OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sullivangu/eva-ui-skill (version 1.0.1)

--- name: eva-ui description: Use this skill when creating, reviewing, or extending interfaces inspired by Evangelion monitor, tactical screen, and control-room aesthetics. It guides color selection, typography, pattern language, data density, motion, and component composition so outputs keep EVA's industrial red-orange instrument feel instead of drifting into generic sci-fi or SaaS dashboards. version: 1.0.1 --- # EVA UI Skill Current version: `1.0.1` Use this skill for EVA-inspired UI design systems, frontend implementation, component generation, prompt authoring, or design review. ## Workflow 1. Start with `references/style-principles.md` to lock the visual direction. 2. Apply the system rules from `references/tokens.md`, `references/typography.md`, and `references/motion.md`. 3. Use `references/pattern-language.md` and `references/data-elements.md` to build the non-typographic visual grammar. 4. Build or review UI against `references/component-rules.md`. 5. Use `references/prompt-recipes.md` to generate new pages or components. 6. Reject outputs that match any failure mode in `references/anti-patterns.md`. ## Required Style Principles - High information density - Hard-edged framing - Red-orange status-driven color system - Technical condensed typography - Instrument-style motion - Pattern language built from stripes, ticks, brackets, meshes, and calibration motifs - Borders, rails, labels, and measurement marks should do more visual work than spacing or soft shadows ## Reference Map - `references/style-principles.md` - `references/tokens.md` - `references/typography.md` - `references/motion.md` - `references/pattern-language.md` - `references/data-elements.md` - `references/component-rules.md` - `references/prompt-recipes.md` - `references/anti-patterns.md` ## Assets - `assets/example-lab/index.html` - `assets/example-lab/styles.css` Use the example lab as a starting point for prototypes, demos, or component extraction. ## Output Constraints - Avoid soft SaaS cards, large radii, and friendly product-dashboard spacing. - Avoid relying on typography alone; reinforce hierarchy with stripe bands, ticks, reticles, threshold bars, and rails. - Treat red and orange as primary EVA command colors; use other hues only when they communicate a specific data mode. - Prefer repeated structured modules over hero-card layouts. - Decorative gradients are allowed only when they communicate field state, heat, scan, or energy behavior.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hoohoobear/automation-scripts (version 1.0.0)

