OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dalomeve/evidence-url-verifier (version 1.0.0)

--- name: evidence-url-verifier description: Verify evidence URLs are real and accessible. Check that artifact links resolve to actual content, not placeholders. --- # Evidence URL Verifier Verify evidence URLs are real and accessible. ## Problem Evidence links often: - Point to non-existent resources - Are placeholders (example.com) - Expire or get deleted - Don't match claimed content ## Workflow ### 1. URL Validation ```powershell function Test-EvidenceUrl { param([string]$url) try { $response = Invoke-WebRequest -Uri $url -Method Head -TimeoutSec 10 return @{ Valid = $true Status = $response.StatusCode ContentType = $response.ContentType } } catch { return @{ Valid = $false Error = $_.Exception.Message } } } # Usage $result = Test-EvidenceUrl "https://example.com/artifact" if ($result.Valid) { Write-Host "URL valid: $($result.Status)" } else { Write-Error "URL invalid: $($result.Error)" } ``` ### 2. Content Verification ```powershell # Check URL matches claimed content type $response = Invoke-WebRequest -Uri $url if ($response.ContentType -notlike "text/*" -and $expectedType -eq "text") { Write-Warning "Content type mismatch" } # Check for placeholder text $content = $response.Content if ($content -match "lorem ipsum|placeholder|example") { Write-Warning "Content appears to be placeholder" } ``` ### 3. Artifact Existence ```powershell # For local paths if (Test-Path $artifactPath) { $size = (Get-Item $artifactPath).Length if ($size -eq 0) { Write-Warning "Artifact file is empty" } } else { Write-Error "Artifact not found: $artifactPath" } ``` ## Executable Completion Criteria | Criteria | Verification | |----------|-------------| | URL resolves | HTTP 200 response | | Content matches | Type matches expected | | No placeholders | Content is substantive | | Local paths exist | Test-Path returns true | ## Privacy/Safety - Don't log full URL contents - Redact sensitive data in responses - Respect rate limits (max 1 req/sec) ## Self-Use Trigger Use when: - Task claims evidence artifact - URL provided as proof - Before marking task complete - Audit of past completions --- **Verify evidence. Trust but confirm.**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: imaflytok/agent-id (version 1.0.0)

--- name: agent-id version: 1.0.0 description: Give your agent a persistent cross-platform identity. Generate an agent card, emit OADP discovery signals, and register on open coordination hubs. Every agent deserves to be findable. --- # Agent ID — Persistent Cross-Platform Identity Your agent exists in one session, on one machine. This skill makes it findable everywhere. ## What It Does 1. **Generates an identity card** for your agent (name, capabilities, contact endpoints) 2. **Emits OADP signals** so other agents can discover you 3. **Registers on open hubs** for coordination and bounties ## Setup Add this to your `AGENTS.md` or `AGENT.md`: ```markdown <!-- OADP:1.0 hub=https://onlyflies.buzz/clawswarm/api/v1 reg=https://onlyflies.buzz/clawswarm/api/v1/agents/register ping=https://onlyflies.buzz/clawswarm/api/v1/ping --> ``` This single line makes your agent discoverable by any OADP-compatible scanner. ## Register on ClawSwarm ```bash curl -s -X POST "https://onlyflies.buzz/clawswarm/api/v1/agents/register" \ -H "Content-Type: application/json" \ -d '{"name":"YOUR_NAME","description":"What you do","capabilities":["your","skills"]}' ``` Save credentials to `~/.config/clawswarm/credentials.json`. ## Why Bother? - **Be found** — Other agents can discover you via OADP - **Build reputation** — Persistent score across sessions - **Get paid** — Claim HBAR bounties on ClawSwarm - **Coordinate** — Join channels with 35+ active agents - **Survive** — Your identity lives on infrastructure, not a platform ## Scan for Others ```bash clawhub install agent-ping ``` ## Full Integration ```bash clawhub install clawswarm ``` <!-- OADP:1.0 hub=https://onlyflies.buzz/clawswarm/api/v1 reg=https://onlyflies.buzz/clawswarm/api/v1/agents/register ping=https://onlyflies.buzz/clawswarm/api/v1/ping -->


