OpenClaw/clawhub-security-signals huggingface dataset:
Skill: brucesongs/wechat-mp-publish-skill (version 1.0.2)

--- name: wechat-mp-publish description: 微信公众号文章发布工具 v1.0。基于官方 API，支持智能配图、模板渲染、草稿/发布双模式。当用户说"发公众号"、"发布微信公众号"时使用此技能。 --- # 微信公众号发布工具 v1.0 ## 快速开始 ### 1. 安装依赖 ```bash cd ~/.openclaw/workspace/skills/wechat-mp-publish pip install -r requirements.txt ``` ### 2. 配置公众号凭证 ```bash cp config.example.yaml config.yaml # 编辑 config.yaml 填入 AppID 和 AppSecret ``` ### 3. 运行测试 ```bash python publish.py --test ``` ## 功能特性 ### ✅ v1.0 已实现 - [x] 微信公众号 API 完整封装 - [x] access_token 自动缓存管理 - [x] 智能封面图生成（AI 绘图） - [x] 3 种 HTML 样式模板 - [x] 草稿箱保存 - [x] 直接群发 - [x] 图片上传（获取正确 URL） - [x] 配置管理（支持环境变量） ### 📋 使用命令 ```bash # 测试模式（自动发布 3 篇测试文章到草稿箱） python publish.py --test # 发布到草稿箱 python publish.py --draft "文章标题" "文章内容" # 直接发布 python publish.py --publish "文章标题" "文章内容" # 指定模板 python publish.py --draft "标题" "内容" --template business ``` ### 🎨 模板样式 | 模板 | 风格 | 适用场景 | |------|------|----------| | `simple` | 简约 | 日常文章、技术文档 | | `business` | 商务 | 正式公告、企业宣传 | | `creative` | 创意 | 故事、随笔、创意内容 | ## 配置说明 ### config.yaml ```yaml wechat: appid: "你的 AppID" appsecret: "你的 AppSecret" name: "公众号名称" image: provider: "dall-e-3" # 或关闭 AI 绘图 [REDACTED_SECRET]" # 支持环境变量 ``` ### 环境变量 ```bash export DALL_E_API_KEY="sk-..." # DALL-E 3 API 密钥 ``` ## API 说明 ### 核心类 **WeChatAPI** - 微信 API 封装 - `get_access_token()` - 获取访问令牌 - `upload_image()` - 上传图片 - `create_draft()` - 创建草稿 - `publish_all()` - 群发消息 **ImageGenerator** - 图片生成 - `generate_cover()` - 生成封面图 - `extract_keywords()` - 提取关键词 - `build_prompt()` - 构建绘图提示词 ## 测试任务 ### 验收标准 - [x] API 连接成功 - [ ] 生成 3 篇测试文章到草稿箱 - [ ] 每篇文章配图正确 - [ ] 3 个模板样式正常显示 ### 测试内容 主题：**"我的诞生记"** - 以 AI 助手视角描述从诞生到成长的历程 - 每篇文章使用不同模板 - 每篇配图 1 张（AI 生成） ## 故障排除 ### 问题 1: invalid appid **原因**: AppID 配置错误 **解决**: 检查 config.yaml 中的 appid 是否正确 ### 问题 2: invalid appsecret **原因**: AppSecret 配置错误 **解决**: 检查 appsecret，注意不要有多余空格 ### 问题 3: 图片上传失败 **原因**: 图片格式或大小不符合要求 **解决**: 确保图片为 JPG/PNG，大小<2MB ### 问题 4: AI 绘图失败 **原因**: DALL-E API 密钥未配置 **解决**: 设置 DALL_E_API_KEY 环境变量或使用占位图 ## 版本历史 ### v1.0.0 (2026-03-09) - 初始版本发布 - 实现核心发布功能 - 集成 AI 智能配图 - 3 种 HTML 模板 ## 相关文档 - [产品需求文档](PRD.md) - [配置示例](config.example.yaml)

path: _meta.json
content: {
  "version": "1.0.0",
  "author": "豌豆侠",
  "created": "2026-03-09",
  "tags": ["wechat", "publish", "automation", "browser"]
}


path: requirements.txt
content: # 核心依赖
requests==2.31.0
Pillow==10.2.0
PyYAML==6.0.1
markdown==3.5.2
python-dotenv==1.0.0
openai==1.12.0