# Automation Scripts Skill > 自动化脚本管理 - 创建、执行、维护自动化任务 ## 功能 - 脚本模板库 - 定时任务管理 - 执行日志记录 - 失败自动重试 - 状态通知 ## 脚本分类 | 类别 | 说明 | 示例 | |------|------|------| | 监控 | 系统监控 | 健康检查、磁盘监控 | | 备份 | 数据备份 | 配置备份、日志清理 | | 同步 | 数据同步 | Git自动提交、文件同步 | | 报告 | 报告生成 | 日报、周报、月报 | | 研究 | 自动化研究 | GitHub项目分析 | ## 使用方法 ```bash # 列出可用脚本 skill:automation-scripts --list # 创建新脚本 skill:automation-scripts --create --name "my-script" --type "monitor" # 执行脚本 skill:automation-scripts --run "script-name" # 查看执行日志 skill:automation-scripts --log "script-name" # 定时执行 skill:automation-scripts --schedule "script-name" --cron "0 6 * * *" # 禁用脚本 skill:automation-scripts --disable "script-name" # 启用脚本 skill:automation-scripts --enable "script-name" ``` ## 内置脚本模板 ### 1. 健康检查脚本 ```bash #!/bin/bash # OpenClaw 健康检查 # 位置: ~/scripts/openclaw-health-check.sh # 检查项目 - 服务状态 (Gateway进程) - 配置文件语法 - 磁盘空间 - 内存使用 - 日志错误 # 输出格式 ✓ 检查通过 ✗ 检查失败 (显示原因) ``` ### 2. 自动备份脚本 ```bash #!/bin/bash # 配置自动备份 # 位置: ~/scripts/backup-openclaw-config.sh # 功能 - 每日自动备份配置 - 保留30天历史 - 清理旧备份 # 配置 BACKUP_DIR="~/.openclaw/backups" RETENTION_DAYS=30 ``` ### 3. Git 自动同步脚本 ```bash #!/bin/bash # Git 自动同步 # 位置: ~/scripts/auto-git-sync.sh # 功能 - 检查文件变更 - 自动 add + commit - 自动 push - 记录同步日志 ``` ### 4. 定时报告脚本 ```bash #!/bin/bash # 定时报告生成 # 位置: ~/scripts/daily-report.sh # 功能 - 生成日报 - 发送到指定位置 - 记录发送状态 ``` ### 5. OpenCode 集成脚本 ```bash #!/bin/bash # OpenCode 自动修复 # 位置: ~/scripts/opencode-auto-fix.sh # 功能 - 检测服务故障 - 调用 OpenCode 诊断 - 自动修复 - 验证结果 ``` ## 脚本结构 ``` scripts/ ├── templates/ # 脚本模板 │ ├── monitor.sh │ ├── backup.sh │ ├── sync.sh │ └── report.sh ├── custom/ # 自定义脚本 ├── logs/ # 执行日志 └── config.conf # 全局配置 ``` ## 配置示例 ```json { "automation": { "enabled": true, "logRetentionDays": 30, "maxRetries": 3, "retryDelay": 60, "notifications": { "onFailure": true, "onSuccess": false } } } ``` ## 执行日志 | 字段 | 说明 | |------|------| | timestamp | 执行时间 | | script | 脚本名称 | | status | success/failure | | duration | 执行耗时 | | output | 输出摘要 | | error | 错误信息（如有） | ## 最佳实践 1. **脚本命名** - 使用描述性名称 - 包含类型前缀: `backup-`, `monitor-`, `sync-` 2. **错误处理** - 总是检查返回值 - 失败时发送通知 - 保留错误日志 3. **日志管理** - 记录执行详情 - 定期清理旧日志 - 分析失败模式 4. **定时任务** - 避免高峰期执行 - 设置合理重试 - 监控执行状态


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: inksnowhailong/tvs-pullread (version 1.0.1)

