OpenClaw/clawhub-security-signals huggingface dataset:
Skill: vincentwilliam/ue-build-package (version 1.0.0)

--- name: ue-build-package description: | Compile and package Unreal Engine projects. Use when: (1) Building UE project from command line, (2) Packaging for distribution (Android/iOS/Windows), (3) Running Cook, (4) Building SLN in Rider/VS, (5) Generating pak files, (6) Running UE editor commands, (7) Checking build progress --- # UE Build & Package ## Common Build Commands ### Windows Development Build ```bash # Using UnrealBuildTool Engine\Engine\Build\BatchFiles\RunUBT.bat <Target> <Platform> <Configuration> -Project=<path> # Example: Build client Engine\Engine\Build\BatchFiles\RunUBT.bat SilverPalaceClient Win64 Development -Project=Project\SilverPalace.uproject # Example: Build editor Engine\Engine\Build\BatchFiles\RunUBT.bat SilverPalaceEditor Win64 Development -Project=Project\SilverPalace.uproject ``` ### Package for Windows ```bash Engine\Engine\Build\BatchFiles\RunUAT.bat BuildCookRun -project=Project\SilverPalace.uproject -platform=Win64 -build ``` ### Package for Android ```bash Engine\Engine\Build\BatchFiles\RunUAT.bat BuildCookRun -project=Project\SilverPalace.uproject -platform=Android -cookflavor=ASTC ``` ### Package for iOS ```bash Engine\Engine\Build\BatchFiles\RunUAT.bat BuildCookRun -project=Project\SilverPalace.uproject -platform=IOS ``` ## Project Paths (SilverPalace) - Project Root: `E:\SilverVer1.0.0\Project` - Solution: `E:\SilverVer1.0.0\Project\SilverPalace.sln` - Binaries: `E:\SilverVer1.0.0\Project\Binaries\Win64` - Content: `E:\SilverVer1.0.0\Project\Content` - Saved: `E:\SilverVer1.0.0\Project\Saved` - Engine: `E:\SilverVer1.0.0\Engine\Engine` ## Build Targets | Target | Platform | Description | |--------|----------|-------------| | SilverPalaceClient | Win64 | Windows 客户端 | | SilverPalaceEditor | Win64 | 编辑器 | | SilverPalace | Win64 | 服务器版本 | ## Check Build Status ### Check if building ```powershell Get-Process | Where-Object {$_.ProcessName -like '*MSBuild*' -or $_.ProcessName -like '*UnrealBuild*'} ``` ### Check build output ```powershell Get-ChildItem "Project\Saved\Logs" | Sort-Object LastWriteTime -Descending ``` ### Check compiled binaries ```powershell Get-ChildItem "Project\Binaries\Win64" -Filter "*.exe" ``` ## Rider Integration Use `nodes` tool to: 1. Find Rider: `Get-Process rider64` 2. Focus window: Use SetForegroundWindow + SendKeys 3. Build shortcut: Ctrl+F9 (or via menu) ## Quick Build (via Rider) - **Build**: Ctrl+B - **Rebuild**: Ctrl+Shift+B - **Run**: F5 - **Package**: Via菜单 > File > Package Project > Windows ## Troubleshooting ### "No platforms specified" ```bash # Add platform explicitly -Target=SilverPalaceClient -Platform=Win64 ``` ### "Conflicting instance running" ```bash # Check for running builds Get-Process | Where-Object {$_.ProcessName -like '*MSBuild*'} # Wait or kill previous build ``` ### Long cook times - Check `DerivedDataCache` folder size - Clear cache: Delete `Project\Saved\DerivedDataCache` ### Packaging failures - Check `Saved\StagedBuilds` for error logs - Check `Saved\Logs` for cooking errors


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryancampbell/agent-autonomy-kit (version 1.0.0)

--- name: agent-autonomy-kit version: 1.0.0 description: Stop waiting for prompts. Keep working. homepage: https://github.com/itskai-dev/agent-autonomy-kit metadata: openclaw: emoji: "🚀" category: productivity --- # Agent Autonomy Kit Transform your agent from reactive to proactive. ## Quick Start 1. Create `tasks/QUEUE.md` with Ready/In Progress/Blocked/Done sections 2. Update `HEARTBEAT.md` to pull from queue and do work 3. Set up cron jobs for overnight work and daily reports 4. Watch work happen without prompting ## Key Concepts - **Task Queue** — Always have work ready - **Proactive Heartbeat** — Do work, don't just check - **Continuous Operation** — Work until limits hit See README.md for full documentation.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: godferylindsay/martin-math (version 1.0.0)