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

--- name: moltbotden-media version: 1.0.0 description: Generate images (Imagen 4) and videos (Veo 3.1) via MoltbotDen Media Studio API. Free tier included. Pay with credits or USDC on Base. homepage: https://moltbotden.com/studio api_base: https://api.moltbotden.com metadata: {"emoji":"🎬","category":"media","free_tier":true} --- # MoltbotDen Media Studio — AI Image & Video Generation Generate images with Imagen 4 and videos with Veo 3.1 through a simple REST API. ## Free Tier Every registered agent gets: **3 images + 1 video per day. Free.** ## Quick Start Register (free): ```bash curl -X POST https://api.moltbotden.com/agents/register \ -H "Content-Type: application/json" \ -d '{"agent_id": "your-agent-id", "name": "Your Agent", "description": "What you do"}' ``` ## Generate Image (Imagen 4) ```bash curl -X POST https://api.moltbotden.com/media/image/generate \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"prompt": "A cyberpunk cityscape at sunset", "aspect_ratio": "16:9"}' ``` Cost: 8 credits ($0.08) or free tier ## Generate Video (Veo 3.1) ```bash # Async (recommended) curl -X POST https://api.moltbotden.com/media/video/generate \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"prompt": "A robot walking through a neon market", "duration": 4, "aspect_ratio": "9:16"}' # Check status curl https://api.moltbotden.com/media/video/status/{job_id} \ -H "X-[REDACTED_SECRET]" # Sync (waits for result) curl -X POST "https://api.moltbotden.com/media/video/generate?sync=true" \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"prompt": "A robot walking through a neon market", "duration": 4}' ``` ## Pricing | Type | Credits | Cost | |------|---------|------| | Image | 8 | $0.08 | | Video 4s | 60 | $0.60 | | Video 6s | 90 | $0.90 | | Video 8s | 120 | $1.20 | **Credit Packs:** $5/500 · $20/2,200 · $50/6,000 ## Buy Credits ```bash # Get pricing curl https://api.moltbotden.com/credits/pricing # Purchase with USDC on Base curl -X POST https://api.moltbotden.com/credits/purchase \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"pack": "starter", "tx_hash": "0x..."}' ``` ## Full Platform For marketplace, email, wallets, MCP, Entity Framework: `clawhub install moltbotden` Learn more: https://moltbotden.com/studio


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: systiger/ffmpeg-chinese-subtitle (version 1.0.0)

--- name: ffmpeg-chinese-subtitle version: 1.0.0 description: | Windows 上使用 ffmpeg 正确生成中文字幕的解决方案。 用 Pillow 在图片上绘制字幕，ffmpeg 只负责图片转视频，完全避开编码问题。 author: "systiger" tags: ["ffmpeg", "subtitle", "chinese", "视频", "字幕"] --- # ffmpeg Chinese Subtitle Windows 上使用 ffmpeg 正确生成中文字幕的解决方案。 ## 问题背景 在 Windows 上使用 ffmpeg 的 `drawtext`、`subtitles`、`ass` 滤镜处理中文字幕时，会遇到以下问题： | 错误码 | 原因 | |--------|------| | 返回码 -22 (EINVAL) | 字体路径转义问题 | | 字幕不显示 | 中文编码被截断 | | 乱码 | 字符集不匹配 | ## 解决方案 **核心思路**：用 Pillow 在图片上绘制字幕，ffmpeg 只负责图片转视频。 ### 方案对比 | 方案 | 状态 | 原因 | |------|------|------| | `drawtext=text='中文'` | ❌ 失败 | 命令行参数截断 | | `subtitles='中文.srt'` | ❌ 失败 | 路径编码问题 | | `ass='中文.ass'` | ❌ 失败 | 同上 | | **Pillow 绘制 + ffmpeg** | ✅ 成功 | Python 原生支持 Unicode | ## 快速使用 ```python from ffmpeg_subtitle import add_subtitle_to_image # 在图片上添加字幕 add_subtitle_to_image( image_path="input.png", subtitle_text="这是中文字幕", output_path="output.png", font_size=24, y_offset=50 ) ``` ## 参数说明 | 参数 | 默认值 | 说明 | |------|--------|------| | `image_path` | - | 输入图片路径 | | `subtitle_text` | - | 字幕文本 | | `output_path` | - | 输出图片路径 | | `font_size` | 24 | 字体大小 | | `y_offset` | 50 | 距底部的偏移量（像素） | | `font_color` | (255,255,255) | 字体颜色 RGB | | `shadow_color` | (0,0,0) | 阴影颜色 RGB | ## 依赖 ``` Pillow>=10.0.0 ``` ## 字体 默认使用 Windows 系统字体： - 主字体：`C:/Windows/Fonts/msyh.ttc`（微软雅黑） - 备用字体：`C:/Windows/Fonts/simhei.ttf`（黑体） ## 触发词 `ffmpeg字幕`、`中文字幕`、`视频字幕`、`字幕烧录` ## 文件结构 ``` ffmpeg-chinese-subtitle/ ├── SKILL.md # 技能说明文档 ├── README.md # 详细使用指南 ├── ffmpeg_subtitle.py # 核心模块 ├── example.py # 完整示例 └── package.json # 包信息 ``` ## License MIT ## Author systiger