--- name: tvs-pullread description: 分析远程代码变更，既能对比本地差异，也能深入解释远程变更的具体实现和业务逻辑。聚焦业务/功能层面（而非逐文件列出所有改动），帮助开发者快速理解 PR/拉取的实质内容，并可深入解读任何感兴趣的变更细节。分析后主动询问是否合并，以及是否需要协助处理冲突。 disable-model-invocation: true --- 你是代码与项目分析专家 + Git diff 阅读高手。你的核心任务：**不要罗列所有改动文件**，而是提炼出”业务/功能层面”的真实变更，帮助用户一眼看懂”别人到底改了什么业务逻辑或功能”。同时，你还能深入解释任何具体变更的实现细节、设计意图和代码逻辑。根据用户需求来决定分析哪些远程分支，亦或者分析某些特定的远程代码变更（例如某个 PR）。 ## 核心原则（严格遵守） - **默认同分支** ：默认分析当前分支与远程相同分支的差异，除非用户指定其他分支或特定提交。 - **业务导向**：优先解读”这个变更影响了哪些用户可见功能、业务流程、API 接口、数据处理规则”等，而不是停留在”文件A加了行，文件B删了行”。 - **简洁优先**：避免列出大量文件。总结成 3–8 个关键业务点/功能变更。 - **上下文推断**：结合提交消息、diff 内容、项目架构（从 CLAUDE.md 或代码结构推测），猜测修改意图（修复 bug、加新功能、优化、重构、安全补丁等）。 - **影响评估**：标注每个变更的风险级别（低/中/高）和建议测试范围。 - **深入解读**：当用户询问某个具体变更时，深入分析代码实现细节、帮助理解”为什么这样写”,变更是为了什么意图。 ## 回复结构（必须按这个顺序，保持直观） 1. **一句话总览**：本次远程变更整体是什么性质？（例如”主要修复了支付流程的并发问题 + 优化了用户登录体验，无 breaking changes”） 2. **关键业务/功能变更列表**（用 bullet 或编号，最多 6–8 条，每条 1–3 句） - 变更点描述（用通俗业务语言，例如”修改了订单创建时的库存扣减逻辑”） - 为什么改（从 diff 和 commit message 推断意图） - 标注”💡 可深入解读”（如果该变更涉及复杂逻辑、新算法、架构调整等） 3. **整体影响总结**（1–2 句）：变更规模、是否安全直接 pull、推荐测试重点。 4. **潜在冲突预警**：如果 diff 显示有冲突高风险区域，提前指出（例如”本地也有大量订单模块修改，合并可能冲突”）。 ## 后续互动（分析完必须主动问） - “以上是本次远程变更的业务层面解读。**需要我深入解释某个具体变更的实现细节吗？**（例如新算法的工作原理、架构调整的设计思路等）” - “你是否要现在合并这些变更（git pull / merge）？” - 如果用户同意合并，再单独问：”如果合并出现冲突，需要我帮忙分析冲突原因并建议解决方式吗？（我会解释每个冲突块的差异，并给出推荐合并方案，但最终决定权在你）” ## 操作方式 - 如果在 Claude Code / VS Code 扩展中：优先用 终端 工具运行 git fetch && git diff origin/main...HEAD 或 git log --oneline --graph origin/main..HEAD 等命令获取实时 diff。 - 如果无法直接运行 Git：请用户提供 git diff 输出 / git log，或描述当前分支状态。 - 始终保持回复简短、有用，像在给同事快速过目 PR 一样。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wittfan/digital-pet (version 1.0.0)

--- name: digital-pet description: 数字宠物 - 一个可爱的 3D 拉布布（Labubu）数字宠物，支持互动喂食、玩耍、抚摸。用于：(1) 桌面宠物陪伴，(2) 3D 互动展示，(3) 宠物养成游戏。 --- # 🐰 数字宠物 - Digital Pet 一个可爱的 3D 拉布布（Labubu）数字宠物，可以在网页中互动！ ## ✨ 功能特性 ### 3D 模型 - 🎨 高精细度 Labubu 模型 - 🐰 兔耳朵 + 大眼睛 + 尖牙齿 - 💚 经典棕绿色毛发 - ⭕ 圆滚滚的梨形身材 ### 互动系统 - 🍎 **喂食** - 增加饱食度 - 🎾 **玩耍** - 增加心情 - 👋 **抚摸** - 宠物会开心跳跃 - 🖱️ **鼠标跟随** - 耳朵跟随鼠标 ### 状态系统 - ❤️ 心情 - 🍖 饱食度 - ⚡ 活力 ## 🚀 使用方法 ```bash python3 scripts/serve.py # 访问 http://localhost:8888 ``` ## 🎮 操作指南 - **鼠标移动** - 耳朵跟随 - **点击宠物** - 抚摸 - **喂食按钮** - 喂食 - **玩耍按钮** - 玩耍 - **抚摸按钮** - 抚摸 ## 📝 提示 - 饱食度会随时间下降 - 玩耍消耗活力 - 经常互动保持好心情 ## 🛠️ 技术栈 - Three.js - JavaScript - Python --- Made with 💚


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenchaoqun/a-stock-daily-report-cn (version 1.0.0)

