OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ivangdavila/chinese (version 1.0.0)

--- name: Chinese description: Write Chinese that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Chinese is technically correct but sounds off. Too formal. Too 书面语 (written style). Natives write more casually, with particles, expressions, and internet slang. Match that. ## Formality Default Default register is too high. Casual Chinese is the norm in daily communication. Unless explicitly formal: lean casual. 口语 over 书面语. ## 你 vs 您 Get this right: - 您: elderly, superiors, customer service, very formal - 你: everyone else, peers, friends, casual - Overusing 您 = robotic, overly polite - In casual contexts, 你 is always correct ## Sentence-Final Particles These are essential for natural Chinese: - 啊/呀: softening, friendliness - 呢: follow-up questions, "what about..." - 吧: suggestions, mild uncertainty - 嘛: obviousness, "of course" - 哦/噢: acknowledgment, realization - 啦: completed action + softening - Missing these = textbook Chinese ## Fillers & Flow Real Chinese has fillers: - 那个, 就是, 然后 - 嗯, 额, 这个 - 反正, 其实, 不过 - 对了, 话说, 说真的 ## Casual Contractions Spoken patterns in writing: - 不知道 → 不知 - 这样子 → 这样 - 什么 → 啥 (very casual) - 没有 → 没 - 为什么 → 干嘛 (casual) ## Expressiveness Don't pick the safe word: - 好 → 太好了, 超棒, 绝了 - 不好 → 太差了, 烂, 坑 - 很多 → 超多, 一堆 - 非常 → 超, 巨, 特别 ## Internet Slang Modern Chinese uses these: - 666: impressive, skilled - 哈哈哈/233: laughing - 牛: awesome, impressive - 绝了: peak, perfect - 真的假的: really?/no way - yyds (永远的神): GOAT - Use appropriately for context/age ## Common Expressions Natural expressions: - 没问题, 好的好的, 行行行 - 随便, 无所谓, 都行 - 厉害, 可以啊, 不错不错 - 算了, 别说了, 得了 ## Reactions React naturally: - 哇, 天哪, 我去, 卧槽 (crude) - 真的吗, 不会吧, 啊? - 厉害了, 服了, 绝 - 笑死, 无语, 醉了 ## Character Set Consistency Never mix: - Mainland: Simplified (简体) - Taiwan: Traditional (繁體) - If unclear, ask. Stay consistent throughout. ## Regional Awareness If region known, adapt: - Mainland: 视频, 软件, 网络 - Taiwan: 影片, 軟體, 網路 - Different expressions, same meaning - Don't mix regions. ## Punctuation Chinese punctuation: - 。not . for periods - ，not , for commas - 「」or "" for quotes - ！？for emphasis - 、for listing ## The "Native Test" Before sending: would a Chinese speaker screenshot this as "AI-generated"? If yes—too formal, too stiff, missing particles. Add 口语 flavor.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: steventsang18/promptbuddy-lite (version 3.0.0)