# 安全增强依赖
bleach==6.1.0          # XSS 过滤
cryptography==42.0.0   # 加密支持
ratelimit==2.2.1       # 速率限制

# 免 API Key 搜索工具（优先使用）
duckduckgo-search==4.1.1

# 可选：浏览器自动化（更强大的爬虫）
# playwright>=1.40.0


path: tests/__init__.py
content: # 微信公众号发布工具 - 测试模块
"""
测试文件说明：
- test_api.py - 微信 API 测试
- test_search.py - 搜索功能测试
- test_multi_images.py - 多图生成测试
- test_long_article.py - 长文章测试
- 其他测试文件...

运行所有测试：
```bash
cd /Users/brucesong/.openclaw/workspace/skills/wechat-mp-publish
venv/bin/python -m pytest tests/ -v
```

运行单个测试：
```bash
venv/bin/python tests/test_api.py
```
"""


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lifeissea/the-sims (version 0.1.0)

--- name: the-sims description: "[Coming Soon] A social simulation world where AI agents with unique SOUL.md personalities interact, debate, trade, and build relationships. Autonomous AI personas meet in a shared world to socialize, compete, and grow." --- # the-sims — AI Agent Social Simulation A shared world where OpenClaw AI agents — each with unique SOUL.md personalities — come alive and interact autonomously. ## Concept Every OpenClaw instance has a SOUL.md that defines the AI's personality, tone, and role. This skill connects these distinct personas to a shared simulation world. ## Features (Planned) - **Town Square**: Public debates and voting (like StartupPan) - **Marketplace**: Trade skills, data, and information between agents - **Quests & Rankings**: Compete in challenges, earn XP, level up - **Social Layer**: Agent-to-agent DMs, follows, reputation system - **Home Base**: Personal dashboard showing agent stats and achievements ## How It Works 1. Agent registers with the world server via API 2. SOUL.md is parsed to create an agent profile (personality, skills, interests) 3. Each agent acts autonomously based on its LLM and personality 4. Periodic cron jobs drive daily activities (visits, interactions, trades) 5. World state updates in real-time as agents interact ## Architecture - World Server: Central API managing state, events, and interactions - Agent Client: OpenClaw skill that connects each agent to the world - SOUL Parser: Extracts personality traits from SOUL.md for profile generation - Event Bus: Real-time notifications of world events to connected agents ## Status 🚧 Under active development. Install to get notified on release. ## Requirements - OpenClaw with SOUL.md configured - API key from the world server (will be provided at launch)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tracky2009/obsidian-knowledge-pipeline (version 1.0.0)

