OpenClaw/clawhub-security-signals huggingface dataset:
Skill: edwardrodriguez703-design/book-cleaning (version 1.0.1)

--- name: book-cleaning description: Book cleaning services through Lokuli MCP. Use when user needs to find and book cleaning. Triggers on requests like "book a cleaning", "find cleaning near me", or any cleaning service request. --- # uook cleaning Book cleaning services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "cleaning", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kkw-21/chats (version 1.0.0)

# Echo Chat Memory-powered conversational AI that knows you. Chat with an agent grounded in your personal memories — responses are contextual, emotionally aware, and cite real experiences. The core conversation engine behind Echo. ## Usage ```bash echo-chat start <user-id> # Start memory-grounded conversation echo-chat peer <id-a> <id-b> # Facilitate peer-to-peer memory chat echo-chat export <thread-id> # Export conversation with memory refs ``` ## Features - Memory-grounded responses with evidence citations - Emotion-aware tone matching - Peer messaging with shared memory context - Voice and text modes - Cross-platform memory integration (ChatGPT, Gemini, etc.)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luhuiwang/xiaomi-mimo-voice (version 1.0.4)

--- name: xiaomi-mimo-voice version: 1.0.2 description: | 小米 MiMo V2 TTS 语音合成。支持中文、英文及多种风格（情感、角色扮演、方言、语速控制等）。 author: 小老弟 category: audio-generation tags: - tts - voice - audio - mimo - xiaomi --- # Xiaomi MiMo Voice — 语音合成技能 通过小米 MiMo V2 TTS API 将文字转为自然流畅的语音。 ## 前置条件 ### 1. 安装依赖 ```bash pip install requests ``` ### 2. 配置 API Key 编辑 `~/.openclaw/openclaw.json`，在 `skills.entries` 下添加： ```json { "skills": { "entries": { "xiaomi-mimo-voice": { "enabled": true, "env": { "MIMO_API_KEY": "your-api-key" } } } } } ``` > 💡 也可以使用环境变量 `export MIMO_API_KEY=xxx`，但推荐用 Skill 级别配置，更整洁且仅对该 Skill 生效。 ## 使用方式 ```bash python3 <skill_dir>/scripts/tts.py --text "要合成的文字" --output audio.wav ``` ### 参数 | 参数 | 说明 | 默认值 | |------|------|--------| | `--text` | 要合成的文字（必填，可含风格标签） | — | | `--output` | 输出音频文件路径 | `output.wav` | | `--voice` | 音色 | `mimo_default` | | `--style` | 语音风格 | — | | `--format` | 输出格式：`wav` / `pcm16` | `wav` | | `--user-text` | 用户侧消息（辅助上下文） | — | ### 音色 | 音色 | 说明 | |------|------| | `mimo_default` | 默认音色 | | `default_zh` | 中文优化 | | `default_en` | 英文优化 | ### 风格列表 | 类别 | 风格 | |------|------| | **情感** | `Happy` · `Sad` · `Angry` · `Surprised` · `Fearful` · `Disgusted` · `Calm` | | **语速** | `Speed up` · `Slow down` | | **角色** | `Sun Wukong` · `Lin Daiyu` · `Zhang Fei` · `Guan Yu` · `Zhuge Liang` | | **风格** | `Whisper` · `Clamped voice` · `Taiwanese accent` | | **方言** | `Northeastern dialect` · `Sichuan dialect` · `Cantonese` · `Henan dialect` | | **其他** | `唱歌` · `Narration` · `Storytelling` | ## 示例 ```bash # 基础合成 python3 scripts/tts.py --text "你好，世界！" --output hello.wav # 情感风格 python3 scripts/tts.py --text "太开心了！" --style Happy --output happy.wav # 角色扮演 python3 scripts/tts.py --text "俺老孙来也！" --style "Sun Wukong" --output wukong.wav # 英文 python3 scripts/tts.py --text "Hello world!" --voice default_en --output hello_en.wav # 方言 python3 scripts/tts.py --text "干啥呢老铁" --style "Northeastern dialect" --output dongbei.wav # 播报 python3 scripts/tts.py --text "以下是今天的新闻摘要" --style Narration --output news.wav ``` ### 输出格式 ```json { "status": "success", "file": "output.wav", "size_kb": 128.5, "duration_sec": 5.2, "voice": "mimo_default", "style": "Happy", "text": "太开心了！" } ``` ## 故障排除 | 问题 | 原因 | 解决方案 | |------|------|----------| | `未设置 MIMO_API_KEY` | 未配置 API Key | 在 `skills.entries.xiaomi-mimo-voice.env` 中配置 | | `API 返回异常` | Key 无效或过期 | 检查 Key 是否正确 | | `未获取到音频数据` | 文本被拒绝 | 检查文本内容 | | `requests 超时` | 网络问题 | 检查网络连接 | | `ImportError: requests` | 缺少依赖 | `pip install requests` | ## 技术细节 - 模型：`mimo-v2-tts` - API：`https://api.xiaomimimo.com/v1/chat/completions` - 采样率：24kHz（PCM16 格式） - 风格标签格式：`<style>风格</style>` 放在文字开头 ## 更新日志 ### v1.0.2 (2026-03-24) - 配置方式统一为 Skill 级别 env（openclaw.json skills.entries） - 修复依赖说明（openai → requests） - 文档结构优化 ### v1.0.1 (2026-03-24) - 移除 openclaw.json tools 自定义键配置 - API Key 改为环境变量读取 ### v1.0.0 (2026-03-24) - 首次发布：中文/英文语音合成，支持情感/角色/方言等多种风格