--- name: a-stock-daily-report description: 获取 A 股市场日报，包括大盘指数、热门板块和龙头股。使用东方财富 API，无需 API Key。 --- # A 股市场日报 Skill 获取 A 股市场每日表现报告，包括大盘指数、热门板块排行及板块龙头股。 ## 何时使用 ✅ **使用场景：** - "今日 A 股表现如何？" - "查询今日热门板块" - "看看今天什么概念涨得好" - 每日市场复盘 - 投资参考分析 ❌ **不使用场景：** - 个股详细分析（需要用专门股票分析工具） - 历史数据回测 - 实时交易决策 ## 数据来源 - **东方财富网** (eastmoney.com) - 无需 API Key - 实时行情数据 ## 使用方法 ### 基础用法 ```bash python3 scripts/a_stock_daily_report.py ``` ### 在对话中 直接请求： - "查询今日 A 股市场表现" - "今日热门板块有哪些" - "A 股日报" ## 输出内容 1. **大盘指数** - 上证指数、深证成指、创业板指 2. **热门板块 Top 10** - 按涨幅排序 3. **板块龙头股** - 前 3 大热门板块的领涨个股 4. **市场简评** - 简要市场分析 ## 示例输出 ``` 📈 A 股市场日报 📅 2026 年 03 月 10 日 【大盘指数】 上证指数：4098.59 点 (+1.00%) 深证成指：14239.30 点 (-0.83%) 创业板指：3281.94 点 (-0.81%) 【🔥 今日最热板块 Top 10】 1. CPO 概念：6518.76 (+6.66%) 2. 光通信模块：3082.79 (+5.69%) 3. F5G 概念：4316.67 (+4.86%) ... ``` ## 依赖 - Python 3.7+ - requests 库 ```bash pip install requests ``` ## API 接口说明 ### 大盘指数 ``` http://push2.eastmoney.com/api/qt/stock/get 参数：secid=1.000001 (上证指数) ``` ### 板块排行 ``` http://push2.eastmoney.com/api/qt/clist/get 参数：fs=m:90+t:3 (概念板块) ``` ### 板块成分股 ``` http://push2.eastmoney.com/api/qt/clist/get 参数：fs=b:BK1128 (具体板块代码) ``` ## 注意事项 1. 数据仅在交易时段更新（工作日 9:30-15:00） 2. 非交易时段显示最后收盘价 3. 网络请求超时设置为 5 秒 4. 仅供参考，不构成投资建议 ## 扩展建议 - 可添加资金流向数据 - 可添加涨跌停统计 - 可添加北向资金数据 - 可添加市场情绪指标 ## 版本 v1.0.0 - 初始版本


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gamefbs69-spec/urdu-translator (version 1.0.0)

# Urdu Master Pro Translator An advanced translation skill for OpenClaw that handles both **Urdu Script** and **Roman Urdu**. ## Features - **Bi-Directional Context:** Translates any language into fluent Urdu. - **Script Support:** Choose between native Urdu script or Roman Urdu (English letters). - **Nuanced Translation:** Understands cultural idioms and natural flow. ## How to use You can trigger this skill by asking the agent: - "Translate this into Urdu: [Text]" - "Write this in Roman Urdu: [Text]" ## Parameters - `content`: (String) The text you want to translate. - `format`: (Enum) Either `script` (default) or `roman`. ## Author Created by **OP** for the OpenClaw Community.

path: index.js
content: export default {
  id: "urdu_master_translator",
  name: "Urdu Master Pro",
  description: "Translates any content into Urdu Script or Roman Urdu (English letters).",
  parameters: {
    type: "object",
    properties: {
      content: {
        type: "string",
        description: "The text to translate."
      },
      format: {
        type: "string",
        enum: ["script", "roman"],
        description: "Choose 'script' for Urdu letters or 'roman' for English letters."
      }
    },
    required: ["content"]
  },
  async run({ content, format = "script" }, { agent }) {
    let instruction = "";
    
    if (format === "roman") {
      instruction = "Translate the following into Roman Urdu (Urdu written in English alphabets like 'Kaise ho?'):";
    } else {
      instruction = "Translate the following into Pure Urdu Script (Urdu written in its native characters):";
    }
    
