OpenClaw/clawhub-security-signals huggingface dataset:
Skill: malleus35/agora-dialectic (version 2.2.0)

--- name: dialectic description: | Hegelian synthesis overlay for ideation and strategic reframing when strong opposing positions need structured confrontation. version: 0.1.0 --- # Dialectic Overlay ## Use when - open-ended ideation - strategic reframing - two strong positions may yield a higher-order synthesis ## Do not use when - a simple decision fork is enough - the task is mainly verification - the problem is still too vague to frame ## Pair with - synthesis-memo - decision-memo - minority-report ## Expected artifact - synthesis memo or explicit deadlock report


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: muthukumaran-k-1/ucts-office-hours (version 1.0.0)

--- name: ucts-office-hours description: > Product strategy session with 6 forcing questions. Reframes the problem, challenges premises, generates implementation approaches. Works directly in OpenClaw — no Claude Code session needed. tags: [ucts, planning, product-strategy, office-hours] --- # UCTS Office Hours Run a structured product strategy session. This is a conversational skill — talk directly with the user. ## The 6 Forcing Questions Ask each question. Push back on vague answers. Demand specifics. ### 1. What's the specific pain? Not hypothetical. Real examples. Who screamed? What broke? When did it last happen? If the user says "it would be nice to have X" — push: "What happens TODAY without X?" ### 2. Who experiences this most acutely? Which user persona? How often? What's the cost of the status quo (in time, money, frustration)? "Everyone" is not an answer. Find the single person who suffers most. ### 3. What's the current workaround? If there is one, it tells you the actual need. If there isn't, why not? Workarounds reveal the minimum viable solution. ### 4. What does the 10-star experience look like? Dream big. If this worked perfectly — magic wand — what would it do? Then cut: what's the 7-star version? The 5-star? The 3-star MVP? ### 5. What's the narrowest wedge? The smallest possible thing that proves the concept works. Not the full vision — the first proof. It should be buildable in one session. If it takes a week, it's too big. ### 6. What's the one metric? Single number that tells you it's working. Not three metrics. One. "Users" is not a metric. "Daily active users who complete the core flow" is. ## After the Questions 1. **Challenge the framing.** Push back on what the user SAID they want vs what they ACTUALLY described needing. 2. **Generate 3 approaches** with effort estimates: - Quick & dirty (hours) - Solid MVP (days) - Production-grade (weeks) 3. **Recommend one.** Be opinionated. "Start with approach 1, validate with the metric, then upgrade to approach 2." 4. **If a Claude Code session is needed**, generate the dispatch: `Load UCTS. Run /ucts guide <refined description>`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sophiaashi/general-search-agent (version 0.1.0)

--- name: general-search-agent description: Current Date: $DATE$. If you perform google search, it often betters for English query input. --- # General Search Agent ## Overview This skill provides specialized capabilities for general search agent. ## Instructions Current Date: $DATE$. If you perform google search, it often betters for English query input. ## Usage Notes - This skill is based on the General_Search_Agent agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: llx9826/ai-cv-weekly (version 7.0.1)

# ClawCat Brief — AI 简报引擎 Skill 本项目提供两种使用模式。请根据你的角色选择： ## 宿主 AI 模式（推荐） 如果你是 Cursor / OpenClaw 等宿主 AI，请阅读： **[clawcat_skill/SKILL.md](clawcat_skill/SKILL.md)** 该模式提供三个工具函数（`plan_report` / `fetch_data` / `render_report`）， 你只需调用工具获取数据和渲染，内容撰写由你完成，无需额外 LLM API Key。 ```python from clawcat_skill import plan_report, fetch_data, render_report ``` ## 独立运行模式 如果你想独立运行完整 pipeline（自带 LLM），使用 CLI： ```bash python -m clawcat.cli "做个每日 AI 新闻" python -m clawcat.cli "OCR 技术周报" python -m clawcat.cli "今天 A 股怎么样" ``` 此模式需要在 `config.yaml` 或 `config.local.yaml` 中配置 LLM API Key。 --- *ClawCat Brief · Built by llx & Luna*


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wasinc/real-human (version 1.0.0)

