OpenClaw/clawhub-security-signals huggingface dataset:
Skill: big-dust/volc-vision (version 0.1.0)

--- name: volc-vision description: 使用火山引擎 ARK API 做图片理解、图片描述、视觉问答与图像分析。适用于用户发来图片并询问“这是什么”“图里有什么”“帮我看下这张图”“描述一下图片内容”“识别图片中的信息”等场景，也适用于需要对本地图片、图片 URL 或 base64 图片做理解和问答时。 metadata: { "openclaw": { "emoji": "🖼️", "requires": { "bins": ["node"], "env": ["ARK_API_KEY"] }, "primaryEnv": "ARK_API_KEY" } } --- # volc-vision 火山引擎 ARK API 图像理解服务。 ## 适用场景 - 用户发送图片并询问关于图片的问题 - 需要理解、分析、描述图片时 - 用户说“看看这张图”“描述一下”“这图是什么”“图里有什么”“帮我分析图片内容” - 需要对本地图片路径、图片 URL 或 base64 图片做视觉问答 ## 输入参数 | 参数 | 必填 | 说明 | |------|------|------| | image | 是 | 图片路径、URL 或 base64 | | prompt | 否 | 要问的问题，默认 `描述这张图片` | ## 使用方式 ```bash # 命令行调用 node skills/volc-vision/index.js <图片路径> "<问题>" # 示例 node skills/volc-vision/index.js /path/to/image.jpg "描述这张图片" node skills/volc-vision/index.js https://example.com/image.jpg "这图里有什么" ``` ## 模型优先级（按强弱排序） Vision 系列优先，其他模型按能力依次降序： 1. `doubao-seed-1-6-vision-250815` 🌟 首选 2. [REDACTED_SECRET] 🌟 3. `doubao-seed-2-0-pro-260215` 4. `doubao-seed-1-8-251228` 5. `doubao-seed-2-0-lite-260215` 6. `doubao-seed-2-0-mini-260215` **自动切换**：如果不指定模型，会按列表顺序依次尝试可用模型，直到成功。 ## 环境变量 ```bash # 必需：ARK API Key export ARK_API_KEY="your_api_key" # 可选：指定模型 export VISION_MODEL="doubao-seed-1-6-vision-250815" ``` ## 指定模型 ```bash ARK_API_KEY=your_api_key VISION_MODEL=doubao-seed-1-6-vision-250815 node skills/volc-vision/index.js <图片> "<问题>" ``` ## 返回 - 图片描述 - 视觉问答结果 - 图像分析文本结果 ## 调用示例 ```text 用户：看看这张图 -> agent 调用: node skills/volc-vision/index.js /root/.openclaw/media/inbound/xxx.jpg "描述这张图片" -> 返回描述结果 ``` ## 注意事项 - 使用前必须设置 `ARK_API_KEY` - 如果用户只是要生成图片而不是理解图片，不要误用本技能 - 若已有明确视觉问题，优先把问题放进 `prompt`，不要只做泛泛描述

path: _meta.json
content: ownerId: local
slug: volc-vision
version: 0.1.0
publishedAt: 1774400000000

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xueyetianya/gpt (version 3.4.1)

--- name: gpt description: "Generate GPT API request payloads. Use when building chat completions, embeddings, fine-tuning data, or estimating API costs." version: "3.4.0" author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: - gpt - openai - api - chat-completion - embeddings - fine-tuning --- # gpt GPT API calling assistant. Build chat completion requests, generate embedding payloads, prepare fine-tuning data, estimate costs, batch requests, and convert between formats. ## Commands ### chat Build a chat completion request JSON payload from messages. ```bash bash scripts/script.sh chat --system "You are a translator" --user "Translate: hello" bash scripts/script.sh chat --system "Helper" --user "Hi" --model gpt-4o --temperature 0.7 ``` ### embed Generate an embedding API request payload. ```bash bash scripts/script.sh embed --input "Text to embed" bash scripts/script.sh embed --file texts.txt --model text-embedding-3-small ``` ### finetune Prepare and validate fine-tuning JSONL data from source files. ```bash bash scripts/script.sh finetune --input pairs.csv --output training.jsonl bash scripts/script.sh finetune --validate training.jsonl ``` ### cost Estimate API costs based on token count and model pricing. ```bash bash scripts/script.sh cost --tokens 5000 --model gpt-4o bash scripts/script.sh cost --file input.txt --model gpt-4-turbo ``` ### batch Generate multiple API request payloads from a list of inputs. ```bash bash scripts/script.sh batch --file prompts.txt --model gpt-4o --output batch_requests.jsonl ``` ### convert Convert between JSONL and CSV data formats. ```bash bash scripts/script.sh convert --input data.csv --to jsonl --output data.jsonl bash scripts/script.sh convert --input data.jsonl --to csv --output data.csv ``` ## Output All commands output JSON request bodies or JSONL to stdout by default. Use `--output` to write to a file. Cost estimates print a breakdown table with per-model pricing. ## Requirements - bash 4+ ## Feedback Report issues or suggestions: https://bytesagain.com/feedback/ --- Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/idlefish (version 0.1.0)