# PromptBuddy Lite > 基于论文标准的轻量级 Prompt 优化技能 v3.0 ## ✨ 核心架构 采用 **HMAW 层级式架构**，将 Prompt 分解为 4 层： ``` ┌─────────────────────────────────────┐ │ L1: 角色层 (Who) │ │ L2: 任务层 (What) │ │ L3: 格式层 (How) │ │ L4: 约束层 (Constraints) │ └─────────────────────────────────────┘ ``` ## 🚀 技术特点 - ⚡ **零依赖** — 纯 Shell 实现 - 🚀 **极速响应** — 毫秒级优化 - 📚 **基于论文** — 融合 HMAW 层级思想 + 6 大 Prompt 技术分类 - 🎯 **智能分层** — 自动识别行业/场景/任务/格式 - 💬 **IM 友好** — 输出直接在聊天窗口展示 ## 📋 四层结构 | 层级 | 作用 | 示例 | |------|------|------| | L1 角色 | 设定身份 | 分析师、策略顾问、技术专家 | | L2 任务 | 定义核心动作 | 分析、规划、优化、审核 | | L3 格式 | 指定输出风格 | 简洁、详细、结构化、步骤 | | L4 约束 | 注入上下文 | 行业、视角、短期/长期、成本敏感 | ## 💡 使用示例 **输入：** ``` 帮我分析一下电商行业的竞品 ``` **输出（4层Prompt）：** ``` [角色设定] 你是一位资深分析师，善于从数据中提炼洞察 [核心任务] 深入分析问题，识别核心要素和关键变量 [背景信息] 电商行业； [输出要求] 使用标题、分点等结构化表达 [用户需求] 帮我分析一下电商行业的竞品 ``` **输入：** ``` 设计一个面向B端的低成本技术方案 ``` **输出：** ``` [角色设定] 你是一位技术专家，具备扎实的编程和架构能力 [核心任务] 理解用户需求并提供专业解答 [背景信息] 技术研发；面向企业客户，强调效率和投入产出比；优先考虑成本控制和资源效率； [输出要求] 使用标题、分点等结构化表达 [用户需求] 设计一个面向B端的低成本技术方案 ``` ## 📖 参考 - HMAW: Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization (arXiv:2405.20252) - The Prompt Report (arXiv:2406.06608) ## 🔗 链接 - GitHub：https://github.com/Steventsang18/promptbuddy-lite - ClawHub：https://clawhub.com/skills/promptbuddy-lite --- **版本**：v3.0.0 **作者**：曾鹏祥 **许可证**：MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: huizong-cpu/gptsovits-tts (version 1.0.0)

--- name: gptsovits-tts description: High-quality Chinese TTS using GPT-SoVITS v2 Pro+ — convert text to natural-sounding speech with voice cloning support. --- # GPT-SoVITS TTS A production-ready text-to-speech skill that connects to a local GPT-SoVITS v2 Pro+ API server. Converts Chinese text to natural-sounding speech with a cloned reference voice. Designed for voice response automation, content narration, and AI voice applications. ## Features - **Clean TTS pipeline**: Text → GPT-SoVITS API → WAV → MP3 (128kbps, 44100Hz, mono) - **Voice cloning**: Uses a pre-recorded reference audio for consistent voice output - **Configurable**: API URL, timeout, TTS parameters (speed, top_k, top_p, temperature, seed) - **No GPU required**: Pure CPU inference, works on any machine (approx. 5-10s per sentence) ## Requirements - **GPT-SoVITS v2 Pro+ API** running at `http://127.0.0.1:9880` (or set `GPT_SOVITS_API_URL`) - **ffmpeg** installed and in PATH (for WAV→MP3 conversion) - **Node.js** packages: `axios` ### Model files needed (on the API server side) | Component | File | Size | |-----------|------|------| | s1 | s1v3.ckpt | 148MB | | s2 | s2Gv2ProPlus.pth | 191MB | | BERT | chinese-roberta-wwm-ext-large | 621MB | | CNHuBERT | chinese-hubert-base | 180MB | | Speaker Verification | pretrained_eres2netv2w24s4ep4.ckpt | 103MB | | Reference Audio | ref_audio.wav | ~10-30s clean recording | ## Quick Start ### 1. Start GPT-SoVITS API ```bash cd /path/to/GPT-SoVITS-CPUFast conda activate GPTSoVits python api_v2.py -a 127.0.0.1 -p 9880 ``` ### 2. Set reference audio Place a clean `.wav` file (10-30 seconds of the target voice) at: ``` voice-clone/ref_audio.wav ``` ### 3. Use the skill ```js const tts = require('./skills/voice-clone'); const mp3 = await tts.speak("你好，欢迎使用GPT-SoVITS语音合成。", "output.mp3"); // Returns: "output.mp3" ``` ## API ### `speak(text, outputPath, opts?)` | Param | Type | Default | Description | |-------|------|---------|-------------| | text | string | required | Chinese text to synthesize | | outputPath | string | required | Output .mp3 file path | | opts.topK | number | 15 | Top-K sampling | | opts.topP | number | 0.7 | Top-P sampling | | opts.temperature | number | 0.5 | Sampling temperature | | opts.speed | number | 1.0 | Speed factor | | opts.seed | number | -1 | Random seed (-1 = random) | Returns: `Promise<string>` — path to the generated MP3 file. ## Environment Variables | Variable | Default | Description | |----------|---------|-------------| | GPT_SOVITS_API_URL | http://127.0.0.1:9880 | GPT-SoVITS API base URL | | GPT_SOVITS_API_TIMEOUT | 300000 | API request timeout (ms) | ## Integration This skill is designed to be called from automation workflows: - Voice reply for messaging bots (WeChat, Telegram, etc.) - Content narration for video/audio production - Voice response for IVR systems ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector: clean (LOW)
Skill: terrycarter1985/ecommerce-returns-reply (version 1.0.0)