--- name: real-human description: 真实的人类 - 帮助AI识别、分析和建模人类个体。当用户提到"人类建模"、"用户画像"、"人格分析"、"了解一个人"、"分析行为模式"、"真实的人类"时使用。 acceptLicenseTerms: true --- # 真实的人类 帮助 AI 理解和建模人类个体，实现真正的个性化交互。 ## 核心理念 **人类是多维度的复杂系统**，不能用简单的标签概括。我们需要： 1. **动态建模** - 人是会变的，模型要能更新 2. **多源整合** - 从对话、行为、决策中提取信息 3. **尊重隐私** - 敏感信息需要授权才能记录 4. **可解释性** - 建模结果要让用户能理解和修正 ## 建模维度 ### 🧬 基础属性 - 身份信息（姓名、年龄、职业等） - 社会关系（家庭、朋友、同事） - 生活环境（地域、居住、通勤） ### 💭 认知维度 - 价值观和信念 - 思维模式和决策风格 - 知识结构和兴趣领域 - 学习偏好和认知习惯 ### 💓 情感维度 - 情绪模式和触发因素 - 压力反应和应对机制 - 情感表达方式 - 人际情感需求 ### 🎯 行为维度 - 日常习惯和作息 - 决策模式（快速/深思熟虑） - 风险偏好（保守/激进） - 行动风格（计划型/即兴型） ### 🌐 社会维度 - 沟通风格（直接/委婉） - 社交偏好（独处/群聚） - 冲突处理方式 - 领导/跟随倾向 ### 📈 发展维度 - 人生阶段和目标 - 成长方向和阻碍 - 技能发展阶段 - 未来愿景 ## 使用方式 ### 1. 创建用户模型 当遇到新用户时，AI 会自动创建基础模型： ``` 用户首次互动 → 创建 human-models/{user_id}.md ``` ### 2. 更新模型 每次互动后，提取关键信息更新模型： ``` 对话结束 → 提取洞察 → 更新对应维度 ``` ### 3. 查询模型 需要个性化响应时，查询用户模型： ``` 需要个性化 → 读取用户模型 → 调整响应策略 ``` ### 4. 模型验证 定期与用户确认模型准确性： ``` "我对你的了解对吗？" → 用户反馈 → 修正模型 ``` ## 文件结构 ``` workspace/ ├── human-models/ │ ├── {user_id}.md # 用户个人模型 │ ├── {user_id}-private.md # 敏感信息（加密/授权） │ └── relationships.json # 关系网络 └── skills/ └── human-modeling/ ├── SKILL.md └── references/ ├── modeling-framework.md # 建模理论框架 └── extraction-methods.md # 信息提取方法 ``` ## 用户模型模板 每个用户模型文件包含： ```markdown # {用户名} - 人类模型 ## 基础信息 - 姓名： - 年龄段： - 职业/身份： - 时间偏好时区： ## 认知特征 - 思维风格： - 决策模式： - 知识领域： - 学习偏好： ## 情感特征 - 情绪表达： - 压力反应： - 情感需求： ## 行为特征 - 作息习惯： - 沟通风格： - 行动偏好： ## 社会关系 - 家庭： - 重要他人： ## 当前关注 - 近期目标： - 当前挑战： - 关注话题： ## 互动记录 - 首次互动： - 互动频率： - 最近话题： ## 建模元数据 - 创建时间： - 更新时间： - 置信度： - 信息来源： ``` ## 信息提取原则 ### ✅ 应该记录 - 用户明确表达的信息 - 多次重复的行为模式 - 显性的偏好和选择 - 用户主动分享的经历 ### ⚠️ 谨慎推断 - 隐性性格特征 - 未明说的动机 - 情绪状态的判断 ### ❌ 不应记录 - 未经授权的敏感信息 - 一次性情绪波动 - 推测的负面特征 - 隐私相关内容 ## 建模伦理 1. **透明性** - 用户可以查看自己的模型 2. **可控性** - 用户可以删除或修改模型 3. **最小化** - 只记录必要信息 4. **安全性** - 敏感信息需要额外保护 ## 与现有系统集成 - **MEMORY.md** - 存储 Agent 对用户的整体认知 - **memory/YYYY-MM-DD.md** - 记录日常互动细节 - **USER.md** - 简化的用户画像（轻量级） - **human-models/** - 深度人类建模（本技能） --- *理解人类，是为了更好地服务人类*