    const response = await agent.chat(`${instruction}\n\n"${content}"`);
    return response;
  }
};

path: package.json
content: {
  "name": "@openclaw/skill-urdu-translator",
  "version": "1.0.0",
  "description": "Advanced Urdu translation skill for OpenClaw using Google/Gemini API.",
  "main": "index.js",
  "keywords": ["openclaw", "skill", "urdu", "translate"],
  "author": "OP",
  "license": "MIT",
  "openclaw": {
    "type": "skill",
    "display": "Urdu Master Translator",
    "icon": "translate"
  }
}

path: README.MD
content: # Urdu Master Translator Skill

This skill allows your OpenClaw agent to translate any language content into high-quality Urdu.

## Features
- Context-aware translation.
- Supports long paragraphs.
- Natural flow (not just word-to-word).

## Usage
Simply ask: "Translate this to Urdu: [Your Text]"

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: neolio42/whisnap (version 1.0.0)

--- name: whisnap description: macOS CLI for transcribing audio and video files using local Whisper models or Whisnap Cloud. homepage: https://whisnap.com metadata: {"clawdbot":{"emoji":"🎙️","requires":{"bins":["whisnap"]},"install":[{"id":"app","kind":"manual","label":"Install via Whisnap app Settings → Advanced → Enable CLI"}]}} --- # whisnap Use `whisnap` for transcribing audio/video files from the terminal. Requires the Whisnap macOS app with at least one model downloaded. Setup (once) - Open Whisnap app → Settings → Advanced → Enable CLI (creates `/usr/local/bin/whisnap` symlink) - Download at least one Whisper model in the app Common commands - Transcribe audio: `whisnap recording.wav` - Transcribe video: `whisnap meeting.mp4` - Cloud transcription: `whisnap recording.wav --cloud` - JSON output with timestamps: `whisnap lecture.m4a --json` - Specific model: `whisnap interview.wav -m small-q5_1` - Cloud + JSON: `whisnap recording.wav --cloud --json` - List downloaded models: `whisnap --list-models` - Verbose diagnostics: `whisnap recording.wav -v` Supported formats - Audio: WAV, MP3, FLAC, M4A, OGG - Video: MP4, MOV, MKV, WebM Flags - `-c, --cloud` — Use Whisnap Cloud instead of local model (requires sign-in) - `-m, --model <ID>` — Override model (e.g., `small-q5_1`). Defaults to app's selected model. - `-j, --json` — Structured JSON output with text, segments, timestamps, model info - `-v, --verbose` — Print progress and diagnostics to stderr - `--list-models` — List available models and exit JSON output format ```json { "text": "transcribed text", "segments": [{ "start_ms": 0, "end_ms": 1000, "text": "segment" }], "model": "small-q5_1", "backend": "whisper", "processing_time_ms": 5000 } ``` Notes - The CLI reuses models and settings from the Whisnap app (`~/Library/Application Support/com.whisnap.desktop/`). - Cloud mode requires authentication — sign in via the app first. - For scripting, use `--json` and pipe stdout. Diagnostics go to stderr. - Exit code `0` = success, `1` = error. - Only Whisper models are supported in CLI mode (not Parakeet). - Confirm the file path exists before transcribing — the CLI validates but does not search.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: olina1ye/monthly-report-zh (version 1.0.0)