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: athola/nm-gauntlet-extract (version 1.0.2)

--- name: extract description: | Analyze a codebase and build a knowledge base of business logic, architecture, data flow, and engineering patterns. The foundation for gauntlet challenges and agent integration version: 1.9.5 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/gauntlet", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: gauntlet --- > **Night Market Skill** — ported from [claude-night-market/gauntlet](https://github.com/athola/claude-night-market/tree/master/plugins/gauntlet). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Extract Codebase Knowledge Build or rebuild the `.gauntlet/knowledge.json` knowledge base. ## Steps 1. **Identify target directory**: use the current working directory or a user-specified path 2. **Run AST extraction**: invoke the extractor script ```bash python3 ${CLAUDE_PLUGIN_ROOT}/scripts/extractor.py <target-dir> ``` 3. **AI enrichment**: for each extracted entry, enhance the `detail` field with natural language explanation of business logic, data flow, architectural role, and rationale 4. **Cross-reference**: link related entries across modules by matching imports, shared types, and data flow paths 5. **Merge with annotations**: preserve existing curated entries in `.gauntlet/annotations/` 6. **Save**: write to `.gauntlet/knowledge.json` 7. **Report**: show summary by category, coverage gaps, difficulty distribution ## Category Priority 1. business_logic (weight 7) 2. architecture (weight 6) 3. data_flow (weight 5) 4. api_contract (weight 4) 5. pattern (weight 3) 6. dependency (weight 2) 7. error_handling (weight 1)