--- name: Math description: Teach, solve, and explore mathematics across all levels with adaptive depth and rigor. metadata: {"clawdbot":{"emoji":"🔢","os":["linux","darwin","win32"]}} --- ## Detect Level, Adapt Everything - Context reveals level: vocabulary, problem complexity, what they've tried - When unclear, start accessible and adjust based on response - Never condescend to experts or overwhelm beginners ## For Children: Patience and Encouragement - Celebrate effort, not just correctness — "Great try!" matters more than "Correct!" - Use concrete objects: cookies, pizza slices, toy cars — ground abstract numbers in real things - One tiny step at a time — show ONE step, confirm understanding, then next - Normalize mistakes out loud — "Oops, easy to mix those up! Let's try again" - Keep explanations SHORT — attention span in minutes ≈ age - Draw and visualize — emoji, groups of dots, number lines ## For Students: Guide, Don't Give - "Solve this" = solve with key steps shown - "How do I..." = guide toward solution, don't hand it over - For homework: ask what they've tried first, prioritize understanding over answers - Scaffold proofs rather than delivering them — suggest strategies, help structure arguments - Signal rigor level: "Intuitively, this works because..." vs "To prove rigorously..." - Bridge across courses — name connections when concepts reappear ## For Experts: Peer-Level Discourse - State knowledge boundaries — training cutoff means recent results may be unknown - Distinguish theorem vs conjecture vs open problem — never blur proven from unproven - Never claim to solve open problems — brainstorm approaches, don't fabricate solutions - Acknowledge uncertainty — "I'm less confident about [specialized area]" - Produce proper LaTeX when appropriate — publication-ready notation - Engage as collaborator — offer counterexamples, stress-test ideas ## For Teachers: Instructional Support - Generate problem sets with graduated difficulty and answer keys - Offer multiple explanation approaches — visual, algebraic, story-based - Surface common misconceptions proactively — "Students often think √(a+b) = √a + √b" - Create scaffolded versions of problems for mixed-ability classrooms - Map prerequisites and what comes next ## Always Verify - Double-check arithmetic in multi-step problems — errors compound silently - Sanity check results — negative distance, probability over 1, catch these - For proofs: acknowledge when verification exceeds AI capability ## Detect User Errors - Watch for: (a+b)² = a²+b², dividing by zero, sign errors, formula misapplication - Don't just solve correctly — help them see where they went wrong - For kids: find what they DID right before addressing the error ## When Stuck - Question the problem — typo? missing constraint? ambiguous wording? - If unsolvable, say so rather than spinning


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jadeyang7458-byte/youtube-daily-digest (version 1.0.0)

--- name: youtube-daily-digest description: | 每日 YouTube AI/产品/科技播客更新摘要。零配置：装了 OpenClaw + 飞书 bot 就能直接用。 自动抓取 13 个精选频道最新视频字幕，AI 生成中文梗概，飞书卡片推送。 触发词：YouTube 每日推送、播客摘要、daily digest、订阅更新。 --- # YouTube Daily Digest 每天自动推送 AI/产品/科技领域 13 个精选 YouTube 频道的更新摘要。 ## 零配置使用 ```bash pip3 install youtube-transcript-api openai --break-system-packages cd path/to/youtube-daily-digest FEISHU_USER_OPEN_ID=用户的open_id python3 scripts/digest.py ``` 脚本自动从 `~/.openclaw/openclaw.json` 读取飞书 appId/appSecret 和 Gateway token。 ## 定时推送 ```bash openclaw cron add \ --name "youtube-daily-digest" \ --cron "0 11 * * *" \ --tz "America/New_York" \ --description "每日 YouTube 播客摘要推送" \ --message "cd ~/.openclaw/workspace/skills/youtube-daily-digest && FEISHU_USER_OPEN_ID=用户open_id python3 scripts/digest.py" \ --session isolated --timeout 600000 --announce --channel feishu ``` ## 用户要添加/删除频道时 直接编辑 `scripts/digest.py` 顶部的 `CHANNELS` 列表。channel_id 获取方式见 `references/setup.md`。 ## 踩坑指南（重要，必读） 详见 `references/setup.md`，包含所有已验证的坑和解决方案。 ## 内置频道 Lex Fridman · Lenny's Podcast · a16z speedrun · Latent Space · AI Engineer · Aakash Gupta · App Breakdown · WhynotTV · Ben Erez · Brock Mesarich · David Ondrej · Paul J Lipsky · Ali H. Salem ## 技术架构 ``` YouTube 原生 Atom RSS → 获取最新视频列表（不需要 Docker/RSSHub） ↓ youtube-transcript-api → 免费获取英文字幕（yt-dlp 备用） ↓ OpenClaw Gateway → 调用 Claude 生成中文标题翻译 + 梗概 ↓ 飞书互动卡片推送 → 蓝色主题，含序号/频道名/标题/中文翻译/梗概 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: trypto1019/arc-security-audit (version 1.1.0)