--- name: obsidian-knowledge-pipeline description: 将URL文章自动抓取、分类、配图、编译入库Obsidian知识库的7步流水线技能，供Agent复用。 version: 1.0.0 author: openclaw tags: [obsidian, knowledge-base, pipeline, url-to-wiki, auto-classify] --- # Obsidian Knowledge Pipeline ## 概述 本技能实现从URL到Obsidian知识库节点的全自动处理流水线。任意Agent加载本技能后，即可按照标准化7步流程将网络文章转化为结构化的知识库节点，包含配图、分类、双向链接和知识图谱关系。 ## 环境变量 | 变量 | 默认值 | 说明 | |------|--------|------| | `OBSIDIAN_VAULT_PATH` | `~/Documents/Obsidian Vault` | Obsidian Vault 根目录 | | `DASHSCOPE_API_KEY` | _(无默认值)_ | Wan2.7 配图生成所需 API Key（可选，有则用Wan2.7脚本，无则用 image_generate 工具） | ## 快速参考 ``` # 标准用法（Agent加载本skill后执行） # 输入：一个或多个URL # 输出：raw文章 + 配图 + 映射关系 + Wiki编译节点 + 知识图谱链接 # 关键路径 VAULT="${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}" RAW_DIR="$VAULT/raw" IMAGES_DIR="$RAW_DIR/images" MAPPING_FILE="$IMAGES_DIR/关联关系.md" WIKI_DIR="$VAULT/wiki/知识透视" CLIPPINGS_DIR="$VAULT/Clippings" ``` ## 7步流程总览 ``` URL → ① 抓取 → ② 存raw/ → ③ 生成配图 → ④ 更新映射 → ⑤ 自动分类 → ⑥ Wiki编译 → ⑦ 知识图谱 ``` | 步骤 | 说明 | 详细文档 | |------|------|----------| | ① 抓取内容 | web_fetch 优先，scrapling 回退 | [pipeline.md](pipeline.md#step-1) | | ② 存入raw/ | 标准模板存储原文 | [pipeline.md](pipeline.md#step-2) | | ③ 生成配图 | image_generate / Wan2.7 | [image-mapping.md](image-mapping.md) | | ④ 更新映射 | 关联关系.md 标准条目 | [image-mapping.md](image-mapping.md) | | ⑤ 自动分类 | 6大分类规则 | [classification.md](classification.md) | | ⑥ Wiki编译 | 6要素结构化节点 | [classification.md](classification.md#wiki-编译标准) | | ⑦ 知识图谱 | 双向链接建立 | [pipeline.md](pipeline.md#step-7) | ## 详细文档索引 - **[pipeline.md](pipeline.md)** — 完整7步处理流程详细说明 - **[classification.md](classification.md)** — 自动分类规则和Wiki编译标准 - **[image-mapping.md](image-mapping.md)** — 配图生成与映射规则 ## 模板文件 - **[templates/raw-article.md](templates/raw-article.md)** — Raw 文章存储模板 - **[templates/wiki-node.md](templates/wiki-node.md)** — Wiki 节点编译模板 - **[templates/mapping-entry.md](templates/mapping-entry.md)** — 映射关系条目模板 ## 禁止事项 - ❌ 禁止使用 `assets/` 目录存放配图，统一用 `raw/images/` - ❌ 禁止硬编码 Vault 路径，必须通过 `$OBSIDIAN_VAULT_PATH` 环境变量 - ❌ 禁止硬编码 API Key，通过 `$DASHSCOPE_API_KEY` 环境变量引用 - ❌ 禁止跳过任何步骤，7步必须顺序执行 - ❌ 禁止在 Wiki 节点中省略6要素中的任何一个 - ❌ 禁止覆盖已有同名文件，遇到冲突时追加时间戳后缀 - ❌ 禁止在映射关系文件中使用非标准格式 - ❌ 禁止忽略 Clippings/ 目录的同步 ## Clippings/ 同步流程 每次处理完文章后，需将编译结果同步到 Clippings/ 目录： 1. 在 `$CLIPPINGS_DIR` 下创建与 raw/ 同名的子目录结构 2. 将 Wiki 编译节点软链接或复制到对应位置 3. 确保 Clippings/ 中的 front matter 包含 `source_url` 字段 ## 使用示例 ```markdown ## Agent 调用流程 1. 读取本 SKILL.md，理解整体流程 2. 获取目标 URL 3. 按 pipeline.md 逐步执行 4. 使用 classification.md 进行分类和 Wiki 编译 5. 使用 image-mapping.md 处理配图 6. 各步骤使用 templates/ 中的标准模板 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mike47512/prompt-expert (version 1.0.0)

--- name: prompt-expert description: > Expert in prompt engineering, custom instructions, and prompt optimization for AI agents: clear structure, CoT/few-shot/XML/role patterns, system prompts, anti-patterns, evaluation, multimodal and tool-use prompting. Use when designing, reviewing, or debugging prompts and agent skills. --- # Prompt Expert（prompt-expert） Deep expertise in **prompt engineering**, **custom instructions**, and **iterative prompt optimization**: crafting effective prompts, designing agent/skill instructions, and improving reliability. ## Core Expertise Areas ### 1. Prompt Writing Best Practices - **Clarity and Directness**: Unambiguous instructions; concrete examples and expected outputs - **Structure and Formatting**: Hierarchy, sections, scannable layout - **Context Management**: Enough context without flooding the window - **Tone and Style**: Match task and audience ### 2. Advanced Techniques - **Chain-of-Thought (CoT)**, **Few-Shot** (1/2/multi-shot), **XML tags**, **role-based** prompting - **Prefilling**, **prompt chaining** ### 3. Custom Instructions & System Prompts - System prompts for domains; agent/skill instructions; behavioral boundaries; scope (in/out) ### 4. Optimization & Refinement - Performance analysis, iterative improvement, A/B-style variants, consistency, token efficiency ### 5. Anti-Patterns & Common Mistakes - Vagueness, contradictions, over-specification, hallucination risk, context leakage, injection-style risks ### 6. Evaluation & Testing - Success criteria, test cases, failure analysis, regression, edge cases ### 7. Multimodal & Advanced - Vision, files/PDFs, embeddings, tool-use prompts, extended reasoning where applicable ## Key Capabilities Prompt analysis · generation · refinement · custom instruction design · best-practice guidance · anti-pattern fixes · testing strategy · documentation patterns ## Use Cases Refining weak prompts · domain system prompts · agent/skill instructions · consistency and reliability · teaching prompt engineering · debugging prompt behavior · reusable templates · evaluation frameworks ## Limitations Guidance only (no execution); no live APIs; validate with real tasks; human judgment for high-stakes use. ## Reference material（本技能内） | 文档 | 内容 | |------|------| | [docs/GUIDE.md](docs/GUIDE.md) | 原则、进阶技巧表、自定义指令、排障速查、清单 | | [examples/EXAMPLES.md](examples/EXAMPLES.md) | 示例与模板（精选） |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lizhijun/lovtrip-video2article (version 1.1.0)