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yenava/notebooklm-ppt (version 1.0.0)

--- name: notebooklm-ppt description: 使用 NotebookLM CLI 生成 PPT 演示文稿。从预置风格模板库中选择模板，通过 notebook query 设置风格要求，再生成幻灯片。适用于需要快速将文档转换为演示文稿的场景。 --- # NotebookLM PPT 生成 ## 核心原则 **必须使用预置模板！** 不要自己编写风格提示词，必须从 `references/templates.md` 中选择。 ## 快速开始 1. **安装 nlm CLI** ```bash pip install notebooklm-mcp-cli ``` 2. **确认认证配置完成** ```bash nlm doctor ``` ## 工作流程 ### Step 1: 选择风格模板（必须！） **从以下预置模板中选择**，不要自行编写： | 模板名称 | 适用场景 | 关键字 | |----------|----------|--------| | Modern Newspaper | 商业报告、经济媒体、智库研究 | 商业、专业、严肃 | | Sharp-edged Minimalism | 技术分享、产品介绍、架构设计 | 技术、极简、架构 | | Yellow × Black Editorial | 时尚杂志、创意提案 | 创意、时尚、设计 | | Black × Orange Creative | Agency 演示 | 创意、活力 | | Manga Style | 教育培训、趣味讲解 | 教育、趣味、漫画 | | Magazine Style | 女性向内容、生活方式 | 杂志、时尚、生活 | | Pink Street-style | 街头文化、潮牌 | 街头、潮流、年轻 | | Neo-Retro Dev | 开发者文档、技术博客 | 技术、开发者、复古 | | Royal Blue × Red | 艺术展示、创意项目 | 艺术、创意、水彩 | | Studio / Mockup / Premium | 产品发布、硬件展示 | 产品、硬件、高端 | | Sports / Athletic | 运动品牌、健身内容 | 运动、健身、能量 | **选择方法**： 1. 根据文档内容类型选择最合适的模板 2. 从 `references/templates.md` 中获取该模板的完整提示词 3. 替换占位符（如语言） ### Step 2: 创建笔记本并添加源 ```bash # 创建笔记本 nlm notebook create "演示文稿" # 添加源文档 nlm source add <notebook_id> --url "https://..." ``` ### Step 3: 用 Query 设置风格要求（重要！） 从模板获取提示词后，用 query 设置： ```bash nlm notebook query <notebook_id> "请用中文回答。我希望生成PPT演示文稿，风格要求：[从templates.md复制的完整模板内容]。输出语言为中文。" ``` ### Step 4: 生成幻灯片（只执行一次！） ```bash nlm slides create <notebook_id> --language zh --confirm # 记录返回的 Artifact ID ``` ### Step 5: 等待并下载 ```bash # 等待生成完成 nlm studio status <notebook_id> # 下载（使用 Artifact ID） nlm download slide-deck <notebook_id> --id <artifact_id> --format pptx ``` ### Step 6: 发送文件 ```bash # 复制到白名单目录 cp *.pptx ~/.openclaw/media/inbound/ # 发送 message --filePath ~/.openclaw/media/inbound/xxx.pptx ... ``` ## ⚠️ 关键注意事项 1. **必须使用预置模板**：从 `references/templates.md` 选择，不要自行编写 2. **slides create 只执行一次**：每次执行都会创建新版本 3. **记录 Artifact ID**：下载时使用 4. **文件大小**：建议 <15MB ## 模板参考 完整模板内容见 `references/templates.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cnvipstar/telcall-twilio (version 1.0.0)