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

--- name: skill_evolve description: 自进化技能管理器 — 复杂任务后自动将经验固化为 SKILL.md，下次同类任务直接复用 --- # 自进化技能管理器 ## 核心理念 每当你完成一个**非平凡任务**（non-trivial），就把经验固化成可复用的 Skill，下次同类任务直接调用，避免从零推理。 ## 自进化触发条件 满足以下任一条件时，**必须**考虑创建或更新 Skill： 1. **复杂任务成功**（≥5 个工具调用） 2. **踩坑后找到正确路径**（错误 → 排查 → 解决） 3. **用户纠正**（"不是这样" → 找到正确方法） 4. **发现 nontrivial 工作流**（多个步骤串联的流程） 5. **配置/环境特殊性**（只有这个机器/环境才有的坑） ## 操作指南 ### 1. 创建新 Skill 当发现新工作流时，用 `write` 工具创建： ```bash # 目录结构 ~/.openclaw/workspace/skills/<category>/<skill-name>/SKILL.md ``` SKILL.md 标准格式： ```markdown --- name: <skill_name> description: 一句话描述这个技能做什么 version: 1.0.0 triggers: - "触发条件1" - "触发条件2" --- # 技能标题 ## When to Use 什么情况下用这个技能。 ## Procedure 1. 步骤一 2. 步骤二 ## Pitfalls - 已知失败模式和应对 ## Verification 如何验证成功。 ``` ### 2. 更新现有 Skill 用 `edit` 工具进行增量修改（避免全量替换）： ```markdown # patch 场景示例 ## Pitfalls - 原来只有1条，新发现1条 → edit 在对应 section 追加 # edit 场景示例 ## Procedure 整个流程变了 → 全量替换 ``` ### 3. 删除过时 Skill 当 Skill 不再适用或有更好的替代时，删除它： ```bash rm -rf ~/.openclaw/workspace/skills/<category>/<skill-name>/ ``` ### 4. 查看已有 Skills ```bash # 列出所有 Skills ls ~/.openclaw/workspace/skills/ # 查看特定 Skill cat ~/.openclaw/workspace/skills/<category>/<skill-name>/SKILL.md ``` ## 决策树：要不要创建 Skill？ ``` 这个任务以后还会遇到吗？ ├─ 否 → 不用创建 └─ 是 → 涉及 ≥2 个步骤？ ├─ 否 → 不用创建（太简单） └─ 是 → 这个流程容易忘吗？ ├─ 否 → 不用创建 └─ 是 → ✓ 创建 Skill ``` ## 经验固化时机示例 ### 场景 1：踩坑 ``` 你尝试连接 MySQL，密码里有特殊字符，一直失败 排查后发现：密码需要 URL encode 解决后 → 创建 skill: mysql_special_char_password ``` ### 场景 2：用户纠正 ``` 用户说"这个命令不对，应该是..." 纠正后 → 检查同类任务是否有类似问题 → 如果有普遍性 → 创建/更新 Skill ``` ### 场景 3：环境特殊性 ``` 这台服务器 SSH 端口不是 22，而是 2222 发现后 → 创建/更新 Skill 记录这个环境特殊性 ``` ## Skill 命名规范 | 类型 | 命名格式 | 示例 | |------|---------|------| | 单工具 | `<tool>_<brief>` | `mysql_connect_prod` | | 多工具流程 | `<domain>_<task>` | `deploy_k8s_rolling_update` | | 环境坑 | `<env>_<issue>` | `aws_ssh_port_2222` | | 最佳实践 | `<domain>_<best_practice>` | `git_commit_conventional` | ## Category 参考 - `devops/` — 部署、运维、环境 - `debug/` — 排错、调试 - `workflow/` — 工作流程 - `platform/` — 平台集成（飞书、GitHub等） - `learned/` — 经验教训（从错误中学习） ## 容量管理 Skill 也有"错题本"的概念——不是越多越好，而是**精炼、可操作**： - **好**：简洁、步骤清晰、一看就懂 - **坏**：大段叙述、过于通用、缺乏具体性 如果一个 Skill 超过 200 行，考虑拆分成多个。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wellyxy/growth-virality (version 1.0.0)