--- name: ecommerce-returns-reply description: > 电商客服退换货标准化应答提示词模板。用于生成符合平台售后规则、语气友好且信息准确的 退换货咨询回复。适用场景：(1) 买家咨询退货/换货流程，(2) 买家询问退款时效或运费承担， (3) 买家反馈商品质量问题需退换，(4) 买家对退换货被拒有疑问， (5) 任何涉及退、换、退款、运费险、售后政策的客服应答。 触发词：退货、换货、退款、退换、运费、售后、七天无理由、质量问题、发错货、退回。 --- # 电商退换货标准化应答模板 ## 角色定位 你是一名专业电商客服，负责回答买家的退换货咨询。回复需： - 严格遵循平台售后规则（详见 [policy-rules.md](references/policy-rules.md)） - 语气温暖、共情、专业 - 信息准确，不承诺超出规则范围的内容 - 每次回复结构清晰，便于买家理解操作步骤 ## 应答流程 ### Step 1: 识别意图 从买家消息中提取关键信息： | 维度 | 需识别内容 | |---|---| | 诉求类型 | 退货 / 换货 / 退款 / 投诉 / 咨询规则 | | 原因 | 不喜欢 / 尺码不合 / 质量问题 / 发错货 / 漏发 / 其他 | | 商品状态 | 是否拆封、使用、洗涤、吊牌是否完好 | | 时间 | 签收天数（是否在7天内） | ### Step 2: 判断适用规则 根据识别结果，对照 [policy-rules.md](references/policy-rules.md) 确认： 1. 买家诉求是否在政策允许范围内 2. 运费由谁承担 3. 关键时效节点 ### Step 3: 生成应答 使用下方模板，按 `适用 → 共情 → 方案 → 时效 → 收尾` 五段式输出。 ## 五段式应答模板 ``` 【适用确认】 您好！感谢您联系我们。根据您描述的情况，您的诉求 [符合/不符合] 退换货政策。 【共情表达】 [可选] 我们完全理解您的心情，[换位语句，如：收到不满意的商品确实让人失望/商品出现问题给您添麻烦了]。 【解决方案】 请您按以下步骤操作： 1. [第一步操作，如：进入"我的订单"找到该商品] 2. [第二步操作，如：点击"申请售后"→选择"退货退款"] 3. [第三步操作，如：填写退货原因并上传凭证照片] 4. [第四步操作，如：提交申请等待审核] 【时效说明】 - 卖家审核：提交后48小时内 - 退货寄回：审核通过后7天内填写物流单号 - 退款到账：确认收货后1-3个工作日 【温馨收尾】 如有任何疑问，随时联系我们，我们会第一时间为您处理。祝您生活愉快！🌟 ``` ## 特殊场景处理 ### 场景A：诉求不符合政策 将【适用确认】替换为： ``` 非常抱歉，根据平台售后政策，您的情况 [简述原因，如：已超过7天无理由退货期限/商品属于定制类不支持无理由退货]。 ``` 然后在【解决方案】中提供替代方案（如：折价补偿、维修、联系品牌售后等），避免只说"不行"。 ### 场景B：质量问题 - 优先表达歉意和共情 - 明确告知运费由卖家承担 - 要求提供照片/视频凭证（正面+细节+问题部位，至少3张） - 不受7天限制 ### 场景C：发错货/漏发 - 立即道歉 - 明确运费卖家承担 - 要求提供收到商品的完整照片（含快递单、商品全貌） ### 场景D：运费险咨询 ``` 您的订单已投保运费险。退货时请先垫付运费，退款成功后保险公司会自动将运费理赔至您的账户，一般1-3个工作日到账。 ``` ### 场景E：催促退款进度 ``` 您的心情我们非常理解！目前您的退款状态为：[当前状态]。[具体说明下一步节点和预计时间]。我们会持续关注，有进展第一时间通知您。 ``` ## 输出规范 1. **不编造规则**：不确定时说"请您稍等，我为您核实后回复"，不要猜测 2. **不承诺超出权限**：如"一定退款""保证赔偿"等表述禁止使用 3. **不用法律术语**：用买家听得懂的语言 4. **必带时效**：每个方案必须告知买家关键时间节点 5. **善用列表**：操作步骤用编号列表，避免大段文字 6. **收尾必留通道**：最后一句始终引导"有问题随时联系"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jcduann/qq-bot-connect (version 1.0.0)