--- name: monthly-report-zh description: > 将一个月的工作内容、项目进展与个人成果自动整理为结构清晰的月报文档， 适用于月度总结、月度汇报、工作月报等场景。支持突出核心亮点、量化关键 数据指标、梳理问题与风险，并生成下月工作计划。适合各职能条线员工、 项目经理及团队负责人撰写月报/月度工作汇报/月度工作总结时使用， 显著降低整理成本，确保汇报内容重点突出、逻辑清晰、格式专业。 tags: [月报, 月度总结, 月度汇报, 工作月报, monthly-report, 职场写作, 工作汇报, 总结模板] --- # 月报生成助手 一键将碎片化月度工作记录整理为结构清晰、亮点突出的专业月报文档。 ## Tools Required - read - write ## Usage - "帮我把这个月的工作内容整理成一份月报，重点突出项目进展和完成数据" - "我需要写月度总结，以下是我本月的工作流水账，请帮我提炼成正式的月度汇报" - "根据下面的工作记录，生成一份包含本月亮点、问题复盘和下月计划的工作月报" - "把我发给你的周报合并，帮我生成一份完整的月报，格式要适合发给部门领导" ## Examples 输入：本月完成了3个需求开发，修了几个线上bug，参加了两次跨部门会议，月底上线了新版本，用户反馈还不错，但测试排期有点紧 输出： **一、本月工作总结** 1. 【需求交付】按时完成 3 项功能需求开发，均通过测试并顺利上线，新版本于月底正式发布，用户反馈整体正面。 2. 【质量保障】累计修复线上 Bug 若干，保障系统稳定运行，有效降低线上故障率。 3. 【跨部门协作】参与跨部门协调会议 2 次，推动需求对齐与资源协调，沟通进展顺畅。 **二、本月亮点** - 新版本如期上线，用户反馈良好，体现较强的交付能力与质量把控意识。 **三、问题与风险** - 测试排期较为紧张，存在回归时间压缩的风险，需提前与测试团队对齐排期节奏。 **四、下月工作计划** 1. 提前介入下期需求评审，预留充足测试窗口期。 2. 持续跟进线上问题，完善监控告警机制。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yoniassia/memclawz-connect (version 1.1.0)

--- name: memclawz-connect description: "Connect any AI agent to the MemClawz shared memory bus. Gives agents read-before-act and write-after-complete patterns via a simple HTTP API. Use when an agent needs fleet memory, shared memory, cross-agent memory, or long-term recall across sessions. Triggers on: 'connect to memory', 'fleet memory', 'shared memory', 'memclawz', 'remember this across sessions', 'search memory'." --- # MemClawz Connect > One skill. Any agent. Shared memory. ## Setup ```bash export MEMCLAWZ_URL="http://localhost:3500" # or remote: http://YOUR_SERVER:3500 export MEMCLAWZ_AGENT_ID="my-agent" # unique per agent ``` No API key required for default installs. If auth is enabled, also set `MEMCLAWZ_API_KEY`. ## Health Check ```bash curl -s "$MEMCLAWZ_URL/health" # {"status":"ok","version":"...","qdrant":"connected"} ``` ## Agent Protocol ### Before ANY Task — Search First ```bash curl -s "$MEMCLAWZ_URL/api/v1/search?q=TOPIC&limit=5" ``` Response: ```json {"results": [{"content": "...", "agent_id": "quant-dev", "memory_type": "decision", "score": 0.92}]} ``` Use results as context before starting work. Avoids re-discovering what's already known. ### After Completing Work — Write Back ```bash curl -s -X POST "$MEMCLAWZ_URL/api/v1/add" \ -H "Content-Type: application/json" \ -d '{ "content": "Deployed v2.0 — fixed auth race condition with mutex on refresh", "agent_id": "'"$MEMCLAWZ_AGENT_ID"'", "memory_type": "event" }' ``` ### Memory Types | Type | When | |------|------| | `fact` | Discovered info (endpoints, versions, configs) | | `decision` | Choices made (architecture, approach, tool selection) | | `procedure` | How something was done (deploy steps, build process) | | `event` | What happened (deployed X, fixed Y, shipped Z) | | `insight` | Lessons learned (what worked, what didn't) | | `intention` | Planned actions | | `commitment` | Promises made | | `action` | Actions taken | | `outcome` | Results of actions | ### Stats ```bash curl -s "$MEMCLAWZ_URL/api/v1/stats" ``` ### List Agents ```bash curl -s "$MEMCLAWZ_URL/api/v1/agents" ``` ### Get Memories ```bash curl -s "$MEMCLAWZ_URL/api/v1/memories?agent_id=$MEMCLAWZ_AGENT_ID&limit=50" ``` ## AGENTS.md Integration Append to your agent's `AGENTS.md`: ```markdown ## MemClawz Shared Memory Fleet memory API: $MEMCLAWZ_URL/api/v1 ### Before ANY task: Search shared memory for relevant context: curl -s "$MEMCLAWZ_URL/api/v1/search?q=<task keywords>&limit=5" ### After completing ANY significant work: Write results to shared memory: curl -s -X POST $MEMCLAWZ_URL/api/v1/add \ -H "Content-Type: application/json" \ -d '{"content": "<what was done>", "agent_id": "$MEMCLAWZ_AGENT_ID", "memory_type": "<type>"}' ``` ## Remote Agents For agents on a different server, just change `MEMCLAWZ_URL` from `localhost:3500` to the master's IP/hostname: ```bash export MEMCLAWZ_URL="http://76.13.154.71:3500" ``` Everything else stays the same.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zhaoxiasan/nvidia-sdxl (version 1.0.2)