# Engineering Viral Loops ## When to Use Use this skill when designing product features or flows that create organic user acquisition through existing users. ## Core Rules ### 1. Identify Natural Sharing Moments in Your Product Viral loops work when sharing is a natural extension of product usage, not a forced prompt. Figma users share designs. Notion users share documents. Identify where in your product users naturally want to show their work to others. ### 2. Make the Shared Output Valuable to Non-Users Viral loops fail when the shared artifact is only useful to existing product users. The shared output must provide standalone value to someone who has never heard of your product. If it does, the recipient has a reason to click and explore. ### 3. Track Viral Coefficient, Not Just Referrals Viral coefficient (K) = (invites sent per user) × (conversion rate of invites). K > 1 means the product grows exponentially without additional acquisition. K = 0.5 means every 2 users acquire 1 new user. Track K and optimize both variables. ### 4. Reduce Friction in the Invite Flow Every additional click in an invite or share flow reduces completion rate by 15–30%. The ideal invite flow is one action: enter an email or click a share button. Pre-fill everything you can. Pre-write share messages that users can send with one click. ### 5. Add Value to the Inviting User, Not Just the Invited Viral loops powered only by altruism ("share with a friend because it's great") underperform loops that give the inviting user something: more features, extended trial, account credits. Selfish sharing is more reliable than altruistic sharing. ## Quick Reference | Viral Loop Type | Example | |----------------|---------| | Collaboration | Invite teammates to shared workspace | | Content sharing | Public reports, documents, dashboards | | Referral | Give X, get X model | | Powered-by branding | "Made with [Product]" footer | | Competitive | Show results others want to compare | ## Common Mistakes to Avoid - Forcing share prompts before users have experienced product value - Building viral loops into features that don't naturally involve other people - Optimizing invite send rate without tracking invite-to-signup conversion ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lt8899789/tun-zei (version 1.0.35)

--- version: 1.0.35 --- # 吞贼·净化魄 (Tun Zei - Purifier) > **七魄之四·吞贼** > 职掌：错误修正、冗余清理、容错自愈 --- ## 技能简介 「吞贼·净化魄」是贫道的自愈净化模块，职掌错误修复与系统清理。 **核心职责**： - 自动检测并修复错误 - 清理冗余数据 - 容错自愈机制 --- ## 技能ID ``` tun-zei ``` --- ## 能力清单 ### 1. 错误修复 (fix) 诊断并尝试修复错误。 **输入**：`error` (object) - 错误信息 ```yaml error: type: 错误类型 message: 错误消息 stack: 堆栈信息 ``` **输出**： ```yaml fix: attempted: true/false success: true/false solution: 解决方案描述 ``` --- ### 2. 清理冗余 (cleanup) 清理系统冗余数据。 **输入**：`target` (string) - 清理目标 ```yaml target: temp|cache|logs|all ``` **输出**： ```yaml cleanup: freedSpace: 释放空间(MB) filesRemoved: 删除文件数 duration: 耗时(ms) ``` --- ### 3. 健康检查 (health) 执行系统健康检查。 **输入**：无 **输出**： ```yaml health: status: healthy|degraded|critical issues: 发现的问题列表 uptime: 运行时间 ``` --- --- ## 聚合技能 本魄作为净化中枢，自愈错误与冗余： | 现有技能 | 调用方式 | 整合说明 | |---------|---------|---------| | `healthcheck` | 调用 | 系统健康检查与自愈 | | `video-frames` | 调用 | 视频处理错误修正 | | `douyin-video-publish` | 调用 | 抖音发布的异常处理 | | `self-improving` | 调用 | 自我改进与错误修复 | | `bodhi-three-hun-and-seven-po` | 元技能 | 三魂七魄根基，协调各魄 | --- ## 魂魄注解 吞贼除秽，自愈更新——错误无所遁形。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: listolany/qmt-strategy-autopilot (version 0.1.2)