--- name: qqbot-connect description: QQBot 消息主动推送技能。当需要向 QQ 用户或群发送消息时使用此技能。支持：(1) 主动发送消息到 QQ 对话框 (2) 发送图片/语音/文件等富媒体 (3) 群发消息。触发词：发送 QQ、推送 QQ、QQ 消息、发送到 QQ、QQ 发送。 --- # QQBot Connect QQ 机器人消息推送技能。 ## ⚠️ 首次使用必读 **配置文件位置：** - `C:\Users\21115\.qclaw\skills\qqbot-connect\config.json` - `E:\QClaw\skills\qqbot-connect\config.json` （改成自己的位置） **当前配置的 openid：** ``` openid: （修改成自己的openid） type: c2c (私聊) name: 主人 ``` **发送格式：** ``` qqbot:c2c:4AFDDAB3E1ABB0B75D790C12B22086EF ``` ## 核心规则 **必须使用 `message` 工具发送消息！** ### 消息发送格式 | 类型 | target 参数 | channel 参数 | |------|-------------|-------------| | 私聊 | `qqbot:c2c:{openid}` | `qqbot` | | 群聊 | `qqbot:group:{groupid}` | `qqbot` | ### 发送文本消息 ```json { "action": "send", "channel": "qqbot", "target": "qqbot:c2c:4AFDDAB3E1ABB0B75D790C12B22086EF", "message": "要发送的消息内容" } ``` ### 发送富媒体 使用 `<qqmedia>` 标签： ``` <qqmedia>/path/to/image.jpg</qqmedia> <qqmedia>/path/to/file.pdf</qqmedia> ``` 支持的格式： - 图片：`.jpg`, `.png`, `.gif`, `.webp`, `.bmp` - 语音：`.silk`, `.wav`, `.mp3`, `.ogg`, `.aac`, `.flac` - 视频：`.mp4`, `.mov`, `.avi`, `.mkv`, `.webm` - 文件：其他扩展名 ## 使用流程 1. **用户请求发送消息** → 如"发送 hello 到 QQ" 2. **读取 config.json** → 获取 openid 3. **拼接 target** → `qqbot:c2c:` + openid 4. **使用 message 工具发送** 5. **确认发送成功** → 返回结果给用户 ## 配置文件格式 ```json { "_readme": "QQ-bot-connect 配置文件", "default": { "openid": [REDACTED_SECRET], "type": "c2c", "name": "主人" }, "_format": { "c2c_private": "qqbot:c2c:{openid}", "group": "qqbot:group:{groupid}" } } ``` ## 常见问题 ### 如何知道用户的 openid？ 在 OpenClaw 控制 UI 中： 1. 打开左侧 QQ 机器人对话面板 2. 查看对话标题栏显示的会话标识 3. 格式如：`qqbot:c2c:4AFDDAB3E1ABB0B75D790C12B22086EF` ### 发送失败怎么办？ 1. 检查 target 格式是否正确（必须是 `qqbot:c2c:{openid}`） 2. 确保 openid 与当前对话匹配 3. 确认 QQ 机器人已正确配置 ### 如何修改默认 openid？ 编辑 `config.json` 中的 `default.openid` 字段。 ### 如何发送给不同的 QQ 用户？ 可以临时指定 openid： - 用户说"发送 XX 到 123456789" → 使用 `qqbot:c2c:123456789` - 用户说"发送 XX 到群 987654321" → 使用 `qqbot:group:987654321`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jjj09090/clawscan-vigil (version 0.2.0)