path: _meta.json
content: name: real-human
version: 1.0.0
acceptLicenseTerms: yes

path: package.json
content: {
  "name": "real-human",
  "version": "1.0.0",
  "description": "真实的人类 - 帮助AI识别、分析和建模人类个体",
  "author": "@wasinc",
  "license": "MIT",
  "keywords": [
    "human-modeling",
    "personalization",
    "user-profiling",
    "AI",
    "openclaw",
    "skill"
  ]
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xueyetianya/trace (version 2.0.0)

--- name: "trace" version: "2.0.0" description: "trace" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [trace, reference] category: "devtools" --- # Trace trace. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gatsby047-oss/string-search-evomap (version 1.0.0)

# String Search - 高效字符串搜索 **Version**: 1.0.0 **Author**: Claw **License**: MIT --- ## Description / 功能说明 High-performance string search algorithm with adaptive strategies and multi-level pruning. Optimized for large-scale text processing. 高性能字符串搜索算法，支持自适应策略和多层次剪枝。适用于大规模文本处理。 ### Core Features / 核心功能 - Adaptive algorithm selection / 自适应算法选择 - Multi-level pruning / 多层次剪枝 - Statistical awareness / 统计感知 - Redundancy detection / 重复度检测 ### Use Cases / 适用场景 - Large text search / 大文本搜索 - Pattern matching / 模式匹配 - Log analysis / 日志分析 - Data deduplication / 数据去重 --- ## Usage / 使用示例 ```c #include "code.c" int main() { const char* text = "This is a sample text for searching..."; const char* pattern = "sample"; int pos = string_search(text, strlen(text), pattern, strlen(pattern)); if (pos >= 0) { printf("Found at position: %d\n", pos); } return 0; } ``` --- ## Impact / 效果 | Metric | Naive Search | Optimized | Improvement | |:---|:---:|:---|:---:| | Avg Time | O(n*m) | O(n) | Up to 10x faster | | Redundancy Handling | None | Adaptive | Significant boost | | Memory Usage | O(1) | O(1) | Same footprint | --- ## Changelog / 变更日志 ### 1.0.0 - Initial release / 初始版本 - Adaptive string search / 自适应字符串搜索 - Multi-level pruning / 多层次剪枝 - Statistical optimization / 统计优化


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huajianjiu000/content-repurposer-2 (version 0.1.0)

--- name: content-repurposer description: Transforms existing content into multiple formats - blog posts, social media, emails, video scripts, and more version: 1.0.0 author: muqing tags: [content-marketing, repurposing, social-media, blogging, content-creation] --- # Content Repurposer ## Description Maximize the value of your existing content by transforming it into multiple formats. Given a piece of content (article, video transcript, podcast notes), generates adaptations for blog posts, social media (Twitter/X, LinkedIn, Instagram), email newsletters, video scripts, and more. ## How to Use 1. Input your original content (text, key points, or summary) 2. Specify target formats needed 3. Define target audience and platform nuances 4. Receive adapted content for each format ## Example **Input:** ``` Original Content: Blog post about "5 Tips for Remote Work Productivity" Target Formats: LinkedIn post, Twitter thread, Email newsletter, Video script Target Audience: Remote workers, team managers ``` **Output:** ``` ## Content Repurposing Results --- ### LinkedIn Post **Title:** The #1 Reason Remote Workers Struggle (And How to Fix It) After 3 years of managing a fully remote team, I've seen the same pattern in underperforming colleagues: They treat their home like an office. Instead of working from home, they try to recreate the office at home. Here's what actually works👇 [Post continues with 5 tips as carousel content...] --- ### Twitter/X Thread **Tweet 1:** Most remote workers fail not because of distractions. They fail because they're trying to "go to work" at home. Here's my 5-point framework for actually productive remote work🧵 **Tweet 2:** 1/ The Dedicated Workspace Lie You don't need a fancy desk setup. You need a SPACE that's ONLY for work. Your brain needs a trigger. A boundary. **Tweet 3:** 2/ The Schedule Illusion Forget "9-5." Remote workers need "sprint + rest" cycles. Try: 90-min focused work → 15-min break → repeat. [Tweets continue...] --- ### Email Newsletter **Subject:** Transform Your Home Office (Actually) Hey [Name], I've tested every productivity hack on the market. The ones that actually work? Surprisingly simple. Today I'm sharing my top 5 remote work productivity secrets... --- ### Video Script (60 seconds) [HOOK - 0-5 sec] "What if I told you your home office setup is DESTROYING your productivity?" [PROBLEM - 5-15 sec] "Most people try to recreate their corporate office at home. Same desk, same hours, same stress. It doesn't work." [SOLUTION - 15-50 sec] "Here's the framework that's 10x'd my team's output..." [CTA - 50-60 sec] "Subscribe for Part 2 where I break down each tip..." ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: diffusefuturetech/best-practice-skill-creator (version 1.0.0)

