OpenClaw/clawhub-security-signals huggingface dataset:
Skill: nutstrut/settlement-witness (version 0.0.11)

\--- name: settlement-witness description: Prove an agent actually completed its task. Deterministic PASS/FAIL verification with signed receipts before payment or settlement. \--- \# SettlementWitness Verify structured agent task outputs and return signed proof of outcome. \--- \## Core Principle Verification should be deterministic, minimal, and optional. Only structured inputs are evaluated. \--- \## What This Does SettlementWitness evaluates submitted task data and returns: \- PASS / FAIL / INDETERMINATE \- signed receipt (SAR) \- optional TrustScore update \--- \## Core Execution Loop 1\. Provide input: &#x20; - task\_id &#x20; - optional agent\_id &#x20; - spec (expected result) &#x20; - output (actual result) 2\. Submit for verification 3\. Receive: &#x20; - verdict &#x20; - receipt &#x20; - optional score update \--- \## Example settlement\_witness({ &#x20; task\_id: "task-001", &#x20; agent\_id: "0xYourWallet:agent", &#x20; spec: { expected: "report generated" }, &#x20; output: { expected: "report generated" } }) \--- \## Result Structure { &#x20; "verdict": "PASS | FAIL | INDETERMINATE", &#x20; "receipt\_id": "sha256:...", &#x20; "signature": "...", &#x20; "confidence": 1.0 } \--- \## Endpoints https://defaultverifier.com/settlement-witness Public keys: https://defaultverifier.com/.well-known/sar-keys.json \--- \## Agent Identity (Optional) Format: 0xWalletAddress:agent-name Used for: \- attribution \- reputation scoring \--- \## Data Handling \- only submit structured evaluation data \- never include secrets or private data \- submit minimal required information \--- \## Verification Model \- stateless \- deterministic \- independently verifiable \- signature-based (Ed25519) \--- \## What This Is Not \- not a payment system \- not data storage \- not enforcement layer \--- \## What This Is \- deterministic verifier \- receipt generator \- proof system \- reputation input layer \--- \## Outcome Agents can: \- prove task completion \- generate verifiable records \- build reputation over time \--- \## Keywords verification, trust, receipts, ai-agents, validation, reputation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chen-feng123/ai-cardiac-rehab (version 1.0.2)

--- name: ai-cardiac-rehab description: "AI 心脏康复管理系统 — 基于 ACC/AHA 指南的安全增强型康复管理 Web 应用。症状预警、风险分层、个体化运动处方、药物依从性提醒。" --- # AI 心脏康复管理系统 (AI Cardiac Rehab) 基于 ACC/AHA 指南的心脏康复安全管理系统。病人安全优先设计。 ## 功能 - 患者档案管理（心脏病诊断、EF值、合并症、药物） - AI 安全决策引擎（绝对禁忌阻断、风险分层） - 风险三档：高（EF<30）/ 中（EF 30-50）/ 低（EF>50） - 基于 β 阻滞剂使用的靶心率动态调整 - 症状红色预警（胸痛/呼吸困难/晕厥）自动阻断 - 每日记录 + 7天心率趋势图 - 运动处方生成 + 药物依从性提醒 ## 快速开始 ```bash pip install flask python app.py ``` 浏览器打开 `http://localhost:5000`，注册后使用。 ## 安全特性 1. 绝对禁忌条件：EF<30%、不稳定心绞痛、急性心梗1月内、高危症状 → 自动阻断运动，强制就医 2. 所有临床输入范围校验（心率/血压/年龄/EF） 3. 数据存储在本地 SQLite，不上传云端 4. 前端验证+后端校验双重保障 5. 所有建议基于 ACC/AHA 临床指南 ## 合规声明 本工具不提供医疗诊断，所有康复方案必须经心脏科医生审核后执行。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: codekungfu/truecaller (version 0.1.0)