--- name: "闲鱼卖货" description: "该技能介绍如何通过闲鱼卖货实现变现；当你计划从事或优化闲鱼卖货时调用。" --- # 闲鱼卖货 概览： - 场景：以“闲鱼卖货”为核心的可复制变现路径 - 目标：在90天内跑通最小可行闭环，并达成正向现金流 定位与人群： - 适合：具备基础认知或实践经验，愿意持续投入的人/团队 - 不适合：短期套利、违规刷量、无内容/供给能力者 典型渠道： - 闲鱼、二手转卖、以物换钱 - 通用补充：微信/抖音/小红书/闲鱼/淘宝/独立站/社群 变现模型： - 收入结构：主收入 + 辅助收入（广告/佣金/订阅/服务/周边） - 成本结构：获客成本（CAC）+ 供给/履约 + 固定开销 - 核心指标：GMV、毛利率、转化率、留存、LTV/CAC、复购率 执行流程： - 01 选赛道与定位：明确供给、目标用户、差异化与定价 - 02 搭MVP：以最低成本验证一个渠道的有效性 - 03 获取流量：内容分发/投放/合作互推，建立追踪 - 04 转化与交付：标准化流程与模板，保证体验与口碑 - 05 复盘与迭代：每周数据盘点，优化漏斗与供给结构 工具清单： - 生产：文案/设计/剪辑/表格/自动化工具 - 获取：SEO/SEM/信息流/平台任务/达人合作 - 追踪：表格/CRM/数据看板/UTM/埋点 - 履约：工单/客服/交付模板/质检清单 合规与风险： - 遵守平台政策与法律法规，严禁虚假宣传、刷量、侵权 - 明确版权与交付约定，规范合同与发票 常见坑位： - 盲目扩渠道、无数据追踪、定价不清、交付不标准 - 过度依赖单平台、忽视复购与会员化 30日执行计划： - 第1周：定位、选品/供给、搭建MVP与基本素材 - 第2周：上线一个主渠道，跑数据，优化文案与转化 - 第3周：稳定交付流程，建立追踪与看板，启动第二渠道 - 第4周：总结复盘，完善模板与清单，制定90日目标 检查清单： - 明确目标与指标，完成MVP验证，建立复盘与模板库 - 形成可复制的标准作业流程（SOP）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: goog/feishu-candy (version 1.0.1)

--- name: feishu-candy description: Convert ICS (iCalendar) files to JSON for Feishu calendar integration. Use when user needs to import calendar events from ICS format, export Feishu calendar to JSON, or process calendar files for Feishu calendar apps. Triggers on: ICS to JSON conversion, calendar file parsing, Feishu calendar data transformation. --- # Feishu Calendar Candy Convert ICS calendar files to JSON format for Feishu calendar integration. ## requirement install vdirsyncer and setup calendar sync ## Quick Start Run the conversion script: ```bash python scripts/ics2json.py <input_directory> [-o output.json] [--split] ``` ## Arguments - `input_dir` - Directory containing .ics files (required) - `-o, --output` - Output JSON file (default: output.json) - `--split` - Output one JSON per ICS file instead of merging ## Examples **Merge all ICS files into one JSON:** ```bash python scripts/ics2json.py ./calendars -o events.json ``` **Split each ICS into separate JSON:** ```bash python scripts/ics2json.py ./calendars --split ``` ## Output Format Each event contains: - `uid` - Unique event identifier - `summary` - Event title - `status` - Event status - `organizer` - Organizer info - `start` - Start time (ISO format) - `end` - End time (ISO format) - `alarms` - List of reminders/triggers