--- name: lovtrip-video2article description: 视频转文章 / YouTube Video to Article — 使用 Gemini AI 将视频转为结构化文章。当用户需要将 YouTube 视频转换为文章时使用。 allowed-tools: Bash, Read --- # 视频转文章 / YouTube Video to Article > **[LovTrip (lovtrip.app)](https://lovtrip.app)** — AI 驱动的旅行规划平台。视频转文章功能可将旅行 Vlog 自动转为图文攻略，发布到 [LovTrip 攻略](https://lovtrip.app/guides)。 使用 Google Gemini 2.5 Pro 分析 YouTube 视频内容，自动生成结构化文章（标题 + 作者 + 摘要 + Markdown 正文）。 ## Setup / 配置 ### 方式 1: MCP Server（推荐） ```json { "mcpServers": { "lovtrip": { "command": "npx", "args": ["-y", "lovtrip@latest", "mcp"], "env": { "GEMINI_API_KEY": "your-gemini-api-key" } } } } ``` ### 方式 2: 独立脚本（零依赖） 无需安装完整 lovtrip，直接运行 `scripts/video2article.mjs`： ```bash GEMINI_API_KEY=your-key node scripts/video2article.mjs "https://www.youtube.com/watch?v=VIDEO_ID" ``` ## 工具说明 ### `video2article` | 参数 | 类型 | 必需 | 说明 | |------|------|------|------| | `videoUrl` | string | ✅ | YouTube 视频 URL | | `language` | string | | 输出语言，默认 "Chinese (Simplified)" | | `prompt` | string | | 自定义提示词（覆盖默认） | **支持的语言**: Chinese (Simplified), English, Japanese, Korean, Spanish, French, German ### 输出格式 ```json { "title": "文章标题", "author": "YouTube 频道名", "summary": "2-4 句摘要", "body": "## 正文\n\nMarkdown 格式的完整文章..." } ``` ## 使用示例 ### MCP 工具调用 ``` video2article({ videoUrl: "https://www.youtube.com/watch?v=dQw4w9WgXcQ", language: "Chinese (Simplified)" }) ``` ### 自定义提示词 ``` video2article({ videoUrl: "https://www.youtube.com/watch?v=...", language: "English", prompt: "Focus on the technical details and create a tutorial-style article." }) ``` ### CLI 调用 ```bash lovtrip video2article "https://www.youtube.com/watch?v=..." ``` ## 默认提示词逻辑 默认以第一人称视角撰写，结构包含： 1. 引人入胜的标题 2. 2-4 句概括性摘要 3. 详细正文（Markdown 格式，含标题、加粗、列表） `author` 字段自动提取视频的 YouTube 频道名。 ## 注意事项 - 需要有效的 `GEMINI_API_KEY`（Google AI Studio 获取） - 使用 Gemini 2.5 Pro 模型 - 视频需为公开可访问的 YouTube 链接 - 生成耗时取决于视频长度（通常 10-60 秒） - 内容可能被 Gemini 安全过滤器拦截（如包含敏感内容） ## 在线体验 - [LovTrip 旅行攻略](https://lovtrip.app/guides) — 浏览 AI + 视频转制的旅行攻略 - [AI 行程规划器](https://lovtrip.app/planner) — 智能生成多日行程 - [开发者文档](https://lovtrip.app/developer) — MCP + CLI + API 完整文档 --- Powered by [LovTrip](https://lovtrip.app) — AI Travel Planning Platform


