单次生成符合i+1学习法的英语阅读材料，解决两个核心痛点： 1. 自动搜索最新全球高质量文章，内容覆盖全球新闻、经济、科技、科学文化，学英语的同时了解行业资讯 2. 自动适配CEFR英语难度等级（A1/A2/B1/B2/C1），提供刚好匹配水平的学习语料 ### 核心特性 ✅ 每次调用从4个已经指定的分类RSS源各选1篇高质量文章（全球政治、经济、科技、科学文化） ✅ 改写为指定难度与字数的英文文章 ✅ 每篇文章提取指定数量的+1难度生词，附带英文释义和语境例句 ✅ 生成新的飞书云文档，直接返回文档链接 ✅ 自动去重，不会重复推荐已经生成过的文章 ## 使用方法 ### 前置检查 使用前请需要确保已安装飞书官方OpenClaw插件： 1. 执行安装命令：`npx -y @larksuite/openclaw-lark-tools install`（安装出错可加`sudo`重试） 2. 安装过程中选择「新建机器人」扫码创建，或「关联已有机器人」 3. 创建完成后在飞书给机器人发任意消息激活，发送`/feishu auth`完成批量授权 4. 验证安装：发送`/feishu start`，返回版本号即安装成功 如果没有安装插件，当用户调用本Skill时你要引导用户完成上述安装步骤。 ### 首次使用 当用户第一次调用本Skill时，你需要询问用户以下配置信息，并修改配置文件完成配置： 1. **阅读难度**：显示默认 A2，CEFR等级（A1/A2/B1/B2/C1，难度递增） 2. **每篇文章字数**：显示默认 350 （可自定义） 3. **每篇在文章最后讲解的可能为i+1难度的生词数量**：显示默认 3 个/篇（可自定义） ### 配置文件 所有配置都在`config`目录下，每次运行前读取一次： - `config/config.json`：基础阅读配置，包含阅读难度、每篇文章字数、每篇讲解生词数量 - `config/rss_sources.json`：预设RSS源，你需要从这里查找用户设置的rss源 - `config/sent_articles.json`：已生成过的文章去重列表，读取过的文章你需要记录在这里，避免下次重复读取 ### 调用方式与运行步骤 当用户发送类似意思的指令：`生成英语阅读材料` 或 `调用Daily-EnglishNews-Reader`，你会自动完成以下步骤： 1. 读取`config/rss_sources.json`配置 2. 运行`scripts/random_source_picker.py`，这个脚本会随机从4个rss分类中各选一个RSS源 3. 从每个源挑选一篇完整的且未在`config/sent_articles.json`中记录的高质量文章 4. 按照用户配置的难度、字数改写文章,在不影响理解的前提下，要求用词地道，且尽量保留专业技术用语 5. 在改写后的文章中，根据用户配置的讲解生词数量，在生成的短文中选取可能属于+1难度的生词加粗,在短文末尾列出所有加粗生词的英文释义和在短文中出现的语境例句； 6. 生成全新的飞书云文档，写入4篇文章内容，并附上来源和文章原始URL 7. 返回文档链接给用户 8. 将本次生成的文章的来源、标题、URL记录到`config/sent_articles.json`中，避免后续重复推荐 `config/config.json`默认配置： ```json { "reading": { "difficulty": "A2", "words_per_article": 350, "words_per_article_to_explain": 3 } } ``` ## 依赖工具 - `python3` 用于RSS源随机选择 - 飞书官方OpenClaw插件：`@larksuite/openclaw-lark-tools`

path: config/sent_articles.json

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: guyxlouspg/cat-viking-memory (version 1.0.0)

# Viking 记忆系统技能 ## 功能 - 五级记忆层级自动管理（L0→L4） - 重要记忆保护（永不降级） - 向量相似度提及检测（动态重要性） - 飞书群聊会话自动保存（需飞书插件） - 跨 Agent 共享（global workspace） - 自动降级与遗忘算法（LLM 压缩） ## 使用方式 ### 1. 安装技能 确保以下文件已复制到你的技能目录： ``` cat-viking-memory/ ├── config.json ├── SKILL.md ├── memory-pipeline/ # CLI 主入口和脚本 ├── simple-viking/ # 向量搜索 └── references/ # 文档 ├── README.md # 详细使用说明 └── 飞书集成说明.md ``` ### 2. 基本配置 编辑 `config.json` 中的配置： ```json { "memory_workspace": "~/.openclaw/viking-{agent}", "auto_save_enabled": true, "feishu_integration": { "enabled": false } } ``` ### 3. 定时任务（推荐） 每天凌晨 3 点自动执行记忆降级： ```bash # crontab -e 0 3 * * ~/.openclaw/workspace/agents/maozhuli/cat-viking-memory/memory-pipeline/memory-tier-cron.sh ``` ### 4. 飞书自动保存（可选） 如需飞书群聊会话自动保存： 1. 确保 feishu 插件已安装并启用 2. 修改 `config.json`： ```json "feishu_integration": { "enabled": true, "check_interval_ms": 300000, "timeout_ms": 1800000 } ``` 3. 在 feishu 插件配置中启用 SessionManager 4. 参考 `references/飞书集成说明.md` 详细步骤 ### 5. 常用命令 ```bash # 加载记忆上下文 memory-pipeline mp_autoload # 保存记忆 memory-pipeline mp_global "任务完成说明" # 搜索记忆 sv_find "关键词" # 手动触发降级（测试） memory-pipeline/memory-tier-cron.sh ``` ## 相关文档 - **详细使用说明**：`references/README.md` - **飞书集成指南**：`references/飞书集成说明.md` - **记忆文件格式**：见 `references/README.md` 的"记忆文件格式"章节 ## 技术架构 ``` 存储流程： 输入 → Ontology映射 → Viking存储 → 向量索引 → 检索 层级管理： L0(0-1天) → L1(2-7天) → L2(8-30天) → L3(30-90天) → L4(90天+) 自动保存： 会话结束 → memory-session-hook.sh → mp_global → 存储 飞书集成： FeishuSessionManager → 检测超时 → 触发 Hook → 保存 ``` ## 注意事项 - **存储规范**：必须使用 `memory-pipeline` 命令存取，不要直接操作文件 - **重要记忆**：使用 `--important` 标记永不忘记的关键信息 - **定期检查**：建议每周检查 crontab 是否正常执行 - **权限**：确保脚本有执行权限 `chmod +x memory-pipeline/*.sh` ## 版本历史 - **v1.0.0** - 初始版本 - 五级记忆层级完整实现 - 飞书会话自动保存集成 - 向量搜索与语义检索 - 自动降级与遗忘算法 --- *基于 OpenViking 思想设计，专为 OpenClaw Agents 打造*


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