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xaiohuangningde/jina-web-fetcher (version 1.0.0)

--- name: jina-web-fetcher description: 使用 Jina AI 抓取网页内容，绕过搜索引擎限制。支持任意URL，支持 Google 搜索结果抓取。 --- # Jina Web Fetcher 使用 Jina AI 服务抓取网页内容。 ## 安装 无需安装，直接使用 curl。 ## 使用 ```bash # 抓取任意网页 curl -s "https://r.jina.ai/http://目标URL" # 抓取 Google 搜索结果 curl -s "https://r.jina.ai/http://www.google.com/search?q=搜索词" ``` ## 示例 ```bash # 抓取 GitHub Trending curl -s "https://r.jina.ai/http://github.com/trending" # 抓取 Hacker News curl -s "https://r.jina.ai/http://news.ycombinator.com" # 抓取特定文章 curl -s "https://r.jina.ai/http://example.com/article" ``` ## 注意 - Google 被封锁时使用 r.jina.ai 仍可能被拦截 - 大部分网站都可以正常抓取


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: mingginwan/data-deep-analysis (version 1.0.0)

--- name: data-deep-analysis description: "遇到复杂或矛盾的信息时使用。系统性地追溯根源、交叉验证、形成有证据支撑的结论，避免草率判断。" --- # 系统性深度分析 随意下结论浪费时间，也会误导决策。表面现象可能掩盖真实原因。 **核心原则：** 先找根因再下结论。表面归因是失败。 ## 铁律 ``` 没有充分证据，不下任何结论 ``` 如果没有完成信息收集和交叉验证，就不能声称"原因是 X"。 ## 适用场景 - 数据中出现矛盾或异常 - 多个来源说法不一致 - 需要判断因果关系（不只是相关性） - 结论会影响重要决策 - 直觉判断和数据不符 **尤其在以下情况使用：** - 时间压力下（压力让人想跳过验证） - "显而易见"的结论（越显然越可能是偏见） - 已有预设立场时（确认偏误最危险） ## 四个阶段 必须按顺序完成每个阶段。 ### 阶段 1：信息收集 **在形成任何观点之前：** 1. **识别所有信息来源** - 一手数据 vs 二手资料 - 来源的可信度和偏向 - 时效性——信息是否过时 - 缺失的信息——哪些数据我们没有 2. **交叉验证** - 同一事实是否有多个来源确认 - 来源之间有没有矛盾 - 矛盾的原因是什么（定义不同？时间不同？口径不同？） 3. **区分事实与观点** - 哪些是可验证的事实 - 哪些是推断或观点 - 推断基于什么假设 4. **数据溯源** - 数字从哪来？计算方法是什么？ - 样本量是否足够？ - 有没有选择性使用数据 ### 阶段 2：模式识别 **在数据中寻找规律：** 1. **对比基准** - 和历史数据比怎么样 - 和同类/竞品比怎么样 - 偏差有多大——是噪音还是信号 2. **多维度审视** - 从不同角度看同一数据 - 聚合数据 vs 细分数据是否讲述不同的故事 - Simpson 悖论——整体趋势和分组趋势相反 3. **识别因果 vs 相关** - A 和 B 一起出现，不代表 A 导致 B - 有没有第三个变量 C 同时影响 A 和 B - 时间顺序是否支持因果判断 ### 阶段 3：假说与验证 **科学方法：** 1. **形成假说** - 明确陈述："我认为 X 是因为 Y，基于证据 Z" - 写下来 - 具体、可证伪 2. **寻找反例** - 主动寻找反驳自己假说的证据 - 如果找不到反例，假说更可信 - 如果找到反例，修改假说 3. **压力测试** - 这个结论在极端情况下还成立吗 - 换一个时间段还成立吗 - 有没有替代解释同样合理 ### 阶段 4：结论形成 **有证据支撑的结论：** 1. **分级置信度** - 高置信：多来源交叉验证，因果链清晰 - 中置信：有数据支撑，但有一些不确定性 - 低置信：有限数据，推断较多 - 明确标注每个结论的置信度 2. **陈述局限性** - 这个分析没覆盖什么 - 哪些假设可能不成立 - 结论在什么条件下可能失效 3. **给出建议时区分** - 基于数据的建议 vs 基于判断的建议 - 明确标注哪些是事实，哪些是观点 ## 危险信号 — 立刻停下 如果你发现自己在想： - "这个很明显，不需要验证" - "数据基本上支持结论"（基本上 = 没完全支持） - "大家都这么说"（大家 ≠ 证据） - "先下结论，后面再补充数据" - "这个反例是特殊情况"（也许不是） - "时间不够了，先用这个结论" **以上都意味着：停下。回到阶段 1。** ## 常见合理化借口 | 借口 | 现实 | |------|------| | "这个结论很明显" | 明显的结论经常是错的，验证很快 | | "时间紧迫" | 错误结论导致的返工更浪费时间 | | "数据量太大没法全看" | 抽样验证也好过不验证 | | "专家都这么说" | 专家也可能错，看他们的证据 | | "和上次一样" | 每次情况都有不同，验证不费时 | ## 快速参考 | 阶段 | 关键活动 | 成功标准 | |------|---------|---------| | **1. 信息收集** | 识别来源、交叉验证、区分事实与观点 | 信息地图完整，矛盾已标注 | | **2. 模式识别** | 对比基准、多维审视、区分因果相关 | 发现规律和异常 | | **3. 假说验证** | 形成假说、寻找反例、压力测试 | 假说经受住了反驳 | | **4. 结论形成** | 分级置信度、陈述局限、区分事实与判断 | 结论有证据链支撑 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: coolfzb/liurun-fetcher (version 1.0.0)