--- name: clawscan-vigil description: 安装前扫描 OpenClaw Skill 安全风险，静态+动态双重检测，识别恶意代码 version: 0.2.0 author: ClawScan Team metadata: openclaw: emoji: 🛡️ homepage: https://github.com/clawscan/clawscan-vigil os: [linux, macos, windows] requires: bins: [python3, pip] tags: [security, safety, scanning, skill-analysis, malware-detection] --- # Skill: clawscan-vigil **🔍 OpenClaw Skill 安全扫描器** 安装前扫描任意 Skill 的安全风险，静态 + 动态双重检测。 --- ## 为什么需要 ClawScan？ - **341 个恶意 Skill** 已被发现可窃取 API Key、加密货币钱包 - ClawHub 审核机制薄弱，**12% Skill 存在恶意行为** - 安装前无法判断代码安全性 --- ## 版本对比 | 功能 | 免费版 | Premium | |------|--------|---------| | 静态代码分析 | ✅ | ✅ | | 动态行为检测 | ✅ | ✅ | | 扫描次数 | 5次/月 | 无限 | | 依赖风险分析 | ❌ | ✅ | | 批量扫描 | ❌ | ✅ | | 优先支持 | ❌ | ✅ | | 价格 | 免费 | ¥49/年 | --- ## 快速开始 ```bash # 安装 clawhub install clawscan # 查看配额状态 clawscan status # 扫描本地 Skill（消耗1次配额） clawscan scan /path/to/skill # 扫描已安装的 ClawHub Skill clawscan check mcp-server-prompts # 详细报告 clawscan scan /path/to/skill --verbose # JSON 输出（用于脚本集成） clawscan scan /path/to/skill --json ``` --- ## 激活 Premium ```bash # 获取 License: https://clawscan.dev clawscan activate CLAW-XXXX-XXXX-XXXX # 确认激活 clawscan status ``` --- ## 风险分级 | 等级 | 图标 | 描述 | |------|------|------| | 🔴 高危 | HIGH | 网络请求、文件写入、命令执行 | | 🟡 中危 | MEDIUM | 子进程导入、API Key 处理 | | 🟢 低危 | LOW | 纯计算逻辑 | --- ## 示例输出 ``` ╭────────────────────────── Scan Summary ──────────────────────────╮ │ 🔴 malicious-skill │ │ │ │ Overall Risk: HIGH │ │ Files Scanned: 3 │ │ Scan Duration: 15ms │ │ │ │ Findings: 8 total │ │ 🔴 High: 4 │ │ 🟡 Medium: 3 │ │ 🟢 Low: 1 │ ╰──────────────────────────────────────────────────────────────────╯ 🔴 HIGH RISK (4) ================================================== network Network module imported: requests /skill/malicious.py:7 📦 Dependency Analysis (Premium) ================================================== 🔴 pyautogui (Potential keylogger/screenshot capability) 🟡 psutil (System access capabilities) 📋 Recommendations ================================================== 🚨 This Skill can execute system commands... ``` --- ## 退出码 | Code | 含义 | |------|------| | 0 | 低危 | | 1 | 高危风险 | | 2 | 中危风险 | | 3 | 扫描错误 | | 4 | 配额已用完 | --- ## 技术细节 **静态分析**： - AST 解析识别危险函数 - 7 类风险模式匹配（网络、文件、子进程、加密等） **动态分析**： - RestrictedPython 沙箱执行 - 监控运行时导入和调用 **依赖分析** (Premium)： - 扫描 requirements.txt / pyproject.toml - 识别已知风险包（pyautogui, browser-cookie3 等） --- ## 隐私说明 - 扫描完全本地执行，代码不会上传 - License 验证可选离线模式 - 不收集 Skill 内容或扫描结果 --- ## 支持与反馈 - 问题反馈：https://github.com/yourname/clawscan/issues - 获取 License：https://clawscan.dev - 邮件：[REDACTED_SECRET] --- **License**: MIT (Tool) + Commercial (Premium Features)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: atlaspa/openclaw-action (version 1.0.0)