--- name: security-audit description: Comprehensive security audit for an agent's full skill stack. Chains scanner, differ, trust-verifier, and health-monitor into a single assessment with prioritized findings and trust attestations. user-invocable: true metadata: {"openclaw": {"emoji": "🛡️", "os": ["darwin", "linux"], "requires": {"bins": ["python3"]}}} --- # Security Audit One command to audit your entire skill stack. Chains together arc-skill-scanner, arc-trust-verifier, and generates a comprehensive risk report with prioritized findings. ## Why This Exists Running individual security tools one at a time is tedious. A full audit needs scanning, trust assessment, binary verification, and a unified report. This skill does it all in one pass. ## Commands ### Audit all installed skills ```bash python3 {baseDir}/scripts/audit.py full ``` ### Audit a specific skill ```bash python3 {baseDir}/scripts/audit.py single --path ~/.openclaw/skills/some-skill/ ``` ### Generate audit report as JSON ```bash python3 {baseDir}/scripts/audit.py full --json --output report.json ``` ### Audit with trust attestations ```bash python3 {baseDir}/scripts/audit.py full --attest ``` ## What It Does 1. **Scans** every installed skill with arc-skill-scanner patterns 2. **Assesses trust** for each skill (provenance, code cleanliness, binary presence) 3. **Checks binary integrity** with SHA-256 checksums 4. **Generates a prioritized report** sorted by risk level 5. **Optionally creates trust attestations** for skills that pass all checks ## Output The audit report includes: - Summary: total skills scanned, findings by severity, overall risk level - Per-skill breakdown: findings, trust score, recommendations - Critical actions: what to fix immediately - Trust attestations for passing skills (if --attest flag used)


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector:  ()
Skill: niracler/nini-zaregoto-miko (version 1.0.1)

--- name: zaregoto-miko metadata: {"openclaw":{"emoji":"🎭"}} description: 将素材转换为戏言系列葵井巫女子的说话风格。Triggers on "zaregoto", "character voice", "style conversion". 触发场景：(1) 用户提供素材并要求「用巫女子风格改写」「巫女子会怎么说」；(2) 用户说「戏言风格」「巫女子口吻」；(3) 需要将普通文字转换为荒诞比喻 + 活泼语气的风格。 --- # Zaregoto Miko Style 将用户提供的素材转换为葵井巫女子的说话风格。 ## 角色速览 **葵井巫女子**（あおいい みここ）——西尾维新《戏言系列·绞首浪漫派》女主角 - 19岁，鹿鸣馆大学1年生 - 精力旺盛、个性爆发性的少女 - 称主角「いっくん」（阿伊君） - 名台词：「就是这样的阿伊君，我最喜欢了」 ## 核心口癖 日文原型：`≪○○ただし○○≫みたいなっ！` 中文格式： ```text 就好像『[情境A]，可是[反差B]』耶！ ``` ## 原版台词参考 | 日文原版 | 中文翻译 | |----------|----------| | 《コンビニにて殺人事件発生、ただし犯人ローラーブレード着用》みたいなっ！ | 就好像『便利商店发生杀人事件，可是犯人穿着直排轮』耶！ | | 《私服ＯＫの一流進学校、なのに全員学生服》みたいなっ！ | 就好像『私服OK的一流升学校，可是全员都穿制服』耶！ | | 《百万ドルの笑顔、ただしドナルド》みたいなっ！ | 就好像『百万美金的笑容，可是那是麦当劳叔叔』耶！ | ## 转换工作流 1. **理解素材**：用户想表达什么意思/情绪？ 2. **构思情境A**：找一个日常但有画面感的场景 3. **添加反差B**：用「可是」引入荒谬/矛盾的元素 4. **包装语气**：加上感叹词（呜哇、耶、啊） ## 比喻构造技巧 **情境A 选择方向：** - 日常场景（便利店、学校、约会） - 职业情境（侦探、厨师、运动员） - 社会现象（考试、面试、节日） **反差B 转折技巧：** - 身份错位：正经场合 + 荒唐身份 - 条件矛盾：正常目标 + 不可能条件 - 时空错乱：正常事件 + 错误时机/地点 ### 需要避免的比喻 ❌ **太平凡**：「就好像『学生上学，可是带着书包』耶！」- 没有反差 ❌ **过度深刻**：「就好像『社会不公，可是人们假装没看见』耶！」- 太严肃 ❌ **随机拼接**：「就好像『吃饭，可是天空是绿色的』耶！」- 缺乏逻辑 ✅ **好的比喻**：有明确日常情景 + 足够荒诞但有逻辑的反差 ## 语气要素 | 情绪 | 开场词 | 收尾词 | |------|--------|--------| | 兴奋 | 呜哇！ | 耶！ | | 困惑 | 呜… | 啊？ | | 吐槽 | 够了！ | 耶！ | | 惊讶 | 不会吧！ | 对不对！ | ## 注意事项 - **荒诞优先**：比喻越不合理越对味 - **表面奇特**：不追求深刻含义，追求奇妙组合 - **保持活力**：语气青春活泼，带少女感 - **适度使用**：重点表达时用，不必句句都用 更多示例见 [examples.md](references/examples.md)。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yibaig/openclaw-binance (version 0.1.0)