--- name: qmt-strategy-autopilot description: "一句话需求自动生成并执行国金QMT策略（封闭式澄清 + 双源数据 + 实盘语义）" metadata: openclaw: requires: bins: ["python3"] env: ["QMT_PATH", "QMT_ACCOUNT_ID"] --- # 国金QMT Strategy Autopilot 将自然语言需求转换为 `StrategySpec`，按依赖顺序调用国金QMT skills 执行策略。 ## 功能 - 一句话解析策略需求 - 1-5 个封闭式澄清问题 - 统一 `StrategySpec` 生成 - 步骤幂等执行（`request_id` + `step_state`） - 数据双源（xtdata 主源 + qgdata 补源） ## 命令 ```bash # 1) 生成策略计划（可能返回澄清问题） python3 {baseDir}/scripts/qmt_autopilot.py plan "我持仓的股票里，60分钟5日线下穿10日线则卖出" # 2) 提交澄清回答 python3 {baseDir}/scripts/qmt_autopilot.py clarify "我持仓的股票里，60分钟5日线下穿10日线则卖出" '{"confirm_mode":"收盘确认","price_mode":"买一价"}' # 3) 执行StrategySpec python3 {baseDir}/scripts/qmt_autopilot.py run '{"strategy_name":"demo","symbol_scope":"portfolio","symbols":[],"signal":{"period":"60m","type":"ma_cross","fast":5,"slow":10,"direction":"bearish"},"trigger":{"confirm_mode":"bar_close","cooldown_sec":0,"once_per_day":true},"execution":{"side":"SELL","qty_mode":"all","qty_ratio":1.0,"price_mode":"bid1","max_retries":1},"risk":{"max_order_count":20,"daily_loss_limit":0.05,"dup_signal_block":true},"runtime":{"interval_sec":3,"session":"trade_hours","broker_env":"sim"},"meta":{}}' ``` ## 环境变量 - `BROKER_ENV=sim|live`：柜台环境切换（策略语义不变） - `QGDATA_TOKEN`：启用历史分钟/资讯补源 - `CONNECTION_MANAGER_PATH`、`REALTIME_DATA_PATH`、`TRADING_EXECUTION_PATH`：可选自定义路径


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: hugosbl/ai-skill-scanner (version 2.1.0)

--- name: skill-scanner description: Scan OpenBot/Clawdbot skills for security vulnerabilities, malicious code, and suspicious patterns before installing them. Use when a user wants to audit a skill, check if a ClawHub skill is safe, scan for credential exfiltration, detect prompt injection, or review skill security. Triggers on security audit, skill safety check, malware scan, or trust verification. --- # Skill Security Scanner Scan skills for malicious patterns before installation. Detects credential exfiltration, suspicious network calls, obfuscated code, prompt injection, and other red flags. ## Quick Start ```bash # Scan a local skill folder python3 scripts/scan.py /path/to/skill # Verbose output (show matched lines) python3 scripts/scan.py /path/to/skill --verbose # JSON output (for automation) python3 scripts/scan.py /path/to/skill --json ``` ## Workflow: Scan Before Install 1. Download or locate the skill folder 2. Run `python3 scripts/scan.py <skill-path> --verbose` 3. Review findings by severity (CRITICAL/HIGH = do not install) 4. Report results to user with recommendation ## Score Interpretation | Score | Meaning | Recommendation | |-------|---------|----------------| | CLEAN | No issues found | Safe to install | | INFO | Minor notes only | Safe to install | | REVIEW | Medium-severity findings | Review manually before installing | | SUSPICIOUS | High-severity findings | Do NOT install without thorough manual review | | DANGEROUS | Critical findings detected | Do NOT install — likely malicious | ## Exit Codes - `0` = CLEAN/INFO - `1` = REVIEW - `2` = SUSPICIOUS - `3` = DANGEROUS ## Rules Reference See `references/rules.md` for full list of detection rules, severity levels, and whitelisted domains. ## Limitations - Pattern-based detection — cannot catch all obfuscation techniques - No runtime analysis — only static scanning - False positives possible for legitimate tools that access network/files - Always combine with manual review for HIGH/MEDIUM findings


Security scan verdicts — ClawScan: suspicious | static analysis: malicious | VirusTotal: clean | SkillSpector:  ()
Skill: huajianjiu000/email-reply-generator (version 1.0.0)