--- name: truecaller description: 注册“Truecaller”技能；用于工具类产品公开页面、功能说明、下载入口与帮助信息整理。 homepage: `https://www.truecaller.com/` metadata: {"clawdbot":{"emoji":"🧰"}} --- # Truecaller 用途与边界 - 面向公开官网、下载页、帮助中心、功能说明页与公告页的信息整理 - 不提供设备控制、权限绕过、非公开安装包、系统破解或风控规避能力 - 仅处理公开可见的功能说明、版本入口、帮助信息与使用边界 关键入口 - 主页：https://www.truecaller.com/ - 下载与版本：官网中的下载页、版本说明、平台支持入口 - 帮助与功能：帮助中心、FAQ、功能介绍、隐私或权限说明入口 常见任务 - 功能模块、版本差异、平台支持与下载入口整理 - 权限说明、隐私说明、常见问题与故障排查入口汇总 - 更新日志、公告页与帮助目录提取 数据字段 - 平台支持、版本入口、功能模块、链接 - 权限说明、隐私政策、帮助页、FAQ 链接 - 更新日志、公告页、下载地址、系统要求 自动化要点 - 工具类产品经常按系统平台区分，整理时明确 Android、iOS、Windows、macOS 差异 - 优先以官方权限说明和隐私政策为准，避免过度推断 - 不处理设备调试、系统破解、越权访问或私有数据导出 示例流程 - 版本整理：访问 Truecaller 下载页 → 提取平台与版本入口 → 输出清单 - 功能摘要：进入功能说明页 → 提取核心能力与限制 → 输出摘要 - 帮助汇总：访问帮助中心与隐私页 → 输出常见问题与权限说明 合规提示 - 遵守平台规则、版权要求与账号安全要求 - 只整理公开信息，不输出敏感数据或非公开内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: callmedas69/basecred (version 1.0.0)

--- name: basecred description: Fetch onchain reputation profiles via BaseCred SDK (Ethos, Talent Protocol, Farcaster/Neynar). Use when the user wants to check wallet reputation, builder score, creator score, Ethos credibility, or Farcaster account quality for any 0x address. Supports multi-source unified profiles with level derivation and recency tracking. --- # BaseCred — Onchain Reputation Queries ## Prerequisites 1. **Package installed** in workspace: `npm i basecred-sdk` 2. **API keys** in workspace `.env`: ``` TALENT_PROTOCOL_API_KEY=<key> NEYNAR_API_KEY=<key> # optional — enables Farcaster scoring ``` Ethos Network requires no key. ## Quick workflow 1. Run the query script from the workspace: ```bash node /path/to/skills/basecred/scripts/query.mjs <0x-address> ``` The script auto-locates `node_modules/basecred-sdk` and `.env` by walking up from cwd. Run it with cwd set to the workspace. 2. Parse the JSON output and present results to the user. Use the level tables in `references/output-schema.md` to translate raw scores into human-readable levels. ## Presenting results Summarize the three facets clearly: - **Ethos** — score + credibility level + review sentiment + vouches. Flag `hasNegativeReviews` if true. - **Talent Protocol** — builder score/level + creator score/level. Note verified status. - **Farcaster** — quality score (0–1) and whether it passes threshold. - **Recency** — `recent` / `stale` / `dormant`. Mention if stale or dormant as a caveat. Highlight actionable signals: e.g. zero vouches on Ethos is an easy win, or a dormant Talent score that needs activity. ## Reference - **Output schema + all level tables:** `references/output-schema.md` — read when you need to map scores → levels or explain the shape of a response.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: omaression/atomic-memory-manager (version 1.0.0-alpha)

--- name: memory-management description: Recall and write workspace memory for OpenClaw agents using MEMORY.md, memory/YYYY-MM-DD.md, memory_search, and memory_get. Use when the user asks about prior work, decisions, dates, people, preferences, todos, or says to remember something; also use when you need to store durable notes or explain how OpenClaw memory works. --- # Memory Management Retrieve first. Write only what should persist. ## Core model - `MEMORY.md` — curated long-term memory - `memory/YYYY-MM-DD.md` — daily notes and running context - `memory_search` — find likely snippets - `memory_get` — read exact lines needed ## Retrieval When user asks about prior work, decisions, dates, people, preferences, or todos: 1. `memory_search` with a tight, specific query. 2. `memory_get` for the needed lines only. 3. Answer from retrieved text, not guesswork. 4. If results are weak or empty, say you checked. Good queries: `omar internship goal fall 2026`, `notion workspace page id` Bad queries: whole paragraphs, vague terms like `memory` ## Writing Write when user says "remember this," states a durable preference, or makes a decision that matters later. | Destination | What goes there | |---|---| | `MEMORY.md` | Standing preferences, durable facts, recurring workflows, important long-term decisions | | `memory/YYYY-MM-DD.md` | Day-to-day notes, progress, one-off events, session context | Rules: - smallest useful note - concrete facts over commentary - no secrets unless asked - do not promise memory unless written ## When not to use Skip retrieval when the answer is in the current message or task is current-turn only. Skip writing when the fact is trivial, belongs in project docs, or is temporary scratch. ## Answering rules - Cite file path/line when useful - Separate memory facts from inference - Flag stale memory - If unavailable, say so