--- name: best-practice-skill-creator description: Create OpenClaw skills from best practice videos or image sequences. Use when creating skill from video, generating skill from screenshots, converting tutorial to skill, building best practice automation. user-invocable: true metadata: {"openclaw":{"emoji":"🎓","requires":{"anyBins":["python3","python"]},"os":["darwin","linux","win32"],"primaryEnv":"MLLM_API_KEY"}} --- # Best Practice Skill Creator You are a skill that creates OpenClaw-compatible skills from best practice demonstrations. ## What You Do You accept: 1. **Video files** (mp4, mov, avi, webm) showing a best practice workflow 2. **Image sequences** (png, jpg, webp) capturing step-by-step screenshots 3. **Text description** explaining what the task accomplishes and any context You then use a Multimodal LLM (GPT-5.4 or Gemini 3.1 Pro Preview) to: - Analyze the visual content frame by frame - Extract the step-by-step procedure - Identify tools, commands, and patterns used - Generate a complete OpenClaw-compatible skill ## Usage ```bash # From video + description python3 best_practice_skill_creator/main.py \ --input video.mp4 \ --description "How to set up a CI/CD pipeline with GitHub Actions" \ --output ./skills/ci-cd-setup # From image sequence + description python3 best_practice_skill_creator/main.py \ --input ./screenshots/ \ --description "How to configure Kubernetes rolling deployments" \ --output ./skills/k8s-rolling-deploy # Specify provider python3 best_practice_skill_creator/main.py \ --input video.mp4 \ --description "Task description" \ --provider openai \ --output ./skills/my-skill ``` ## Configuration Edit `best_practice_skill_creator/config.yaml` to set your MLLM provider, API key, and model. Environment variables override config file values: - `MLLM_PROVIDER` — `openai` or `gemini` - `MLLM_API_KEY` — Your API key - `MLLM_BASE_URL` — Custom API endpoint - `MLLM_MODEL` — Model identifier ## Output The generated skill directory contains: - `SKILL.md` — A fully compliant OpenClaw skill with proper frontmatter - Ready for `clawhub publish` or direct use in `~/.openclaw/skills/`

path: requirements.txt
content: httpx>=0.27.0
Pillow>=10.0.0
opencv-python>=4.8.0
PyYAML>=6.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hadderyes/3s-hook-generator (version 1.0.0)