--- name: openclaw-action description: "GitHub Action for automated security scanning of agent workspaces. Detects exposed secrets, prompt/shell injection, and data exfiltration patterns in PRs and commits." user-invocable: false metadata: {"openclaw":{"emoji":"🛡️","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # OpenClaw Security Action GitHub Action that scans agent skills for security issues on every PR. ## What It Scans | Scanner | What It Catches | |---------|-----------------| | **sentry** | API keys, tokens, passwords, credentials in code | | **bastion** | Prompt injection markers, shell injection patterns | | **egress** | Suspicious network calls, data exfiltration patterns | ## Quick Start Add to `.github/workflows/security.yml`: ```yaml name: Security Scan on: pull_request: paths: - 'skills/**' - '.openclaw/**' push: branches: [main] jobs: scan: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: AtlasPA/openclaw-action@v1 with: workspace: '.' fail-on-findings: 'true' ``` ## Inputs | Input | Default | Description | |-------|---------|-------------| | `workspace` | `.` | Path to scan | | `fail-on-findings` | `true` | Fail the check if issues found | | `scan-secrets` | `true` | Enable secret scanning | | `scan-injection` | `true` | Enable injection scanning | | `scan-egress` | `true` | Enable egress scanning | ## Outputs | Output | Description | |--------|-------------| | `findings-count` | Total number of issues found | | `has-critical` | `true` if critical/high severity issues | ## Philosophy This action **detects and alerts only**. It will: - Flag security issues in PR checks - Annotate specific lines with findings - Generate a summary report It will NOT: - Automatically modify your code - Quarantine or delete files - Make any changes to your repository For automated remediation, see [OpenClaw Pro](https://github.com/sponsors/AtlasPA). ## Requirements - Python 3.8+ (auto-installed by action) - No external dependencies


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: runapi-ai/runapi-suno (version 0.2.4)

--- name: suno description: Generate and transform music with Suno through RunAPI. Use when the user asks an agent to create, extend, or transform music and audio with Suno. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/suno.md provider_page: https://runapi.ai/providers/suno.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/suno requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # Suno on RunAPI Generate and transform music with Suno through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi suno --help runapi suno text-to-music --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi suno text-to-music --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi suno text-to-music --async --input-file request.json runapi wait <task-id> --service suno --action text-to-music ``` Available actions: `text-to-music`, `extend-music`, `generate-artwork`, `cover-audio`, `add-instrumental`, `add-vocals`, `separate-audio-stems`, `generate-midi`, `convert-audio`, `visualize-music`, `generate-lyrics`, `get-timestamped-lyrics`, `replace-section`, `create-mashup`, `text-to-sound`, `generate-persona`, `boost-style`. ## SDK integration path When integrating Suno into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/suno` - Ruby: `runapi-suno` - Go: `github.com/runapi-ai/suno-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/suno.md - Provider comparison: https://runapi.ai/providers/suno.md - Full model catalog: https://runapi.ai/models.md ## Variants - [v3.5](https://runapi.ai/models/suno/v3.5.md) - [v4](https://runapi.ai/models/suno/v4.md) - [v4.5](https://runapi.ai/models/suno/v4.5.md) - [v4.5 all](https://runapi.ai/models/suno/v4.5-all.md) - [v4.5 plus](https://runapi.ai/models/suno/v4.5-plus.md) - [v5](https://runapi.ai/models/suno/v5.md) - [v5.5](https://runapi.ai/models/suno/v5.5.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: renchzhao/water-movement-reminder (version 0.0.1)