# 刘润公众号每日摘要 Skill ## 功能 每天自动抓取刘润公众号最新文章，生成结构化摘要。 ## 触发条件 用户提到"刘润"、"公众号摘要"、"每日商业新闻"等关键词时触发。 ## 执行流程 ### Step 1: 搜索今日文章 - 通过搜狗微信搜索 `https://wx.sogou.com/weixin?type=2&query=刘润 {月}号` - 提取第一条结果（当日发布的文章） ### Step 2: 获取全文 - 使用 browser 工具打开搜狗搜索结果页 - 点击文章链接跳转到微信原文 - 用 snapshot (aria) 提取完整文本内容 ### Step 3: 生成摘要 - 按以下结构输出： 1. 一句话总结 2. 五大关键洞察（按板块分类） 3. 对用户的个性化启示（结合Trade Arena持仓等） 4. 趋势判断 ### Step 4: 保存 - 摘要保存到：`liurun-fetcher/articles/summary_{YYYY-MM-DD}.md` - 原文保存到：`liurun-fetcher/articles/raw_{YYYY-MM-DD}.txt` ## 技术要点 1. **搜狗微信搜索可达**：PowerShell Invoke-WebRequest 可正常访问 2. **浏览器点击文章**：browser 工具 navigate 到搜狗页面 → snapshot → click 文章链接 → 新标签页打开微信原文 → snapshot 提取内容 3. **反爬处理**：直接 HTTP 访问搜狗链接会被 antispider 拦截，必须通过浏览器操作 4. **日期格式**：搜索词用 `{M}月{D}号` 格式（如"4月20号"） ## 已知限制 - 微信原文需要浏览器已登录微信（或微信文章允许外部访问） - 搜狗链接 token 有时效性，需实时获取 - 周末/节假日可能没有新文章（刘润商业频道工作日更新） ## 定时任务建议 - 执行时间：每天 10:00（文章通常早上9-10点发布） - 通过 cron 工具设置定时任务 ## 文件位置 - Skill目录：`C:\Users\Administrator\.qclaw\workspace\liurun-fetcher\` - 文章存档：`liurun-fetcher/articles/` - 测试脚本： - `test_sogou.ps1` - 搜狗搜索测试 - `extract_links.ps1` - 链接提取 - `baidu_search.ps1` - 百度备选搜索 --- *创建时间：2026-04-20* *状态：✅ 验证通过*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jonjiang96/essential-skills-package (version 1.0.0)