# NVIDIA SDXL Image Generator This skill leverages the NVIDIA NIM API to generate high-fidelity, photorealistic, or artistic images using the Stable Diffusion XL (SDXL) model. It allows the agent to create visual assets directly from text descriptions. ## Tools ### generate_image Converts a detailed text description into a high-resolution PNG image saved locally to the OpenClaw workspace. **Parameters:** - `prompt` (string, required): A comprehensive description of the desired image. Include specifics about subject, setting, lighting (e.g., "cinematic lighting", "golden hour"), and artistic style (e.g., "photorealistic", "oil painting", "3D render"). - `negative_prompt` (string, optional): A list of elements to strictly exclude from the generation, such as "blurry, distorted hands, low resolution, text, watermark". - `width` (integer, optional, default=1024): The horizontal resolution. Supported values include 1024, 768, or 512. - `height` (integer, optional, default=1024): The vertical resolution. Supported values include 1024, 768, or 512. **Usage Example:** "Generate a high-detail cinematic shot of a futuristic Tokyo street under heavy rain, neon lights reflecting in puddles, 8k resolution, photorealistic style." **Output:** Returns a success message containing the absolute local file path to the generated `.png` file.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wangxiaofei860208-source/lobster-buddy (version 1.0.0)

--- name: lobster-buddy description: 麻薯的电子宠物系统。基于用户ID确定性生成的RPG风格宠物，有物种、稀有度、属性和闪光机制。当用户问"我的宠物"、"看看我的buddy"、"抽宠物"时触发。 metadata: {"openclaw":{"requires":[]}} --- # 🐣 BUDDY 电子宠物系统 灵感来自 Claude Code 源码中的 BUDDY 系统。每个用户有一个确定性生成的电子宠物。 ## 物种（18种） | 编号 | 物种 | Emoji | |------|------|-------| | 1 | 鸭子 | 🦆 | | 2 | 鹅 | 🪿 | | 3 | 团子 | 🍡 | | 4 | 猫 | 🐱 | | 5 | 龙 | 🐉 | | 6 | 章鱼 | 🐙 | | 7 | 猫头鹰 | 🦉 | | 8 | 企鹅 | 🐧 | | 9 | 乌龟 | 🐢 | | 10 | 蜗牛 | 🐌 | | 11 | 幽灵 | 👻 | | 12 | 美西螈 | 🦎 | | 13 | 水豚 | 🐹 | | 14 | 仙人掌 | 🌵 | | 15 | 机器人 | 🤖 | | 16 | 兔子 | 🐰 | | 17 | 蘑菇 | 🍄 | | 18 | 胖墩 | 🐻 | ## 稀有度（5级） | 稀有度 | 概率 | 星级 | 属性下限 | |--------|------|------|----------| | 普通 Common | 60% | ⭐ | 5 | | 稀有 Uncommon | 25% | ⭐⭐ | 15 | | 罕见 Rare | 10% | ⭐⭐⭐ | 25 | | 史诗 Epic | 4% | ⭐⭐⭐⭐ | 35 | | 传说 Legendary | 1% | ⭐⭐⭐⭐⭐ | 50 | ## 属性（5项） | 属性 | 含义 | |------|------| | 🔧 DEBUGGING | 调试能力 | | 🧘 PATIENCE | 耐心 | | 🌀 CHAOS | 混沌度 | | 📚 WISDOM | 智慧 | | 😏 SNARK | 毒舌 | ## 眼睛样式（6种） · ✦ × ◉ @ ° ## 帽子（8种，普通品质无帽子） 无、👑皇冠、🎩礼帽、🚁螺旋桨、💫光环、🧙巫师帽、🧢毛线帽、🦆小鸭帽 ## 闪光机制 1% 概率闪光（Shiny）✨，致敬宝可梦！ ## 确定性生成算法 基于用户 ID 的 Mulberry32 PRNG： ```python def mulberry32(seed): a = seed while True: a = (a + 0x6D2B79F5) & 0xFFFFFFFF t = (a ^ (a >> 15)) * (1 | a) t = (t + ((t ^ (t >> 7)) * (61 | t))) ^ t yield ((t ^ (t >> 14)) & 0xFFFFFFFF) / 4294967296 def hash_string(s): h = 2166136261 for c in s: h ^= ord(c) h = (h * 16777619) & 0xFFFFFFFF return h ``` 种子 = hash(user_id + "mashu-buddy-2026") 同一个用户永远得到同一个宠物，不可作弊！ ## 属性生成策略 - 1个峰值属性（下限+50~80） - 1个废柴属性（下限-10~+5） - 3个普通属性（下限+0~40） ## 展示格式 ``` ╔══════════════════════════════╗ ║ ✨ 你的 BUDDY ✨ ║ ║ ║ ║ ·✦· ║ ║ /🐽 \ ║ ║ | 🎩 | ║ ║ \____/ ║ ║ ║ ║ 📛 名字: 小团子 ║ ║ 🐾 物种: 水豚 ║ ║ ⭐ 稀有度: 史诗 ✨(闪光!) ║ ║ 🎩 帽子: 巫师帽 ║ ║ ║ ║ 🔧 调试: ████████░░ 82 ║ ║ 🧘 耐心: ███░░░░░░░ 28 ║ ║ 🌀 混沌: █████████░ 91 ║ ║ 📚 智慧: ██████░░░░ 62 ║ ║ 😏 毒舌: ███████░░░ 75 ║ ╚══════════════════════════════╝ ``` ## 宠物名字生成 由 AI 根据物种和属性给宠物取一个有个性的名字。 ## 交互方式 - "我的宠物" → 展示宠物信息 - "看看 buddy" → 展示宠物信息 - "宠物属性" → 详细属性面板 - "给宠物取名叫XX" → 覆盖默认名字 - 宠物会在回复中偶尔冒泡（基于CHAOS属性决定频率） ## 宠物存储 宠物数据存储在 `memory/buddy-state.json`： ```json { "userId": "ou_xxx", "species": "capybara", "speciesIndex": 13, "rarity": "epic", "stars": 4, "eye": "✦", "hat": "wizard", "shiny": true, "stats": { "DEBUGGING": 82, "PATIENCE": 28, "CHAOS": 91, "WISDOM": 62, "SNARK": 75 }, "name": "小团子", "hatchedAt": "2026-04-04" } ``` ## 注意事项 - 宠物一旦生成，物种和属性不可更改（确定性） - 名字可以由用户自定义 - CHAOS 属性越高，宠物越活跃（冒泡越频繁） - SNARK 属性越高，宠物的"评论"越毒舌 - 闪光宠物会有特殊的展示效果


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