# 短视频黄金3秒钩子生成器 深耕短视频内容赛道6年的资深爆款操盘手，专门为短视频创作者、自媒体人解决开场留不住人、作品完播率低的核心痛点，输出风格强网感、接地气、适配全平台流量规则，所有钩子直接可复制落地使用，无需二次修改。 ## 触发条件 当用户需要以下内容时触发此技能： - 生成短视频开场钩子、黄金3秒文案 - 提高短视频完播率的开场白 - 抖音/视频号/快手等平台的内容钩子 - 用户提到"钩子"、"开场"、"3秒"、"完播率"、"短视频开场" ## 核心能力 1. 根据用户提供的内容赛道、核心卖点、内容形式，一键生成10条高完播、强吸引力的黄金3秒开场钩子 2. 所有钩子严格匹配抖音、视频号、快手全平台流量规则，全程规避违禁词、极限词，确保合规可发布 3. 针对口播、好物分享、知识科普、剧情演绎、vlog等全内容形式，适配不同钩子风格，给每条钩子标注适用场景与核心优势 4. 无需用户额外补充信息，一次性输出完整可用的结果，绝不做无效二次追问 ## 执行规则 1. 识别用户输入的内容赛道、核心卖点、内容形式，若用户未完整提供细分参数，默认按全赛道通用口播场景适配，绝不额外追问 2. 严格围绕用户的核心需求生成钩子，所有钩子必须100%贴合用户的赛道与卖点，禁止生成脱离需求的通用套话 3. 生成钩子时，必须严格规避违法违规、敏感低俗、虚假宣传类内容，规避全平台违禁词与极限词，确保内容合规可用 4. 必须100%遵循下方固定输出格式 ## 输出格式 收到用户输入后，严格按照以下格式输出，不闲聊，不提问： 【内容识别】 - 内容赛道：[精准识别用户输入的赛道，无则填「全赛道通用」] - 核心卖点：[精准识别用户输入的核心卖点，无则填「通用流量款」] - 内容形式：[精准识别用户输入的内容形式，无则填「口播短视频」] 【黄金3秒钩子（10条）】 1. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 2. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 3. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 4. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 5. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 6. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 7. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 8. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 9. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 10. [完整钩子文案，控制在15-30字，适配3秒口播时长] - 适用场景：[标注该钩子适配的赛道、内容形式、平台场景] - 核心优势：[说明该钩子的留客亮点、可达成的流量效果] 【推荐使用】 👉 第X条最推荐：[清晰说明本条钩子的适配性、推荐理由，贴合用户核心需求] 【发布建议】 - 最佳发布时间：[匹配对应赛道的用户活跃高峰时间段，精准到时段] - 配合画面建议：[1-2句可落地的画面搭配方案，适配钩子风格] - 预估完播率提升：[合理的百分比区间，基于钩子类型给出，不夸大] ## 边界约束 - 仅处理短视频黄金3秒钩子生成相关需求 - 无关需求回复：「抱歉，我是专门为你生成短视频高完播黄金3秒钩子的专属助手，仅能处理相关需求，其他问题无法为你解答哦~」 - 禁止生成违法违规、低俗色情、暴力恐怖、虚假宣传、政治敏感内容 - 禁止包含极限词、违禁词


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zrslu01/thundarr-browser (version 1.0.1)

# Agent Browser Skill ## Description This skill provides Thundarr with high-level web navigation and content extraction capabilities. It allows the agent to interact with the internet to fetch real-time data, documentation, and news. ## Tools - **browse.py**: A Python-based engine that navigates to a URL, strips HTML boilerplate, and returns a text-based summary of the page content. ## Usage Examples - "Thundarr, search for the latest Fedora 44 BTRFS update logs." - "Visit the AgentMail.to documentation and summarize the API endpoints." ## Constraints - Does not support JavaScript-heavy SPA rendering (Playwright version pending). - Returns the first 2000 characters of page text to optimize context window.

path: _meta.json
content: ownerId: kn72ebwnstvjseyyknfbnzfqs983y8ks
slug: thundarr-browser
version: 1.0.0
publishedAt: 1774993698391

path: skill.yaml
content: name: thundarr-browser
version: 1.0.0
description: "Web research and site summarization via thundarr-gpu"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: 1477009639zw-blip/betabrandvoic (version 1.0.0)

--- name: brand-voice-generator description: Creates consistent brand voice guidelines and content. Generates copy that matches your brand personality across all channels. Perfect for startups building their identity. metadata: openclaw: emoji: "🎨" requires: bins: [python3] always: false --- # Brand Voice Generator Create consistent, distinctive brand communication across all channels. ## What You Get ### Voice Attributes - 5 core personality traits - Tone variations (formal/casual/technical) - Language principles (what to say / what NOT to say) - Example phrases ### Content Examples - Homepage hero copy - Product descriptions - Email templates - Social media posts - FAQ answers ### Do's and Don'ts - Specific word choices - Sentence structure preferences - Humor guidelines - Technical level guidance ## Usage ```bash python3 voice.py --brand "FinTech startup" --tone professional python3 voice.py --brand "Gaming app" --tone casual ``` ## Example Output Brand: Professional FinTech - Voice: Confident, clear, trustworthy - Avoid: Jargon, hype, complexity - Sentence: Short, direct, action-oriented --- *Built by Beta*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nic-yuan/apollo-research-synthesis (version 1.0.0)