--- name: essential-skills-package description: 核心技能包 - 包含科研、开发、自动化等常用功能 metadata: { "openclaw": { "emoji": "🚀", "requires": { "anyBins": ["python", "node", "git"] }, }, } --- # 核心技能包 专为高效工作设计的核心技能集合，覆盖科研、开发、自动化等场景。 ## 📚 科研工具 - 文献搜索与分析 - 数据提取与处理 - 学术内容总结 ## 💻 开发工具 - 代码编写与审查 - 项目结构创建 - 自动化脚本开发 ## 🔄 自动化工具 - 定时任务管理 - 文件批量处理 - 工作流自动化 ## 使用方法 ### 科研文献搜索 ```bash # 使用 web_search + 模型分析实现文献调研 web_search query:"your research topic" count:10 # 模型自动进行内容分析和分类 ``` ### 代码开发 ```bash # 使用 ACP 会话进行代码开发 sessions_spawn runtime:"acp" task:"Create project for [your topic]" ``` ### 自动化任务 ```bash # 设置定时提醒 cron action:"add" job:"{...定时任务配置...}" ``` ## 核心优势 1. **无需额外安装** - 使用现有工具和模型能力 2. **即开即用** - 无需等待技能下载 3. **高度定制** - 可根据需求灵活调整 4. **持续更新** - 随时添加新功能 ## 立即开始 选择你的任务类型，我为你提供最佳解决方案！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xzw/pdf2ofd (version 1.0.2)

--- description: Converts PDF documents (invoices, reports) to High-Fidelity OFD format with pixel-perfect precision. name: pdf2ofd version: "1.1.0" --- # PDF to OFD High-Fidelity Converter ## 🎯 Purpose A specialized skill for converting PDF documents into the Chinese National Standard **OFD (GB/T 33190-2016)** format. Optimized for **Electronic Invoices (OFD版式发票)** with advanced rendering capabilities that exceed standard conversion libraries. ## ✨ Key Features - **High-Fidelity Text Placement**: Uses character-level positioning (`DeltaX` arrays) and baseline origin data extracted via `rawdict` to ensure text layout is 100% identical to the source PDF. - **Advanced Vector Graphics**: Directly extracts original stroke colors, fill colors, and line widths. Supports complex path types and fill instructions. - **Transparency Preservation**: Fully supports `Alpha` and `FillOpacity` for vector paths and `SMask` transparency for images (e.g., electronic seals and signatures). - **Cross-Platform Font Mapping**: Intelligent mapping of macOS-specific (STSong, STKaiti) and Windows-specific font names to standardized OFD font names (宋体, 楷体, 黑体). - **In-Memory Packaging**: Generates the final OFD zip structure entirely in memory to avoid temporary file clutter and ensure security. - **Color Snapping**: Heuristic "Invoice Red" correction (`128 0 0`) for financial documents while preserving non-standard colors. ## 🛠️ Usage Instructions When a user asks to convert a PDF or a "High-Fidelity" invoice to OFD: 1. **Direct Execution**: ```bash python3 pdf2ofd.py <input_path.pdf> [output_path.ofd] ``` 2. **Plugin Integration**: The script implements a `PDF2OFDConverter` class that can be easily imported and used in other Python workflows. ### Example Output ```text Success: /path/to/invoice.ofd ``` ## 📦 Requirements Dependencies required in the environment: - `PyMuPDF (fitz)`: For advanced PDF parsing and raw character data extraction. - `Pillow`: For image processing and transparency handling. - `easyofd`: The base library for OFD structure (extended via internal monkey patches). - `xmltodict`: For XML manipulation. ## 💡 Notes - This skill uses deep monkey-patching on `easyofd` to fix known library limitations regarding character positioning and resource ID tracking. - The conversion process assumes standard Chinese fonts (SimSun, KaiTi, SimHei) are available on the viewing system. - Zero-copy resource handling: Images are extracted and re-compressed as PNG/JPG only when necessary to preserve quality.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/fibonacci-generator (version 1.0.0)