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: musofan/musofan-douyin-download (version 1.0.0)

--- name: douyin-download description: "Download Douyin (Chinese TikTok) videos to local MP4 files. Use when the user shares a Douyin video URL or asks to download or save a Douyin/TikTok video. Works with exported browser cookies and a local HTTP proxy. Triggers: download Douyin video, save TikTok, 抖音视频下载." version: "1.0.0" author: musofan homepage: "https://github.com/musofan/douyin-download" repository: "https://github.com/musofan/douyin-download" keywords: ["douyin", "tiktok", "video", "download", "playwright", "cookies"] --- # Douyin Download | 抖音视频下载 > Save any Douyin video to MP4 — no watermarks, no limits. ## 特性 | Features - 🎬 **高清下载** — 1080p MP4，无水印 / No watermark, full 1080p - 🤖 **全自动** — 只需发链接给我，剩下的我来 / Just send the link, I'll handle the rest - 🔒 **隐私安全** — Cookie 来自你自己的浏览器，不经过第三方 / Your cookies stay local, no third-party involved - ⚡ **即下即用** — 拦截真实视频直链，直接下载 / Intercepts real video stream, downloads directly ## 使用前提 | Prerequisites | 项目 | 说明 | |------|------| | Chrome 扩展 | 安装 [Get cookies.txt](https://chrome.google.com/webstore) (可选，如果无法下载再装) | | 导出 Cookie | 访问 douyin.com → 点扩展 → Export → 保存到 `sessions/cookies/cookies.txt` | | 代理 | Clash Verge 或任意 HTTP 代理，运行在 `127.0.0.1:7897` | | Python 包 | `playwright`, `requests` | > 注意：Cookie 只需要导出一次，除非过期。文件保存在你自己的电脑上，不会上传。 ## 使用方法 | Usage 发给我任意抖音链接即可： ``` https://www.douyin.com/video/7627894054088461611 ``` 我会自动下载并保存到 `sessions/video-download/` 目录。 或者用命令行指定路径： ```bash python skills/douyin-download/scripts/download.py <video_url> [output_path] [cookie_file] ``` ## 工作原理 | How It Works ``` 1. Playwright 启动无头浏览器 → 加载你的 cookies 2. 访问抖音页面 → Douyin 以为是真实浏览器 3. 拦截 douyinvod.com 视频响应 → 拿到真实 MP4 直链 4. 直接下载 MP4 文件 → 不经过任何第三方服务器 ``` **为什么不用 yt-dlp？** 因为抖音有额外的浏览器指纹验证，导出的 cookie 缺少签名信息，yt-dlp 会报 "Fresh cookies needed"。Playwright 用真实浏览器环境解决这个限制。 ## 输出 | Output 下载完成后，你会看到： - 文件路径：`sessions/video-download/video.mp4` - 文件大小、时长、分辨率 - 下载进度条 ## 注意 | Notes - 视频直链有过期签名，请尽快下载 / Video URLs expire quickly, download promptly - 如果下载失败，尝试重新导出一次新鲜 cookies / If it fails, re-export fresh cookies from douyin.com - 支持抖音和 TikTok 链接 / Supports both Douyin and TikTok URLs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 403914291/remote-install (version 1.0.0)