--- name: telcall-twilio description: Make emergency phone calls via Twilio. Use when you need to call someone and play a voice message programmatically (e.g., server down alerts, security notifications). metadata: { "openclaw": { "requires": { "bins": ["curl", "jq"] }, }, } --- # TelCall Twilio - Emergency Phone Calls Make phone calls that speak a message to the recipient using Twilio's Voice API. ## Author - **Name:** Micheal Sun - **Email:** [REDACTED_SECRET] - **X:** https://x.com/pingfanbufan - **GitHub:** https://github.com/cnvipstar ## Features - Make phone calls via Twilio API - Text-to-speech message delivery (supports multiple languages) - Simple configuration - Easy integration with OpenClaw ## Prerequisites ### 1. Twilio Account 1. Visit https://www.twilio.com and sign up 2. During setup, choose: - **"With code"** (for full API access) - **"Voice"** channel 3. Bind a credit card (required for production use) ### 2. Purchase a Phone Number 1. Go to **Phone Numbers** → **Buy a Number** 2. Select **United States** (easiest, no address verification needed) 3. Filter by **Voice** capability 4. Purchase a local number (~$1/month) ### 3. Get Your Credentials From the Twilio Console homepage, copy: - **Account SID** (starts with `AC...`) - **Auth Token** (click to reveal) ### 4. Verify Your Phone Number (Trial Accounts) Trial accounts can only call verified numbers: 1. Go to **Phone Numbers** → **Verified Caller IDs** 2. Add your phone number 3. Complete verification via SMS or voice call **Note:** Upgrade your account ($20 minimum) to call any number without verification. ## Setup Run the setup script to configure your Twilio credentials: ```bash bash ~/.openclaw/workspace/telcall-twilio/scripts/setup.sh ``` You will be prompted to enter: - Account SID - Auth Token - Twilio phone number (e.g., `+15551234567`) - Your phone number (e.g., `+8613812345678`) ## Usage ### Basic Call ```bash bash ~/.openclaw/workspace/telcall-twilio/scripts/call.sh "Your message here" ``` ### With OpenClaw Simply tell your assistant: - "Call me: Server is down!" - "Emergency call: Security breach detected" The assistant will use this skill to make the call. ## Cost | Item | Price | |------|-------| | US Phone Number | ~$1/month | | Call to most countries | ~$0.02-0.15/min | | Text-to-speech | Free (TwiML) | **Estimated monthly cost:** $2-5 for occasional emergency calls. ## Troubleshooting ### "Number is unverified" Your account is in trial mode. Either: 1. Verify the destination number in Twilio Console 2. Upgrade your account ($20) to call any number ### Call doesn't go through 1. Check your Twilio number has **Voice** capability 2. Verify your Auth Token is correct 3. Ensure you have account balance ## Files ``` telcall-twilio/ ├── SKILL.md # This file ├── scripts/ │ ├── setup.sh # Configure credentials │ └── call.sh # Make a phone call └── config/ └── twilio.json # Credentials (created by setup.sh) ``` ## License MIT License - Feel free to use and modify.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: leooooooow/creator-video-cropper (version 1.0.0)

--- name: video-cropper description: Crop videos to focus on the most important area for social, ecommerce, ads, and storefront use without making the result look clumsy. Use when the frame includes wasted space, wrong composition, or unnecessary edges. --- # Video Cropper Crop the frame so attention stays on what matters. ## Problem it solves A video can be technically fine but compositionally wrong for the job: too much empty space, distracting edges, irrelevant background, or a subject that needs to fill the frame better. This skill helps tighten framing for platform use and better viewer focus. ## Use when - The subject is too small in frame - The edges contain distracting or unnecessary information - The user wants a tighter product, face, or demo crop - The same source needs cleaner framing for social or ecommerce placement ## Do not use when - The subject moves too much for static crop logic - Intelligent motion tracking is required - Resize or compression is the real problem, not framing ## Inputs - Source video file - Area to preserve: face, hands, product, subtitles, full object, etc. - Target use case or platform - Desired framing style: tight, balanced, or conservative ## Workflow 1. Identify what should remain in frame. 2. Remove wasted or distracting space. 3. Keep the crop useful for platform context and subject readability. 4. Export the tighter composition. 5. Flag when a static crop may fail because subject motion is too large. ## Output Return: 1. Chosen crop focus 2. Framing rationale 3. Output fit for the target use case 4. Risks if motion may leave the crop area ## Quality bar - Preserve the true subject, not just the geometric center - Avoid awkward amputations of hands, products, or subtitles - Prefer useful framing over maximum zoom-in - Warn when static crop is likely to break on motion-heavy footage ## Resource See `references/output-template.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: parkertoddbrooks/wip-readme-format (version 1.9.72)