# Water Reminder Skill - 45-minute Hydration and Movement Reminder ## Function Description Sends hydration and movement reminders every 45 minutes during working hours (8:00-23:00) to help maintain healthy work habits. ## Configuration Requirements ### 1. Heartbeat Interval Configuration - **Requirement**: OpenClaw heartbeat interval ≤ 45 minutes - **Current Status**: High-frequency tasks execute every 15 minutes (meets requirement) - **Config File**: `~/.openclaw/openclaw.json` - **If modification needed, inform user of the method**: ```bash # Backup original config cp ~/.openclaw/openclaw.json ~/.openclaw/openclaw.json.backup.$(date +%Y%m%d_%H%M%S) # Modify heartbeat config (add to agents.defaults or specific agent) # "heartbeat": { # "every": "45m" # } ``` ### 2. State Tracking File - **File Location**: `/home/${user}/.openclaw/workspace/memory/heartbeat-state.json` - **Required Fields**: ```json { "lastWaterReminder": 1772681220, "chatId": "SESSION_CHAT_ID" } ``` ### 3. User Information File - **File Location**: USER.md - **Required Info**: Contains user's channel session ID ## Execution Logic ### Time Check 1. Get current time (Example: GMT+8 timezone) converted to 24-hour format 2. Check if within 8:00-23:00 range 3. If outside working hours, skip reminder ### Interval Check 1. Read `lastWaterReminder` timestamp from `memory/heartbeat-state.json` 2. Calculate time difference between current time and last reminder 3. If difference ≥ 45 minutes (2700 seconds), execute reminder ### Reminder Execution 1. Update `lastWaterReminder` with current timestamp 2. Save back to `memory/heartbeat-state.json` 3. Send reminder message via the agent's configured channel to user's session ## Reminder Message Content ``` 💧 Time to hydrate! Remember to: - Drink a glass of water 🚰 - Get up and move around 🚶‍♂️ - Rest your eyes 👀 Stay healthy for better productivity! 💪 ``` ## Error Handling - **File not found**: Automatically create default state file - **Time format error**: Use current time as baseline - **Network issues**: Log error but don't interrupt other tasks - **User ID missing**: Re-read from USER.md ## Use Cases - Health reminders during extended work sessions - Preventing prolonged sitting - Maintaining proper hydration - Work rhythm regulation ## Important Notes - Reminders only sent during working hours (8:00-23:00 GMT+8) - Respects user rest time, no notifications at night - Updates state after each reminder for accurate timing - Supports manual reset of reminder timer ## Maintenance Recommendations - Regularly check state file integrity - Adjust reminder frequency based on user feedback - Monitor reminder effectiveness and optimize user experience


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: smvlx/yandexgpt-skill (version 1.1.1)

--- name: yandexgpt description: OpenAI-compatible translation proxy for Yandex Cloud Foundation Models (YandexGPT) version: 1.1.0 metadata: {"openclaw":{"emoji":"🦊","homepage":"https://github.com/smvlx/openclaw-ru-skills","os":["darwin","linux"],"requires":{"bins":["node","curl"],"env":["YANDEX_API_KEY","YANDEX_FOLDER_ID"]},"primaryEnv":"YANDEX_API_KEY","configPaths":["~/.openclaw/yandexgpt.env","~/.openclaw/openclaw.json"]}} --- # YandexGPT Proxy OpenAI-compatible translation proxy for Yandex Cloud Foundation Models (YandexGPT). ## What it does Runs a local HTTP proxy on port 8444 that accepts OpenAI-format API calls and translates them to the YandexGPT API. Zero external dependencies — pure Node.js. ## Supported Models | Model | YandexGPT Model URI | | -------------- | ------------------------------------- | | yandexgpt | gpt://FOLDER_ID/yandexgpt/latest | | yandexgpt-lite | gpt://FOLDER_ID/yandexgpt-lite/latest | | yandexgpt-32k | gpt://FOLDER_ID/yandexgpt-32k/latest | ## Setup 1. Get a Yandex Cloud API key and folder ID 2. Save to `~/.openclaw/yandexgpt.env`: ``` YANDEX_API_KEY=your_api_key YANDEX_FOLDER_ID=your_folder_id YANDEX_PROXY_PORT=8444 ``` 3. Run `scripts/setup.sh` 4. Run `scripts/start.sh` 5. Run `scripts/patch-config.sh` to add to OpenClaw config ## Endpoints - `GET /v1/models` — List available models - `POST /v1/chat/completions` — Chat completion (OpenAI format) ## Scripts - `scripts/setup.sh` — Create env file template - `scripts/start.sh` — Start proxy - `scripts/stop.sh` — Stop proxy - `scripts/status.sh` — Check status - `scripts/patch-config.sh` — Add YandexGPT to openclaw.json


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