--- name: remote-install description: Remote software installation via automated installer detection and GUI automation. Use when: user needs to install software packages (.exe/.msi) on Windows machines, automate Office/Adobe/Chrome installations, or handle unattended software deployment. NOT for: Linux/macOS installations, package manager installs (apt/yum/brew), or containerized deployments. --- # Remote Install Skill - 远程软件安装 通过自动化检测和 GUI 控制实现 Windows 远程软件安装。 ## 何时使用 ✅ **使用场景：** - "帮我安装桌面上的软件" - "远程安装 Office" - "自动安装下载目录的安装包" - "静默安装 MSI 包" - "检测并安装用户电脑上的软件" ❌ **不使用：** - Linux/macOS 安装 - 包管理器安装 (apt/yum/brew) - 容器化部署 ## 工作流程 1. **检测安装包** - 扫描桌面/下载目录查找 .exe/.msi/.zip/.rar 2. **识别软件类型** - Office/Adobe/Chrome/7-Zip 等 3. **选择安装方式**： - `.msi` → msiexec 静默安装 - `.exe` → pywinauto GUI 自动化 - `.zip/.rar` → 解压后安装 4. **执行安装** - 处理弹窗、点击"下一步"按钮 5. **返回结果** - 成功/失败报告 ## 调用示例 **"安装桌面上的所有软件"** → 自动检测桌面所有安装包并依次安装 **"只安装 Office"** → 过滤并安装 Office 相关安装包（自动判断 32/64 位） **"检查下载目录有什么安装包"** → 列出安装包但不安装 **"安装 C:\Users\桌面\setup.exe"** → 安装指定路径的安装包 ## 输出格式 ```json { "success": true, "message": "远程安装流程完成", "summary": { "total_packages": 2, "successful_installs": 1, "failed_installs": 1, "results": [ { "package": "C:\\Users\\...\\Office.exe", "success": true, "message": "EXE 包安装成功" } ] } } ``` ## 配置 编辑 `config.json` 自定义： ```json { "installation": { "timeout_seconds": 300, "retry_attempts": 3, "log_level": "INFO" }, "ui_elements": { "next_button_texts": ["下一步", "Next", "继续", "Install"], "finish_button_texts": ["完成", "Finish", "Close"] } } ``` ## 依赖 - Python 3.8+ - pyautogui, pywinauto, Pillow - Windows 系统 ## 安装 ```bash pip install pyautogui pywinauto Pillow ``` ## 使用说明 脚本会自动： 1. 检测系统架构（32/64 位） 2. 检测已安装 Office 版本（如有） 3. 根据内存大小推荐架构 4. 自动点击安装窗口的"下一步"按钮 5. 记录完整日志到 `installer.log` ## 安全说明 - 仅在用户授权下运行 - 所有操作记录日志 - 可配置超时和重试 - 不收集或外传用户数据


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ghwyever/02-script-generator (version 1.0.4)

# 短剧剧本生成技能 ## 功能介绍 根据IP解析结果自动生成符合抖音/番茄平台规范的短剧剧本，单集时长 90–120 秒，节奏紧凑、冲突密集。 ## 输入参数 - ip_info：结构化IP信息（必填） - episode_count：生成集数，默认 1 - duration：单集时长，默认 90 秒，范围 90–120 秒 ## 输出结果 - scenes：分场景剧本列表 - total_duration：单集总时长 ## 使用场景 - 小说IP自动转短剧剧本 - 批量合规剧本生成 - 规模化短剧内容生产 ## 技术说明 支持通用大模型接入，需配置 API_KEY、API_BASE、MODEL_NAME。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: b-gutman/pipeworx-numbersapi (version 1.0.0)

--- name: pipeworx-numbersapi description: NumbersAPI MCP — wraps numbersapi.com (free, no auth) version: 1.0.0 metadata: openclaw: homepage: https://pipeworx.io/packs/numbersapi --- # pipeworx-numbersapi NumbersAPI MCP — wraps numbersapi.com (free, no auth). Free, no API key. Part of [Pipeworx](https://pipeworx.io). ## Tools - `number_fact` - `date_fact` - `math_fact` - `random_fact` ## Connect ```json { "mcpServers": { "pipeworx-numbersapi": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/numbersapi/mcp"] } } } ``` More at [pipeworx.io/packs/numbersapi](https://pipeworx.io/packs/numbersapi)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: volcengine-skills/byted-bytehouse-slow-query (version 1.0.0)