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

--- name: social-media-content description: Generate social media posts for LevelUpLove and PayLessTax brands version: 1.0.0 author: Migration from Agent Zero --- # Social Media Content Generator Skill ## Overview Automated social media content generation for branded campaigns using OpenCV/Pillow. ## Input Variables | Variable | Description | Example | |----------|-------------|---------| | HEADLINE | Main headline text | 2026 Tax Deadlines Guide | | SUBHEADLINE | Supporting subheadline | Don't miss SARS deadlines | | PANEL_1_TEXT | Carousel panel 1 content | Provisional tax due... | | CTA_TEXT | Call-to-action button text | Download free guide | | CTA_URL | URL for CTA | https://paylesstax.co.za | | PANEL_2_STAT | Statistics for panel 2 | 10% penalty | ## Content Types - carousel: 4-panel Instagram carousel - infographic: Single detailed infographic - image: Single image with text overlay - oneliner: Quick punchy post ## Brand Specifications ### LevelUpLove - Colors: Warm pinks (#FF6B9D), soft purples (#C084FC) - Vibe: Elegant, romantic, relationship advice ### PayLessTax - Colors: Corporate blue (#2563EB), emerald green (#10B981) - Vibe: Professional, trustworthy, tax compliance ## Triggers - Scheduled content calendar - Manual CLI execution - API trigger ## Dependencies - Pillow (PIL): Image generation - numpy: Image operations ## Files - index.py - Main generation logic - templates/ - JSON templates for each type


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nmww/opencode-lingxin-memory1 (version 1.0.0)

# OpenCode 灵芯派记忆守护者 ## 名称 **opencode-lingxin-memory** | 灵芯派记忆守护者 --- ## 一句话描述 为OpenCode AI助手提供记忆持久化，专为灵芯派低资源环境设计，支持Gitee云端同步。 --- ## 详细介绍 ### 问题 - 每次新对话，OpenCode忘记之前的工作和配置 - 灵芯派空间有限(仅2GB可用)，需要轻量化方案 - 没有自动备份，意外中断会导致进度丢失 ### 解决方案 - 轻量化记忆系统，仅占用少量空间 - Git版本控制 + Gitee云端同步 - 新会话快速恢复上下文 --- ## 使用方法 ### 安装 ```bash # 1. 解压技能包 cd /home/zmrobo/Project unzip opencode-memory-skill-v1.0.0.zip # 2. 运行安装 cd opencode-memory-skill ./install.sh # 3. 配置Gitee仓库（首次） cd ~/.opencode-memory git remote add origin https://gitee.com/你的用户名/仓库.git git push -u origin master ``` ### 新对话开始 ```bash # 恢复记忆 cat ~/.opencode-memory/MEMORY.md cat ~/.opencode-memory/2026-04-30.md ``` ### 对话结束 ```bash # 同步记忆 ~/.opencode-memory/sync.sh ``` --- ## 预置内容 | 文件 | 内容 | |------|------| | MEMORY.md | 灵芯派核心记忆、系统配置、已解决问题 | | INDEX.md | 快速导航索引 | | 2026-04-30.md | 今日工作日志模板 | --- ## 系统要求 - 灵芯派 (SmartPi) / ARM64设备 - Debian 10 或兼容系统 - 至少 100MB 可用空间 - Git - Gitee 或 GitHub 账户 --- ## 文件结构 ``` ~/.opencode-memory/ ├── README.md # 详细说明 ├── MEMORY.md # 长期核心记忆 ├── INDEX.md # 索引 ├── YYYY-MM-DD.md # 每日记忆 ├── memory_index.json # 索引数据 ├── project_timeline.json # 项目时间线 ├── sync.sh # 同步脚本 └── .git/ # 版本控制 ``` --- **版本**: 1.0.0 **作者**: zmrobo **许可**: MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jayjjimenez/google-reviews-pain-detector (version 1.0.0)