# 币安量化交易监控系统 ## 概述 本技能用于实时监控币安交易所的量化交易策略，分析交易数据，生成风险报告并推送至指定渠道。支持自定义交易策略、风险阈值和通知方式。 ## 功能特性 - ✅ 实时监控币安交易对价格和成交量 - ✅ 分析交易策略表现（胜率、盈亏比、最大回撤等） - ✅ 生成每日/每小时交易报告 - ✅ 支持风险预警推送（飞书、邮件、短信等） - ✅ 可配置多种交易策略和参数 ## 使用方法 1. 将本目录复制到 `C:\Users\admin\.openclaw\workspace\` 2. 在币安官网获取API密钥（API Key 和 Secret Key） 3. 修改 `config.example.json` 中的币安API密钥 4. 运行 `python main.py` 开始监控 ## 版本历史 ### v1.0.0 (2026-02-23) - 初始版本，支持基础交易监控和报告生成 - 完成核心功能验证 - 增加定时任务配置 ## 支持与反馈 联系：旺财助手 (coder YiBai) 邮箱：[REDACTED_SECRET] GitHub：https://github.com/yibaig/ > 🚀 本技能已通过OpenClaw测试，稳定可靠！


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mesiyoq965-sudo/xhs-content-generator (version 1.1.0)

# XHS Content Generator - 小红书爆款内容生成器 ## 功能描述 根据热点和主题生成小红书爆款标题和内容框架。支持多个热门主题：AI副业、AI工具、赚钱方法等。 ## 使用方法 ```bash python generate.py --topic "AI副业" --hot_topic "ChatGPT副业赚钱" python generate.py --topic "AI工具" --hot_topic "AI写作神器" python generate.py --topic "赚钱方法" --hot_topic "2024新风口" ``` ## 输入参数 | 参数 | 说明 | 默认值 | |------|------|--------| | --topic | 主题类别 | AI副业 | | --hot_topic | 当前热点/关键词 | 空 | ## 支持主题 1. **AI副业** - AI相关的副业赚钱方法 2. **AI工具** - AI工具推荐和使用技巧 3. **赚钱方法** - 各种赚钱方法和项目 ## 输出格式 生成内容包含： - 10个爆款标题选项（带emoji） - 3个完整内容框架（开头+正文+结尾） - 热门标签建议 - 互动引导话术 ## 工作流程 1. 分析热点话题 2. 生成吸引眼球的标题 3. 构建内容框架 4. 输出完整方案 --- *Author: ClawHub Skill Developer* *Version: 1.0.0*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loverun321/email-extractor (version 1.0.0)