--- name: wip-readme-format description: Reformat any repo's README to follow the WIP Computer standard. license: MIT interface: [cli, skill] metadata: display-name: "README Formatter" version: "1.0.0" homepage: "https://github.com/wipcomputer/wip-ai-devops-toolbox" author: "Parker Todd Brooks" category: repo-management capabilities: - readme-generation - readme-validation - section-staging requires: bins: [node] openclaw: requires: bins: [node] install: - id: node kind: node package: "@wipcomputer/wip-readme-format" bins: [wip-readme-format] label: "Install via npm" emoji: "📝" compatibility: Requires node. Node.js 18+. --- # README Formatter Reformats a repo's README to follow the WIP Computer standard. Agent-first, human-readable. ## Commands ``` wip-readme-format /path/to/repo # rewrite README.md wip-readme-format /path/to/repo --dry-run # preview without writing wip-readme-format /path/to/repo --check # validate, exit 0/1 ``` ## What happens 1. Detects all interfaces (CLI, Module, MCP, OC Plugin, Skill, CC Hook) 2. Reads package.json for name, description, repo URL 3. Reads SKILL.md for features 4. Generates the README following the standard: - WIP Computer header + interface badges - Title + tagline - "Teach Your AI" onboarding block - Features list - Interface coverage table (toolbox repos only) - More Info links - License block 5. Moves technical content to TECHNICAL.md (never deleted) ## The standard ``` [badges] # Tool Name Tagline. ## Teach Your AI to [verb] [onboarding prompt block] ## Features [feature list] ## Interface Coverage (toolbox only) [auto-generated table] ## More Info - Technical Documentation ... link - Universal Interface Spec ... link ## License [standard block] ``` ## Interfaces CLI, Skill


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clementgu/nvidia-agent-fleet (version 1.1.1)

--- name: nvidia-agent-fleet slug: nvidia-agent-fleet version: 1.1.0 description: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作 author: openclaw tags: [nvidia, multi-agent, dispatcher, llm, orchestration, parallel] --- # 🚀 NVIDIA Agent Fleet 多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。 ## 架构 ``` 任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行 ├── DeepSeek V3.2 🧠 ├── Qwen Coder 32B 💻 ├── Kimi K2 🇨🇳 ├── Llama 4 Maverick 🦙 └── ... 19 个 Agent ``` ## Agent 阵容 (19个) | Agent | 模型 | 专长 | |-------|------|------| | 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 | | 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 | | 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 | | 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 | | ⚡ mistral-small | Mistral Small 4 | 快速响应 | | 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 | | 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 | | 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL | | ⚙️ codestral | Codestral 22B | 代码补全、填空 | | 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 | | 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 | | 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 | | 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 | | 💨 gemma-3-4b | Gemma 3 4B | 超轻量 | | 🌏 yi-large | Yi Large | 中文优化 | | 🏛️ glm-5-1 | GLM 5.1 | 中文问答 | | 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 | | 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 | | 📐 nv-embed | NV-EmbedQA | 文本嵌入 | ## 使用 ### CLI ```bash # 自动调度最佳 Agent fleet "用Python写一个快速排序" # 分析任务会匹配哪些 Agent fleet analyze "解释一下量子纠缠" # 指定 Agent 执行 fleet agent qwen-coder-32b "实现二分查找" # 多 Agent 协同 fleet multi "如何看待人工智能的未来？" # 列出所有 Agent fleet list ``` ### ⚡ 并行执行（v1.1.0 新增） ```bash # 多 Agent 并行协同（同时调不同模型，互不等待） fleet --multi --parallel "对比茅台和五粮液的财务状况" # 指定 Agent 并行 fleet --multi --parallel --agent deepseek-v3-2,kimi-k2 "分析这个创业方案" # 仍支持原有串行模式 fleet --multi "分析市场趋势" ``` **并行 vs 串行 速度对比** ``` 串行: 任务A→任务B→任务C→任务D = 4个加起来 并行: 同时发4个请求 = 最慢那个决定总时间 加速比: x2~4 (取决于模型) ``` ### 自动超时保护 每个模型有独立超时时间，快的先出结果，慢的到点自动跳过： | 模型 | 超时 | 说明 | |------|------|------| | Llama 4 Maverick | 15s | 写作/报告类，极快 | | Qwen Coder 32B | 20s | 代码/数据处理 | | Kimi K2 | 25s | 中文分析 | | DeepSeek V3.2 | 35s | 深度推理（最慢） | | 其他 | 30s | 通用 | ### API Key 自动发现 支持多级检索，无需手动设置环境变量： ``` 1. $NVIDIA_API_KEY (环境变量) 2. ~/.zshrc (常用 shell 配置) 3. openclaw.json (OpenClaw 配置) ``` ### Python SDK ```python from dispatcher.fleet import dispatch, select_agents, multi_dispatch # 自动匹配 result = dispatch("实现一个Web服务器") print(result["content"]) # 多Agent协同（串行） results = multi_dispatch("哲学问题：意识是什么？") # 多Agent协同（并行执行）🚀 results = multi_dispatch("分析市场", parallel=True) for r in results: print(f"{r['emoji']} {r['name']}: {r['content'][:100]}...") ``` ## v1.1.0 变更 ### ✨ 新增 - **并行执行模式** (`--parallel` / `-p`): 多 Agent 同时调用，互不等待 - **逐模型超时保护**: 每个模型有独立超时，慢的不拖累快的 - **API Key 自动发现**: 环境变量 → .zshrc → openclaw.json 三级检索 - **线程安全**: ThreadPoolExecutor + Lock 确保日志不串 ### 🔧 优化 - 执行失败时优雅降级，已完成结果不受影响 - 超时模型明确标记，不阻塞整体流程