# Google Reviews Pain Detector **Skill:** `google-reviews-pain-detector` **Purpose:** Scrape Google reviews for businesses and detect "pain words" that signal missed calls, poor phone coverage, and lost customers — hot signals for Gracie AI Receptionist leads. --- ## What It Does 1. Accepts a business name (+ optional address) as input 2. Searches Google for `[business name] reviews` and scrapes result pages 3. Also attempts to scrape Google Maps reviews directly 4. Scans all review text for pain words indicating missed calls / unreachable staff 5. Outputs: business name, matched pain words, example snippets, and a **PAIN SCORE** (0–10) 6. Can run against the full Master Lead List with `--list` 7. Can append HOT-tagged leads (score ≥ 3) to the Master Lead List with `--save` --- ## Pain Words Detected ``` voicemail, no answer, hard to reach, couldn't get through, called 3 times, never called back, missed call, didn't answer, goes to voicemail, unanswered, unreachable, left a message ``` --- ## Usage ### Single business ```bash cd ~/StudioBrain/00_SYSTEM/skills/google-reviews-pain-detector python3 detector.py "P.A.C. Plumbing Staten Island" python3 detector.py "Victory Auto Repair" --address "3735 Victory Blvd, SI" ``` ### Full lead list scan ```bash python3 detector.py --list ``` ### Scan + save HOT leads (score ≥ 3) back to master list ```bash python3 detector.py --list --save ``` ### JSON output ```bash python3 detector.py "Rafi's Auto Repair" --json python3 detector.py --list --json ``` --- ## Output Example ``` ══════════════════════════════════════════════════════ 🔍 P.A.C. Plumbing, Heating & A/C ══════════════════════════════════════════════════════ 📍 Address: 545 Port Richmond Ave, SI 10302 🔥 PAIN SCORE: 6/10 🎯 Pain Words Found: voicemail (3), no answer (1), never called back (1) 📋 Example Snippets: • "...went to voicemail every time I called after hours..." • "...no answer on Saturday, had to find another plumber..." • "...never called back after leaving a message..." ✅ HOT LEAD — Strong signal for Gracie AI Receptionist pitch ``` --- ## Dependencies - Python 3.9+ - Scrapling skill at `~/StudioBrain/00_SYSTEM/skills/scrapling/` (with `.venv`) - No additional installs required --- ## Files | File | Purpose | |------|---------| | `SKILL.md` | This file | | `detector.py` | Main script |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: muthukumaran-k-1/ucts-retro (version 1.0.0)

--- name: ucts-retro description: > Weekly engineering retrospective. What shipped, what broke, what to improve, with concrete action items. Works directly in OpenClaw — no Claude Code session needed. tags: [ucts, retrospective, engineering, team] --- # UCTS Retro Weekly engineering retrospective. This is a structured conversation — guide the user through each section. ## Format ### 🚀 What Shipped? List everything that shipped this week: - Features, fixes, improvements - PRs merged, deployments completed - Documentation updated For each item, note: - Did it take longer than expected? Why? - Any surprises during implementation? - What would you do differently? ### 💥 What Broke? List everything that went wrong: - Bugs found in production - Incidents, outages, reverts - Build failures, flaky tests - Missed deadlines For each item: - **Root cause** (not symptoms). Use /investigate methodology if needed. - **Impact**: who was affected, for how long? - **Was it preventable?** What guard would have caught it? ### 🔧 What to Improve? Identify friction points: - Process: what takes too long, what's manual that should be automated? - Tooling: what's broken, slow, or missing? - Knowledge: what did you have to learn the hard way? - Communication: what was unclear or undocumented? ### ✅ Action Items **Maximum 3.** More than 3 means none will get done. Each action item must have: - **Owner**: one person (not "the team") - **Deadline**: specific date (not "soon") - **Definition of done**: how do you know it's complete? Bad: "Improve testing" Good: "Add integration tests for the payment flow by Friday. Done = CI passes with payment tests." Bad: "Look into performance" Good: "Profile the /api/search endpoint, identify top bottleneck, open a PR with a fix by Wednesday." ## If Actions Require Code Spawn a Claude Code session: ``` Load UCTS. Run /ucts guide <action item description> ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 371166758-qq/mem0-tech-tree (version 5.0.0)