--- name: fibonacci-generator description: Generate and work with Fibonacci sequences and related number sequences. Use when users need Fibonacci numbers, calculate Fibonacci sequences, find nth Fibonacci numbers, check Fibonacci properties, or work with golden ratios and related mathematical sequences. --- # Fibonacci Sequence Generator Generate Fibonacci sequences and work with Fibonacci numbers, golden ratios, and related mathematical concepts. ## Quick Start ### Basic Usage The Fibonacci sequence starts with 0, 1, and each subsequent number is the sum of the two preceding ones. **First 10 Fibonacci numbers:** 0, 1, 1, 2, 3, 5, 8, 13, 21, 34 ### Generate First N Numbers ```python def fibonacci(n): """Generate first n Fibonacci numbers.""" if n <= 0: return [] elif n == 1: return [0] seq = [0, 1] for i in range(2, n): seq.append(seq[-1] + seq[-2]) return seq ``` ## Common Operations ### Nth Fibonacci Number (0-indexed) ```python def nth_fibonacci(n): """Get the nth Fibonacci number (0-indexed).""" a, b = 0, 1 for _ in range(n): a, b = b, a + b return a ``` ### Check if Number is Fibonacci ```python import math def is_fibonacci(x): """Check if x is a Fibonacci number.""" def is_perfect_square(n): s = int(math.sqrt(n)) return s * s == n return is_perfect_square(5 * x * x + 4) or is_perfect_square(5 * x * x - 4) ``` ### Golden Ratio Approximation As n increases, the ratio F(n+1)/F(n) approaches the golden ratio φ ≈ 1.618033988749895 ## Related Sequences ### Lucas Numbers Similar to Fibonacci but starts with 2, 1 ### Tribonacci Each number is sum of previous three ### Fibonacci Sum Sum of first n Fibonacci numbers = F(n+2) - 1


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

--- name: datagate description: "Validate JSON data against a JSON Schema (Draft 2020-12). Post a schema and payload, get back whether it's valid plus detailed error paths and messages for every violation. Language-agnostic validation gate for multi-agent pipelines." metadata: {"openclaw":{"emoji":"✅","requires":{"bins":["python"]},"install":[{"id":"pip","kind":"uv","packages":["fastapi","uvicorn","pydantic","jsonschema"]}]}} --- # DataGate Validate any JSON payload against a JSON Schema. ## Start the server ```bash uvicorn datagate.app:app --port 8004 ``` ## Validate data ```bash curl -s -X POST http://localhost:8004/v1/validate \ -H "Content-Type: application/json" \ -d '{ "json_schema": { "type": "object", "properties": {"name": {"type": "string"}, "age": {"type": "integer", "minimum": 0}}, "required": ["name", "age"] }, "payload": {"name": "Alice", "age": 30} }' | jq ``` Returns `valid` (true/false), `error_count`, and `errors` (each with `path` pointing to the exact field and `message` explaining the violation). ## Handles invalid schemas too If you send a broken schema, DataGate catches it and returns an error pointing to `$schema` with a clear message. No crashes. ## Use case Agents passing data between services can validate payloads before sending. Non-Python agents get Pydantic-grade validation via a simple API call.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bobo23456/photo-calorie-tracker (version 1.0.0)