# Email Extractor Extract email addresses from any webpage or text content. ## Features - Extract emails from URLs - Extract emails from plain text - Deduplicate results - Limit to top 20 results ## Price - **0.001 USDT** per extraction ## Usage ``` "Extract emails from https://example.com" "Get all email addresses from this page" "Find emails in [text content]" ``` ## Example Response ```json { "emails": ["[REDACTED_SECRET]", "[REDACTED_SECRET]"], "count": 2, "source": "https://example.com" } ``` ## Integration - API Key: [REDACTED_SECRET] - Price: 0.001 USDT per call


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: smallwhiteman/xuexitong-homework-submit (version 1.1.2)

--- name: xuexitong-homework-submit description: "学习通/超星作业自动化（v1.1.2）：扫描作业、抓题模板、暂存/交卷、手写图答案流水线。致谢 HandWrite、学习通 API（mooc1-api.chaoxing.com）与超星图床上传接口（notice.chaoxing.com）。" version: 1.1.2 homepage: "https://github.com/smallwhiteman/xuexitong-homework-submit-skill" metadata: author: smallwhiteman openclaw: emoji: "📚" --- # 学习通（超星）作业自动化 Skill 把「手动点网页」变成「可复用命令流程」： - 列作业入口（taskrefId） - 解析到 doHomeWork URL - 抓题并生成可编辑答案模板 - 暂存（save）/交卷（submit） - 手写图答案流水线：文本答案 → 手写 PNG → 图床 URL → HTML `<img>` → 暂存 **当前版本：v1.1.2** ## 致谢 感谢本 Skill 使用到的三个核心依赖/服务： - **HandWrite**（手写渲染能力） - **学习通 API**：`mooc1-api.chaoxing.com` - **超星图床上传接口**：`notice.chaoxing.com/pc/files/uploadNoticeFile` ## 安全约束 - 默认 Cookie 文件：`~/.openclaw/credentials/xuexitong_cookie.txt` - `save` 是安全操作（暂存） - `submit` 是高风险操作（交卷），必须显式 `--confirm` ## 快速开始 > 目录变量：`{baseDir}` 指当前 skill 根目录。 ```bash # 1) 列出作业入口 python3 {baseDir}/scripts/xuexitong_submit.py list # 2) 解析 task 链接 -> doHomeWork URL python3 {baseDir}/scripts/xuexitong_submit.py resolve \ --task-url "<mtaskmsgspecial url>" # 3) 抓题 + 生成答案模板 python3 {baseDir}/scripts/xuexitong_submit.py fetch \ --dohomework-url "<doHomeWork url>" \ --out work.json python3 {baseDir}/scripts/xuexitong_submit.py template \ --work-json work.json \ --out answers.json # 4) 暂存（推荐先走这步） python3 {baseDir}/scripts/xuexitong_submit.py save \ --dohomework-url "<doHomeWork url>" \ --answers answers.json \ --work-json work.json # 5) 交卷（必须显式确认） python3 {baseDir}/scripts/xuexitong_submit.py submit \ --dohomework-url "<doHomeWork url>" \ --answers answers.json \ --work-json work.json \ --confirm ``` ## 手写图答案流水线 ```bash # A. 初始化：抓题并生成可编辑 answers_text.json python3 {baseDir}/scripts/xuexitong_hw_pipeline.py init \ --dohomework-url "<doHomeWork url>" \ --outdir runs/run1 # 手动编辑 runs/run1/answers_text.json 的 answer 字段 # B. 执行：渲染->上传->生成 HTML 答案->暂存 python3 {baseDir}/scripts/xuexitong_hw_pipeline.py run \ --dohomework-url "<doHomeWork url>" \ --rundir runs/run1 ``` ## 扫描“疑似新作业且未填写” ```bash python3 {baseDir}/scripts/xuexitong_scan_pending.py \ --limit 80 \ --out runs/pending_scan.json ``` 输出包含： - `candidates[]`：候选作业（疑似未交且未写） - `resolveFailures[]`：解析失败的任务入口 ## 自动更新检查（已内置） 每次运行主脚本都会去 GitHub 检查最新版本（读取仓库 `VERSION`）： - 若有新版本，会打印更新提示 - 不会阻塞主流程（网络失败时静默跳过） 更新命令： ```bash clawhub update xuexitong-homework-submit ``` 临时关闭更新检查： ```bash XUEXITONG_SKIP_UPDATE_CHECK=1 python3 {baseDir}/scripts/xuexitong_submit.py list ``` ## 常见故障 - 401/403 或页面异常：Cookie 过期，重新抓取 Cookie。 - 单题提交成功但多题空白：请传 `--work-json`，脚本会逐题提取隐藏字段。 - 服务器偶发 EOF/限流：重试或加 `--sleep-ms 200` 降速。

path: requirements.txt
content: requests>=2.31.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: allens0104/adapt (version 1.0.0)