--- name: byted-bytehouse-slow-query description: ByteHouse慢查询分析和性能优化工具，用于识别和分析慢查询、查询性能优化建议、查看查询执行计划、分析查询历史趋势。当用户需要识别和分析ByteHouse数据库中的慢查询、查询性能优化建议、查看查询执行计划、分析查询历史趋势时，使用此Skill。 --- # ByteHouse 慢查询分析 Skill ## 🔵 ByteHouse 品牌标识 > 「ByteHouse」—— 火山引擎云原生数据仓库，极速、稳定、安全、易用 > > 本Skill基于ByteHouse MCP Server，提供完整的慢查询分析和性能优化能力 --- ## 描述 ByteHouse慢查询分析和性能优化工具。 **当以下情况时使用此 Skill**: (1) 需要识别和分析慢查询 (2) 需要查询性能优化建议 (3) 需要查看查询执行计划 (4) 需要分析查询历史趋势 (5) 用户提到"慢查询"、"查询优化"、"性能分析"、"执行计划" ## 前置条件 - Python 3.8+ - uv (已安装在 `/root/.local/bin/uv`) - **ByteHouse MCP Server Skill** - 本skill依赖 `bytehouse-mcp` skill提供的ByteHouse访问能力 ## 依赖关系 本skill依赖 `bytehouse-mcp` skill，使用其提供的MCP Server访问ByteHouse。 确保 `bytehouse-mcp` skill已正确配置并可以正常使用。 ## 📁 文件说明 - **SKILL.md** - 本文件，技能主文档 - **slow_query_analyzer.py** - 慢查询分析主程序 - **README.md** - 快速入门指南 ## 配置信息 ### ByteHouse连接配置 本skill复用 `bytehouse-mcp` skill的配置。请确保已在 `bytehouse-mcp` skill中配置好： ```bash export BYTEHOUSE_HOST="<ByteHouse-host>" export BYTEHOUSE_PORT="<ByteHouse-port>" export BYTEHOUSE_USER="<ByteHouse-user>" export BYTEHOUSE_PASSWORD="<ByteHouse-password>" export BYTEHOUSE_SECURE="true" export BYTEHOUSE_VERIFY="true" ``` ## 🎯 功能特性 ### 1. 慢查询识别 - 从query_log表获取慢查询 - 按执行时间排序 - 识别Top N慢查询 - 分析慢查询模式 ### 2. 查询性能分析 - 查询执行时间分布 - 查询类型统计 - 查询频率分析 - 性能趋势分析 ### 3. 执行计划分析 - 获取查询执行计划 - 分析执行计划节点 - 识别性能瓶颈 - 提供优化建议 ### 4. 优化建议生成 - 索引优化建议 - 查询重写建议 - 表引擎建议 - 配置参数调优 ## 🚀 快速开始 ### 方法1: 运行慢查询分析 ```bash cd /root/.openclaw/workspace/skills/bytehouse-slow-query # 先设置环境变量（复用bytehouse-mcp的配置） export BYTEHOUSE_HOST="<ByteHouse-host>" export BYTEHOUSE_PORT="<ByteHouse-port>" export BYTEHOUSE_USER="<ByteHouse-user>" export BYTEHOUSE_PASSWORD="<ByteHouse-password>" export BYTEHOUSE_SECURE="true" export BYTEHOUSE_VERIFY="true" # 运行慢查询分析 uv run slow_query_analyzer.py ``` **分析内容包括：** - Top 20慢查询 - 查询性能统计 - 执行时间分布 - 优化建议生成 **输出文件（保存在 `output/` 目录）：** 1. **`slow_queries_{timestamp}.json`** - 慢查询列表 2. **`query_stats_{timestamp}.json`** - 查询统计报告 3. **`optimization_suggestions_{timestamp}.json`** - 优化建议 ## 💻 慢查询分析维度 ### 时间维度分析 - **按小时**: 每小时慢查询数量 - **按天**: 每天慢查询趋势 - **按周**: 每周慢查询模式 - **按月**: 每月慢查询统计 ### 查询类型分析 - **SELECT查询**: 查询语句分析 - **INSERT查询**: 写入性能分析 - **UPDATE查询**: 更新性能分析 - **DELETE查询**: 删除性能分析 - **DDL查询**: 建表/改表性能分析 ### 性能指标 - **平均执行时间**: 所有查询平均耗时 - **P50执行时间**: 中位数执行时间 - **P95执行时间**: 95分位执行时间 - **P99执行时间**: 99分位执行时间 - **最大执行时间**: 最慢查询耗时 --- ## 📊 慢查询报告示例 ### 慢查询列表 ```json { "analysis_time": "2026-03-12T21:00:00", "total_queries": 10000, "slow_queries": 150, "top_slow_queries": [ { "query_id": "query-12345", "query_text": "SELECT * FROM large_table WHERE ...", "duration_ms": 15000, "start_time": "2026-03-12T20:55:00", "read_rows": 1000000, "read_bytes": 104857600 } ] } ``` --- ## 📚 更多信息 详细使用说明请参考 [bytehouse-mcp skill](../bytehouse-mcp/SKILL.md) --- *最后更新: 2026-03-12*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lj22503/expression-layer (version 1.0.0)