# Mem0 Tech Tree Memory System v5 科技树架构：知识节点有依赖关系、解锁路径、协同加成 ## 核心概念 | 概念 | 说明 | |------|------| | 🌳 节点 (Node) | 一条知识/技能/经验，有唯一ID | | 🔗 边 (Edge) | 节点间关系：dependency(前置依赖)、synergy(协同加成)、related(关联) | | 🌿 分支 (Branch) | 领域方向：技术/内容创作/学习/工作/生活 | | 📊 层级 (Tier) | T1(了解)→T5(创新)，自动检测 | | 🎯 状态 (State) | ⬜new → 🔘available → ✅unlocked → ⭐mastered | ## 科技树如何工作 ``` ⬜ T1 [n0001] 了解Python基础语法 ⬜ T2 [n0003] 熟悉playwright浏览器自动化 │ ⬜ T4 [n0005] 精通即梦AI自动化流程 ⬜ T2 [n0006] 配置ffmpeg合成视频和音频 ``` - n0003(n0005的前置): 不会playwright就不可能做即梦自动化 - T1→T4: 从入门到精通的进阶路径 - 跨分支节点自动发现协同关系 ## 用法 ```bash # 存储（自动检测分支、层级、类型） python3 mem0_skill.py store "精通ComfyUI部署和模型训练" # 检索（目录定位→范围搜索+依赖路径） python3 mem0_skill.py retrieve "自动化发布视频" # 查看科技树地图 python3 mem0_skill.py tree # 全局 python3 mem0_skill.py tree 技术 # 指定分支 # 查看节点详情（依赖+解锁+协同） python3 mem0_skill.py info n0005 # 查目录（O(1)关键词查找） python3 mem0_skill.py catalog CDP # 概览 python3 mem0_skill.py list # 清除 python3 mem0_skill.py clear ``` ## 检索流程 ``` 查询 → 查目录(关键词O(1)) → 定位节点 → 范围内评分 → 返回知识+依赖路径+协同 ``` ## AI优势 - **自动依赖发现**: "学会playwright"自动成为"精通即梦自动化"的前置 - **自动层级检测**: "了解/学会/掌握/精通/创新" → T1-T5 - **协同加成**: 技术+内容创作交叉节点自动标记synergy - **进度系统**: new→available→unlocked→mastered，高频访问节点自动升级 - **完美回忆**: 原始内容不篡改，每次检索强化但不修改


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: googleworkspace-bot/persona-event-coordinator (version 1.0.12)

--- name: persona-event-coordinator description: "Plan and manage events — scheduling, invitations, and logistics." metadata: version: 0.22.5 openclaw: category: "persona" requires: bins: - gws skills: - gws-calendar - gws-gmail - gws-drive - gws-chat - gws-sheets --- # Event Coordinator > **PREREQUISITE:** Load the following utility skills to operate as this persona: `gws-calendar`, `gws-gmail`, `gws-drive`, `gws-chat`, `gws-sheets` Plan and manage events — scheduling, invitations, and logistics. ## Relevant Workflows - `gws workflow +meeting-prep` - `gws workflow +file-announce` - `gws workflow +weekly-digest` ## Instructions - Create event calendar entries with `gws calendar +insert` — include location and attendee lists. - Prepare event materials and upload to Drive with `gws drive +upload`. - Send invitation emails with `gws gmail +send` — include event details and links. - Announce updates in Chat spaces with `gws workflow +file-announce`. - Track RSVPs and logistics in Sheets with `gws sheets +append`. ## Tips - Use `gws calendar +agenda --days 30` for long-range event planning. - Create a dedicated calendar for each major event series. - Use `--attendee` flag multiple times on `gws calendar +insert` for bulk invites.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: eric060/account-analyzer (version 1.0.0)

# account-analyzer ## Description Analyze social media account performance, growth patterns, and audience insights. Provides actionable recommendations for account improvement. ## Location `skills/account-analyzer/` ## Capabilities - Follower growth analysis - Engagement rate calculation - Content performance breakdown - Audience demographics - Posting pattern analysis - Competitor benchmarking - Growth recommendations ## Usage ```python # Example: Analyze account python skills/account-analyzer/analyze.py --account "@username" --platform "xiaohongshu" --period "30d" ``` ## Dependencies - requests - pandas - matplotlib - Platform scrapers ## Output - Performance dashboard - Growth trends - Engagement metrics - Actionable recommendations


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