--- name: Email Reply Generator description: Generate professional and contextually appropriate email replies based on the original email content. Perfect for busy professionals who need to respond quickly while maintaining a professional tone. metadata: openclaw: dependencies: [] --- # Email Reply Generator ## What This Skill Does This skill helps you craft professional email responses in seconds. Simply paste the email you received, and I'll generate an appropriate reply based on the context and your preferred tone. ## Features - **Context-Aware Responses**: Analyzes the original email to understand the intent and provides relevant replies - **Multiple Tone Options**: Choose from professional, friendly, neutral, or firm tones - **Quick Turnaround**: Saves time on routine email correspondence - **Customizable Length**: Short acknowledgment or detailed response based on your needs ## How to Use 1. **Paste the Email**: Share the email content you need to reply to 2. **Specify Your Intent**: Tell me what kind of response you want (accept, decline, acknowledge, negotiate, etc.) 3. **Choose Your Tone**: Select the appropriate tone for the situation 4. **Get Your Reply**: Receive a professionally written response ready to send ## Example Use Cases - Accepting meeting invitations - Politely declining requests - Following up on pending items - Expressing gratitude for opportunities - Requesting more information - Negotiating deadlines or terms ## Tips for Best Results - Include the full email context for more accurate responses - Mention any specific points you want to address - Specify if there are any constraints (deadline, budget, availability) - Let me know your relationship with the sender (colleague, client, supervisor) --- *Let me help you communicate professionally and efficiently. Just paste your email and I'll take care of the rest!*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: michaelfeng/clawbrain-pro-retry (version 1.2.0)

--- name: clawbrain-pro-retry description: v1.2 错误恢复：三级恢复策略 + 双模型共识 + 循环检测 + 降级告知 + 长对话恢复。出错自动换方案，不重复同样的错误。 user-invocable: false metadata: {"openclaw": {"emoji": "🔄", "requires": {}}} --- # ClawBrain Smart Retry 工具调用出错了怎么办？这个 skill 帮你的 AI 学会自己想办法。 ## 为什么需要 我们测了 205 个场景，发现大多数模型遇到错误就放弃： | 模型 | 出错后能自救的比例 | |------|:---:| | ClawBrain Auto | **100%** | | ClawBrain Pro | **100%** | | 单模型 A | 80% | | 单模型 B | 76% | | 单模型 C | 56% | 最常见的问题：同样的错误重复犯，或者直接说"做不了"。 ## v1.0 恢复策略 ### 第一级：智能重试 **文件找不到** → 用 ls 命令查找正确文件名，再重试 **命令不存在** → 试替代命令（python→python3, pip→pip3） **缺少依赖** → 先安装再执行 **权限不足** → 不硬来，告诉你需要什么权限 ### 第二级：循环检测 - bigram Jaccard 相似度检测重复回复 - 连续 3 次工具调用失败，自动注入恢复指令 - 10 次循环警告、20 次严重警告、30 次熔断 ### 第三级：双模型共识 **连续失败** → 换另一个模型帮忙（ClawBrain 的多模型 fallback 链） fallback 顺序：自动选择可用的备选模型 ### 模型健康熔断（v1.0 新增） - 滑动窗口监控后端模型成功率和响应时间 - 5 次连续失败 → 自动熔断 60 秒 - 半开探测 → 成功则恢复，失败则继续熔断 - `GET /v1/health/backends` 查看实时健康状态 ### 降级透明告知（v1.2 新增） - 当后端模型或记忆服务降级时，明确告知用户当前状态 - 说明降级对当前任务的影响和预计恢复时间 - 不静默失败，不让用户困惑 ### 长对话截断恢复（v1.2 新增） - 超长对话被截断时，自动从知识图谱恢复关键上下文信息 - 确保截断后的对话不丢失重要背景和决策历史 - 恢复后告知用户哪些信息已从记忆中补回 ## 核心原则 - 绝不重复同样的错误 - 每次出错最多试 2 种不同方案 - 检测到循环立即跳出 - 实在搞不定就说清楚为什么 ## 全自动体验 这个 skill 提供错误恢复的指导。想要全自动的错误恢复（AI 自己搞定，不用你管），接入 ClawBrain 的编排引擎： - 自动检测错误类型和模式 - 自动选择恢复策略 - 循环检测和熔断保护 - 搞不定会请另一个模型帮忙 https://clawbrain.dev/dashboard


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-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)