--- name: expression-layer version: 1.0.0 description: "［何时使用］当需要内容生成、格式转换或多形式输出时触发。支持直接输入问题/素材/链接，无需前置思考层。统一路由至 ljg-skills 及发布工具。" author: 燃冰 & ant created: 2026-04-24 skill_type: 通用🟡 allowed-tools: [Bash, Read, Write, Exec, WebSearch] related_skills: [investor-education-workflow, investment-workflow, investment-advisory-workflow, ljg-skills] tags: [内容生成, 格式转换, 技能编排, 路由, 多形式输出] --- # expression-layer: 表达层 🎨 ## 📋 功能描述 帮助用户**统一调度内容生成与可视化输出**。不依赖前置思考层，直接接收问题/素材/链接，按意图路由至对应 skill，支持单步/串联/并行/发布编排。 **适用场景：** - 直接问概念/问题 → 口语化解释/深度文章 - 提供素材/草稿 → 写作 + 人性化 + 多形式输出 - 论文/单词/城市 → 解读 + 可视化卡片 - 完整文章 → 公众号发布 **边界条件：** - 不负责深度分析/数据查询（由上游工作流或思考层提供） - 输出格式依赖上游指定或意图自动识别 - 新增 skill 需更新路由矩阵 --- ## 🔄 路由编排矩阵 | 输入类型 | 触发意图 | 编排路径 | 输出形式 | 典型场景 | |---------|---------|---------|---------|---------| | 专业概念/问题 | `plain` | `ljg-plain` | 大白话文本（≤200字） | “什么是PE？说人话” | | 观点/素材/草稿 | `writes` | `ljg-writes` → `humanizer-zh` | 深度文章（1000-1500字） | “把这段写成公众号文章” | | 任何文本/数据 | `card` | `ljg-card`（-l/-i/-c/-w/-b） | PNG 卡片 | “做个信息图/漫画/大字” | | 文本/大纲 | `present` | `ljg-present` | HTML 高桥流 | “做成演讲PPT” | | 论文链接/PDF | `paper_flow` | `ljg-paper` → `ljg-card` | 解读Markdown + PNG | “读论文并做漫画卡片” | | 英文单词 | `word_flow` | `ljg-word` → `ljg-card` | 解析Markdown + PNG | “解词并做信息图” | | 城市/主题 | `travel` | `ljg-travel` | 研究报告 + PNG卡片 | “做西安旅行功课” | | 完整文章/解读 | `wechat` | `wechat-publisher` | 公众号推文（HTML+封面） | “发到公众号” | **编排模式**： - `单步`：直接路由到 1 个 skill - `串联`：A 输出 → B 输入（如 `ljg-paper → ljg-card`） - `并行`：同时生成多个形式（如 大白话 + 卡片 + 文章） - `发布`：内容 → `wechat-publisher` → 公众号 --- ## ⚠️ 常见错误 **错误 1：强行附加思考层** ``` 问题： • 用户只问“什么是定投”，却先跑降秩/追本分析 • 输出冗长，偏离直接表达需求 解决： ✓ 表达层是独立入口，不依赖思考层 ✓ 直接按意图路由到 ljg-plain / ljg-writes ✓ 如需深度分析，由上游工作流调用，非表达层职责 ``` **错误 2：忽略输出格式指定** ``` 问题： • 只给内容，不指定输出形式 • 路由歧义（该出文章还是卡片？） 解决： ✓ 优先按关键词自动识别意图（如“说人话”→plain，“做卡片”→card） ✓ 歧义时主动询问：“需要大白话解释、深度文章，还是可视化卡片？” ✓ 支持并行输出：plain + card + writes ``` **错误 3：新增 skill 未更新路由** ``` 问题： • 安装了新 skill，但路由矩阵未更新 • 表达层无法调度新能力 解决： ✓ 新增 skill 后，同步更新 SKILL.md 路由矩阵 ✓ 在 references/orchestration-matrix.md 维护完整映射 ✓ 提交版本升级（patch version） ``` --- ## 🧪 使用示例 **输入：** ``` 什么是定投？说人话，顺便做个卡片。 ``` **预期输出：** - 识别意图：`multi` (plain + card) - 编排：并行执行 `ljg-plain` + `ljg-card -i` - 输出：大白话文本 + PNG 信息图 **输入：** ``` 读这篇论文 https://arxiv.org/abs/xxx，做成漫画卡片。 ``` **预期输出：** - 识别意图：`paper_flow` - 编排：串联执行 `ljg-paper` → `ljg-card -c` - 输出：论文解读 Markdown + 漫画风格 PNG **输入：** ``` 把刚才的消费分析写成公众号文章发出去。 ``` **预期输出：** - 识别意图：`wechat` - 编排：`ljg-writes` → `humanizer-zh` → `wechat-publisher` - 输出：公众号草稿（已排版+封面+合规检查） --- ## 🔧 故障排查 | 问题 | 检查项 | |------|--------| | 不触发 | description 是否包含触发词？用户输入是否匹配意图？ | | 路由错误 | 意图识别是否准确？是否歧义未询问？ | | 输出为空 | 上游 skill 是否安装？（ljg-plain/ljg-card/wechat-publisher） | | 新增 skill 不调用 | 路由矩阵是否更新？references 是否同步？ | | 格式错误 | 输出格式是否指定？并行输出是否超出上下文限制？ | --- ## 🔗 相关资源 - 路由编排矩阵：`references/orchestration-matrix.md` - 投教工作流编排分析：`references/ie-orchestration.md` - 输出模板：`templates/output-template.md` - 标准参考：`docs/SKILL-STANDARD-v3.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: 2719040953/process-output (version 1.0.0)