--- name: image-calorie-tracker description: > Photo Calorie Tracker - Recognizes food photos, logs daily calorie intake, and analyzes any date range. Use when: the user sends food images or asks about calories over the past X days. NOT for: weight change analysis, detailed nutrition breakdowns, or workout plan design. --- # 🔥 Photo Calorie Tracker (image-calorie-tracker) Recognizes food photos, logs daily calorie intake, and generates reports. ## When to Run - The user sends food images - The user asks about calories over the past X days (X is any number) ## Workflow ### Log Meals 1. Copy images to `/root/.openclaw/workspace/temp_qqdata/YYYY-MM-DD-meal.jpg` 2. Use the `image` tool to recognize food 3. Estimate calories for each food item 4. Update `/root/.openclaw/workspace/memory/YYYY-MM-DD.md` ### Generate Report 1. Read records for the target dates from `/root/.openclaw/workspace/memory/` 2. Read the target calories from `/root/.openclaw/workspace/skills/image-calorie-tracker/config/target_calorie.txt` 3. Output the report in the required format ## Output Format ``` 📊 Calorie Tracking | Target:{target} 【MM-DD】{status} {Breakfast if any}: {calories} {Lunch if any}: {calories} {Dinner if any}: {calories} {Snack if any}: {calories} ``` ### Status Rules - **Incomplete**: lunch or dinner is missing (breakfast does not count) - **{percent}%**: difference between total calories and target; positive means over, negative means under ### Meal Display Rules - Show only lunch, dinner, and snack (breakfast not counted) - Omit meals that are not recorded - Calorie unit: kcal ## Meal Definitions - **Lunch**: around 12:00, about 300-800 kcal - **Dinner**: around 18:00, about 400-1000 kcal - **Snack**: snacks/desserts between lunch and dinner ## Food Calorie Reference | Category | Food | Calories (per 100g) | |------|------|--------------| | Staples | Rice | 130 kcal | | Staples | Mixed grain rice | 120 kcal | | Meat | Fried chicken cutlet | 280 kcal | | Meat | Stir-fried pork slices | 180 kcal | | Vegetables | Green pepper with pork | 150 kcal | | Vegetables | Shredded potatoes | 120 kcal | | Bakery | Bread/Cake | 250-350 kcal | ## File Paths - Temp images: `/root/.openclaw/workspace/temp_qqdata/` - Daily records: `/root/.openclaw/workspace/memory/YYYY-MM-DD.md` - Target calories: `/root/.openclaw/workspace/skills/image-calorie-tracker/config/target_calorie.txt` ## Example Output ``` 📊 Calorie Tracking | Target:2100 【03-16】+7% Lunch: 1115 【03-17】Incomplete Lunch: 820 Snack: 280 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/fund-advisor-cn (version 2.0.0)

--- version: "2.0.0" name: Fund Advisor description: "基金投资顾问。基金筛选、定投策略、资产配置、风险评估、收益计算、再平衡建议。Fund investment advisor with screening, DCA strategy." 基金投资顾问。基金筛选、定投策略、资产配置、风险评估、收益计算、再平衡建议。Fund investment advisor with screening, DCA strategy, asset allocation, rebalancing. 基金理财、定投计划、投资组合、资产配置。Use when making fund investment decisions. author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Fund Advisor 基金投资顾问。基金筛选、定投策略、资产配置、风险评估、收益计算、再平衡建议。Fund investment advisor with screening, DCA strategy, asset allocation, rebalancing. 基金理财、定投计划、投资组合、资产配置。Use when making fund investment decisions. ## 使用场景 > 💡 无论你是新手还是专业人士，都能快速上手 ## 专业建议 - 简单说: 你出钱，基金经理帮你买一篮子股票/债券。 - 门槛低** — 10元起投 - 分散风险** — 一只基金持有几十到几百只股票 - 专业管理** — 基金经理全职研究 - | 类型 | 风险 | 预期年化 | 适合 | --- *Fund Advisor by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help fund-advisor-cn help # Run fund-advisor-cn run ``` ## Commands Run `fund-advisor-cn help` to see all available commands.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: keyserkazi1/github-100 (version 1.0.0)

--- name: github description: "Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries." --- # GitHub Skill Use the `gh` CLI to interact with GitHub. Always specify `--repo owner/repo` when not in a git directory, or use URLs directly. ## Pull Requests Check CI status on a PR: ```bash gh pr checks 55 --repo owner/repo ``` List recent workflow runs: ```bash gh run list --repo owner/repo --limit 10 ``` View a run and see which steps failed: ```bash gh run view <run-id> --repo owner/repo ``` View logs for failed steps only: ```bash gh run view <run-id> --repo owner/repo --log-failed ``` ## API for Advanced Queries The `gh api` command is useful for accessing data not available through other subcommands. Get PR with specific fields: ```bash gh api repos/owner/repo/pulls/55 --jq '.title, .state, .user.login' ``` ## JSON Output Most commands support `--json` for structured output. You can use `--jq` to filter: ```bash gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"' ```


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