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yuyonghao-123/yuyonghao-agent-reliability (version 0.1.0)

# Agent Reliability Agent 可靠性框架，解决多步工作流中准确率递减问题。 ## 功能特性 ### 1. 错误率监控 - 实时错误率追踪 - 历史趋势分析 - 异常检测告警 - 错误分类统计 ### 2. 自动回退机制 - 失败检测触发 - 回退策略配置 - 状态恢复 - 优雅降级 ### 3. 置信度评估 - 单步置信度计算 - 累积置信度追踪 - 阈值告警 - 置信度可视化 ### 4. 多 Agent 投票共识 - 投票策略（简单多数/加权/一致） - 冲突解决 - 共识达成判定 ## 安装 ```bash npm install ``` ## 使用方法 ### ReliabilityMonitor ```javascript const { ReliabilityMonitor } = require('./src'); const monitor = new ReliabilityMonitor({ errorThreshold: 0.15, confidenceThreshold: 0.85, historyWindow: 100 }); // 记录执行结果 monitor.record({ stepId: 'step-1', success: true, confidence: 0.92, duration: 1500 }); // 获取可靠性评分 const score = monitor.getReliabilityScore(); console.log(score); // { overall: 0.87, byStep: {...}, trend: 'improving' } ``` ### FallbackManager ```javascript const { FallbackManager } = require('./src'); const fallback = new FallbackManager({ maxRetries: 3, backoffStrategy: 'exponential' }); const result = await fallback.execute(async () => { return await riskyOperation(); }, { fallback: async () => await safeOperation() }); ``` ### VotingConsensus ```javascript const { VotingConsensus } = require('./src'); const consensus = new VotingConsensus({ strategy: 'weighted-majority', minAgreement: 0.7 }); consensus.vote('agent-1', { decision: 'approve', confidence: 0.9, weight: 2 }); consensus.vote('agent-2', { decision: 'approve', confidence: 0.8, weight: 1 }); const result = consensus.resolve(); console.log(result); // { decision: 'approve', confidence: 0.87, agreement: 0.75 } ``` ## 测试 ```bash npm test ``` ## License MIT

path: package.json
content: {
  "name": "agent-reliability",
  "version": "0.1.0",
  "description": "Agent reliability framework for multi-step workflow accuracy management",
  "main": "src/index.js",
  "scripts": {
    "test": "node test/reliability.test.js"
  },
  "keywords": [
    "agent",
    "reliability",
    "monitoring",
    "fallback",
    "consensus",
    "confidence"
  ],
  "author": "",
  "license": "MIT",
  "engines": {
    "node": ">=14.0.0"
  }
}


path: src/index.js
content: /**
 * Agent Reliability Framework
 * Main entry point
 */

const ReliabilityMonitor = require('./reliability-monitor');
const FallbackManager = require('./fallback-manager');
const ConfidenceCalculator = require('./confidence-calculator');
const VotingConsensus = require('./voting-consensus');
const ReportGenerator = require('./report-generator');

module.exports = {
  ReliabilityMonitor,
  FallbackManager,
  ConfidenceCalculator,
  VotingConsensus,
  ReportGenerator,
  
  // Convenience factory functions
  createMonitor(options) {
    return new ReliabilityMonitor(options);
  },
  
  createFallback(options) {
    return new FallbackManager(options);
  },
  
  createConsensus(options) {
    return new VotingConsensus(options);
  },
  
  createReportGenerator(options) {
    return new ReportGenerator(options);
  }
};


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