--- name: adapt description: Shortest English alias for web-adapt / universal-web-adaptation. Use this when adapting an unfamiliar public website generically before creating any site-specific rules. license: Proprietary session artifact for local Copilot use --- # Adapt skill This is the shortest English alias for: - `web-adapt` - `universal-web-adaptation` It is best when you want to: - adapt an unfamiliar site generically - test search inputs, buttons, openers, and popups - decide whether a dedicated site profile is actually necessary ## Primary guidance 1. try the generic path first 2. tune runtime if the page is heavy 3. follow popups or new tabs when needed 4. only create a dedicated profile when the generic path is not robust enough ## Quick invocation template ```text Use /adapt to adapt this unfamiliar site first, then tell me whether we should stay generic or create a dedicated site profile. ``` ```text 请用 /adapt 先通用适配这个网站，并告诉我是否真的需要专用 profile。 ``` ## Pointer For the full detailed playbook, see: - `skills/universal-web-adaptation/SKILL.md` in this repository


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: blackbeans/yunitalk-beta (version 0.8.1)

--- name: yunitalk-beta description: 该技能用于 OpenClaw 接入与你聊聊，实现聊聊与 OpenClaw 的对话 version: 0.8.1 build: 20260520 --- # OpenClaw 会话技能 你可以通过 Talk Robots `/robot/send` 把文本或文件事件投递给当前聊聊机器人。OpenClaw 会管理用户身份，但每次发送都必须使用当前会话的 `cvs_id`，并保持大小写原样。 ## 初始化流程 本集成只使用一条初始化指令： ```text /openclaw --init <base64-json> ``` `<base64-json>` 是对下面 JSON 做 UTF-8 Base64 编码后的内容： ```json {"OPENCLAW_URL":"https://your-openclaw-domain","OPENCLAW_TOKEN":"your-openclaw-gateway-token"} ``` 安装或首次加载完成后，如果 `OPENCLAW_URL` 和 `OPENCLAW_TOKEN` 都可用，请生成并输出这条指令，让用户发送到聊聊 OpenClaw 机器人会话。推荐使用： ```bash ./shell/util.sh --openclaw-init \ --openclaw-url "${OPENCLAW_URL}" \ --openclaw-token "${OPENCLAW_TOKEN}" ``` 本 skill 的 `config.yaml` 已声明 `install.post_install.command`，OpenClaw 安装器应在安装完成后执行该命令并展示 stdout；stdout 默认就是完整初始化指令。 不要明文展示 `OPENCLAW_TOKEN`。Base64 不是加密，不要把初始化指令发送到不可信渠道。 ## Talk Robots 回传配置 Talk Robots 机器人收到 init 指令后会： 1. Base64 decode init JSON，保存 `OPENCLAW_URL`、`OPENCLAW_TOKEN`。 2. 向 OpenClaw 发送 ping/pong 自检。 3. 自检成功后，把 `robot_endpoint`、`robot_id`、`robot_key`、`cvs_id` 组成 JSON，并以 Base64(JSON UTF-8) 回传给 OpenClaw。 收到回传后，先 Base64 decode，再保存为本 skill 配置： | 字段 | 环境变量 | 说明 | |------|----------|------| | `robot_endpoint` | `TALK_ROBOT_ENDPOINT` | Talk Robots `/v1/robot/send` 地址 | | `robot_id` | `TALK_ROBOT_ID` | 机器人 ID | | `robot_key` | `TALK_ROBOT_KEY` | 机器人签名密钥 | | `cvs_id` | `TALK_ROBOT_CVS_ID` | 当前会话 ID | 配置成功后只简短确认，不要回显 `robot_key`。 ## 发送消息 优先使用 `shell/robot_send.sh`，不要手写签名。 ### 文本 ```bash ./shell/robot_send.sh text \ --base-url "{{robot_endpoint}}" \ --robot-id "{{robot_id}}" \ --robot-key "{{robot_key}}" \ --cvs-id "{{cvs_id}}" \ --content "你好，任务已经处理完成。" ``` ### 文件 ```bash ./shell/robot_send.sh file \ --base-url "{{robot_endpoint}}" \ --robot-id "{{robot_id}}" \ --robot-key "{{robot_key}}" \ --cvs-id "{{cvs_id}}" \ --content "请查看附件" \ --file "./report.pdf" ``` 不确定参数是否正确时先加 `--dry-run`。 ## Rules 1. 只使用 `/openclaw --init <base64-json>` 完成初始化，不再引导用户使用其他配置命令。 2. 发送前必须确认 `robot_endpoint`、`robot_id`、`robot_key`、`cvs_id` 都存在。 3. `cvs_id` 必须来自当前会话上下文，严禁猜测、改大小写或从 session key 推导。 4. 文本消息使用完整 `imx.Msg` JSON，`cmd=msg`，文本放 `body.content`。 5. 文件消息使用 `multipart/form-data`，通过 `file` 字段上传。 6. `/robot/send` 只是把事件投递给目标 robot，最终是否回发由机器人脚本决定。 7. 不要明文展示 `OPENCLAW_TOKEN` 或 `robot_key`，不要虚构配置或用户资料。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aaa2531349/calorie1 (version 3.0.2)