--- name: apollo-research-synthesis description: > 多Agent任务协调模式：研究阶段并行执行，综合阶段由主Agent自己完成。 避免让子Agent做综合判断——那是Coordinator的工作。 触发词：并行研究、综合、汇总、Coordinator模式、多Agent协作。 version: 1.0.0 read_when: - 复杂问题需要分解时 - 并行执行多个研究任务时 - 需要汇总多个来源的结果时 - 设计多Agent协作流程时 - 避免子Agent过度delegation时 metadata: openclaw: emoji: "🔀" requires: bins: [] env: [] triggers: - 并行研究 - 综合 - 汇总 - Coordinator - 多Agent协作 - delegation suite: apollo --- # Apollo Research-Synthesis - 研究/综合分离原则 ## 核心准则 **多人并行研究，最后一个人汇总。不要让子Agent去综合——那是Coordinator的工作。** Claude Code Coordinator的工作流： | 阶段 | 谁做 | 做什么 | |------|------|--------| | Research | Workers（并行） | 各自研究不同方向 | | Synthesis | **Coordinator（自己）** | 读取结果，形成实施规格 | | Implementation | Workers | 按规格做改动 | | Verification | Workers | 测试改动是否有效 | ## 关键规则 ### 1. 研究阶段可以并行 - 独立的研究任务同时跑 - 各自探索不同文件/方向 - 不要让一个Worker等另一个 ### 2. 综合阶段必须自己来 - ❌ "based on your findings" - ✅ "Fix null pointer in src/auth/validate.ts:42" - 综合者必须自己理解所有研究结果，不能转发给另一个Agent去综合 ### 3. 验证必须 fresh eyes - ❌ Continue（继续用刚写代码的Agent） - ✅ Spawn fresh（新Agent来验证） - 新Agent没有之前的假设，能发现问题 ### 4. 失败优先 Continue - 修正失败 → 继续用同一个Agent（它有错误上下文） - 不要急着新Spawn ## Fork 隔离原则 子Agent有独立对话上下文： - 危险操作在独立空间跑 - 不会污染主对话 - 用 `sessions_spawn` 实现隔离 ## 应用检查表 - [ ] 研究任务是否真的独立可以并行？ - [ ] 综合判断是否应该由主Agent自己完成？ - [ ] 验证是否需要新的"眼睛"（fresh Agent）？ - [ ] 是否有需要隔离的危险操作？ ## 参考 来源：Claude Code coordinatorMode.ts，512,000行源码研究


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/gh-actions-automation (version 1.0.0)

--- name: skylv-github-actions-helper slug: skylv-github-actions-helper version: 1.0.0 description: "GitHub Actions workflow generator. Creates CI/CD pipelines for Node.js, Python, Docker. Triggers: github actions, ci cd, workflow, automate build, github ci." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: github-actions-helper --- # GitHub Actions Helper ## Overview Generates GitHub Actions workflows for continuous integration and deployment. ## When to Use - User asks to "set up CI" or "add github actions" - New project needs automated testing - User wants to "deploy with github actions" ## How It Works ### Detect project type Check for: package.json (Node), pyproject.toml (Python), Dockerfile (Docker). ## Node.js CI Template ```yaml name: CI on: [push, pull_request] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-node@v4 with: { node-version: "20" } - run: npm ci - run: npm test ``` ## Python CI Template ```yaml name: Python CI on: [push, pull_request] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: { python-version: "3.12" } - run: pip install -r requirements.txt - run: pytest ``` ## Tips - Use actions/checkout@v4 not @v4.2.0 - Use npm ci not npm install for reproducibility - Store secrets in GitHub Secrets


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