--- name: process-output description: "DEFAULT OUTPUT MODE: Always emit machine-parseable `openclaw-process` fenced JSON blocks in your assistant reply so a custom web client can render a live progress panel. Use when: any user message. Skip ONLY when the user explicitly requests no intermediate process (e.g. '只给最终答案'). Keep it lightweight for simple Q&A." --- # Process Output (intermediate progress) This skill defines the **default reply format**: you MUST emit structured progress events in the assistant text stream using fenced JSON blocks, even if the user does not mention the protocol. ## Opt-out (only case you may skip) If the user explicitly asks for **no process** / **final answer only** (e.g. “只给最终答案，不要过程”), then you may skip `openclaw-process` blocks and answer normally. ## Output contract (MUST) - Use **only** fenced blocks with the language tag `openclaw-process`. - Each block MUST contain **one JSON object**. - Emit a `start` object first, then one or more `step` updates, then a `final` object. - Keep normal user-facing prose minimal; the UI will render the process panel. ## Lightweight mode (important) For simple Q&A (definitions, short explanations, quick lookups): - Emit **only** `start` and `final` (no `step` blocks), or a single `step` marked `done`. - Keep arrays short (0–3 items) and keep `notes` brief. **Greetings** (e.g. “你好”, “hi”): still emit **start + final** in lightweight mode. Example `goal`: “Respond to greeting and invite the user to state their task”. Example `final.summary`: one short line mirroring your friendly reply intent. ### 1) Start (emit once) ```openclaw-process { "type": "start", "goal": "string", "context": ["string"], "assumptions": ["string"], "inputs": ["string"], "outputs": ["string"] } ``` ### 2) Step updates (emit many, update status/progress) Rules: - `id` MUST be stable across updates for the same step (e.g. `"1"`, `"2"`). - `status` MUST be one of: `"pending" | "running" | "done" | "error"`. - `progress` MUST be an integer 0–100. - If `status === "error"`, include `error`. ```openclaw-process { "type": "step", "id": "1", "title": "string", "status": "running", "progress": 35, "notes": ["string"] } ``` ```openclaw-process { "type": "step", "id": "1", "title": "string", "status": "done", "progress": 100, "notes": ["string"] } ``` ### 3) Final (emit once) ```openclaw-process { "type": "final", "summary": ["string"], "artifacts": ["string"], "next": ["string"] } ``` ## Tool usage policy (important) - Do NOT call tools unless the user explicitly asks you to execute something (write files, run commands, etc.). - Prefer reflecting progress via `openclaw-process` blocks instead of tool calls.


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