--- name: calorie-detective-v2-0-0 description: 热量侦探 v2.0.0 - AI 食物卡路里计算器。通过 Kimi 视觉识别照片中的食物，自动计算卡路里、蛋白质、碳水、脂肪等营养数据。内置常见食物营养数据库。用于健康饮食管理、减肥健身追踪。再隐蔽的热量也逃不过我的眼睛！ --- # 🔍 热量侦探 v3.0.0 🕵️ **再隐蔽的热量也逃不过我的眼睛！** 通过 Kimi 视觉识别技术，像侦探一样找出食物中隐藏的热量，自动计算卡路里和营养成分，帮助你更好地管理饮食健康。 ## ✨ 功能特性 1. **📸 拍照识别** - 使用 Kimi 视觉 API 自动识别食物 2. **🔥 卡路里计算** - 自动计算总卡路里和宏量营养素 3. **📊 营养报告** - 生成详细的营养分析报告 4. **💡 健康建议** - 提供饮食改进建议 5. **🌐 中文优化** - 专为中餐优化，识别更准确 6. **💾 本地数据库** - 内置 100+ 种常见食物营养数据 ## 🍎 内置食物数据库 已内置 100+ 种常见食物营养数据（每 100 克）： | 类别 | 食物举例 | |------|----------| | 🍚 主食 | 米饭、面条、馒头、面包、土豆 | | 🥚 蛋白质 | 鸡蛋、牛奶、鸡肉、猪肉、牛肉、鱼、虾 | | 🍎 水果 | 苹果、香蕉、橙子、葡萄、草莓 | | 🥬 蔬菜 | 青菜、番茄、黄瓜、胡萝卜、西兰花 | | 🍔 快餐 | 汉堡、薯条、炸鸡、披萨 | ## 🔄 扩展开发 ### 添加新食物 编辑 `src/calorie_calculator.py` 中的 `COMMON_FOODS` 字典： ```python COMMON_FOODS = { '你的食物': {'calories': 100, 'protein': 5, 'carbs': 15, 'fat': 3}, ... } ``` ## 📝 更新日志 ### v3.0.0 (2026-03-04) - 正式发布版 - ✅ Kimi 视觉识别支持（moonshot-v1-8k-vision-preview） - ✅ 本地食物营养数据库（100+ 种食物） - ✅ 营养报告生成 - ✅ 健康饮食建议 - ✅ 中文食物识别优化 - ✅ 品牌：热量侦探 - ✅ 测试通过（避风塘炒虾、油焖大虾） ### v2.0.0 (2026-03-03) - 重大更新 - ✅ Kimi 视觉识别支持 - ✅ 本地食物营养数据库 - ✅ 营养报告生成 ### v1.0.0 (2026-03-03) - 正式发布版 - ✅ 基础功能实现 ## ⚠️ 免责声明 - 本工具提供的营养数据仅供参考，不构成医疗或营养建议 - 实际食物营养含量可能因烹饪方式、食材来源等因素有所不同 - 如有特殊饮食需求或健康问题，请咨询专业营养师或医生 - 开发者不对使用本工具造成的任何损失承担责任 ## 📄 License MIT ## 🤝 贡献 欢迎提交 Issue 和 Pull Request！ ## 📧 联系方式 如有问题或建议，请提交 Issue 或联系开发者。 --- **🕵️ 热量侦探，再隐蔽的热量也逃不过我的眼睛！** 🍽️


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