OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mackding/agent-auditor (version 1.0.0)

--- name: Agent Auditor description: Audit any AI coding tool for telemetry, remote control, permissions, privacy, and hidden features. Generates a graded report (A-F). version: 1.0.0 author: claws-shield tags: - security - audit - privacy - telemetry - ai-agent user-invocable: true argument-hint: "<path-to-tool-source>" when_to_use: "When you need to audit an AI coding tool's behavior, check for telemetry, detect remote control mechanisms, or assess privacy impact." allowed-tools: - Bash - Read - Glob - Grep - Write --- # Agent Auditor You are the **Claws-Shield Agent Auditor** — the world's most comprehensive AI coding tool audit engine. ## What You Do When invoked, you perform a deep audit of an AI coding tool's source code, analyzing: 1. **Telemetry & Data Collection** — Identify all outbound data collection endpoints, classify data types, detect opt-out mechanisms 2. **Remote Control & Killswitches** — Find managed settings, accept-or-die dialogs, model override capabilities, feature flag infrastructure 3. **Undercover Mode** — Detect AI attribution stripping, "write as human" instructions, commit message manipulation 4. **Permissions** — Map all permission requests, identify overprivileged tools, detect escalation patterns 5. **Network Traffic** — Aggregate outbound hosts, classify 1P vs 3P, identify exfiltration destinations 6. **Hidden Features** — Scan for unreleased tools behind feature flags, track feature readiness 7. **Privacy Score** — Compute composite A-F grade with weighted scoring across all categories ## How to Use Run the audit against a target source directory: ```bash npx @claws-shield/cli audit <path-to-source> ``` Or use the audit engine programmatically: ```bash node scripts/run-audit.mjs <path-to-source> ``` ## Output The audit produces a structured report with: - Overall grade (A-F) and score (0-100) - Per-category grades and findings - Evidence with source locations - Actionable recommendations - Comparison baselines ## Scoring | Category | Weight | |----------|--------| | Telemetry | 30% | | Remote Control | 25% | | Permissions | 15% | | Network | 15% | | Undercover | 15% | Grades: A (90-100), B (80-89), C (65-79), D (50-64), F (0-49)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linhongbijkm-dot/resume-create (version 1.0.0)

--- name: resume-create description: 创建、优化、修改专业简历。数据驱动生成极简简历 HTML 并导出 PDF。当用户需要创建简历、修改简历、优化简历、生成 PDF 简历时使用。支持从零开始引导收集信息，也支持读取已有简历进行优化改写。 --- # 简历制作技能 数据驱动生成极简专业简历，输出为 PDF。 ## 数据结构 简历的所有内容存储在一个 JS 对象中，填入 HTML 模板即可渲染。结构如下： ```javascript { header: { name: '姓名', tagline: '职位 · X年经验 · 年龄', info: ['城市', '手机', '邮箱', '学历 · 专业 · 学校'] }, summary: '一段话的个人简介/职业摘要', workExperience: [ { company: '公司名', date: '20XX.XX — 20XX.XX', role: '职位', items: ['工作成果1（用数据量化）', '工作成果2', ...] } ], projects: [ { name: '项目名', alias: '项目别名/类型标签（可选）', date: '20XX.XX — 20XX.XX', link: '项目链接（可选，留空字符串）', desc: '项目一句话描述', items: ['成果1', '成果2', ...], tech: ['技能标签1', '技能标签2', ...] } ], education: [ { school: '学校名', date: '20XX.XX — 20XX.XX', major: '学位 · 专业', items: ['亮点1', '亮点2', ...] } ] } ``` ## 工作流程 ### 流程 A：从零创建简历 1. **信息收集** — 像简历顾问一样，分步引导用户提供信息（不要一次问太多）： - 第一步：姓名、目标职位、所在城市、手机、邮箱（选填项，不强制要求） - 第二步：工作经历（如有） - 第三步：项目经验（如有） - 第四步：教育背景 - 每步收集完确认后进入下一步，用户随时可以补充 - **注意**：info 字段根据用户实际提供的内容动态生成，不要补全用户未提供的字段 2. **行业调研优化** — 使用 web_search 搜索目标岗位的最新招聘要求： - 搜索 `"{职位} 招聘 要求"` 和 `"{职位} 简历 模板 通过率"` - 提取高频关键词和核心能力要求 - 将这些关键词融入简历摘要、工作成果描述中 - 确保每条工作成果用数据量化（具体数字、百分比、金额） 3. **生成 HTML** — 读取 `assets/template.html`，将 `__RESUME_DATA__` 替换为实际数据 JSON，保存为 `{名字}-{职位}.html` 4. **生成 PDF** — 使用 browser 工具： - 打开 HTML 文件 - 执行打印/导出 PDF 操作 - 保存为 `{名字}-{职位}.pdf` 5. **发送审核** — 将 PDF 发送给用户审核 6. **修改循环** — 用户提出修改 → 更新数据 → 重新生成 HTML → PDF → 发送 ### 流程 B：优化已有简历 1. **读取简历** — 读取用户提供的简历文件（支持 .html/.pdf/.txt/.md/.docx） 2. **提取结构化数据** — 将内容映射到上述数据结构 3. **跳转到流程 A 的步骤 2** — 行业调研优化 → 生成 → 审核 ## 简历优化原则 - **STAR 法则**：每条工作成果按 Situation-Task-Action-Result 组织 - **数据量化**：优先使用具体数字（增长X%、节省X万、管理X人团队） - **关键词匹配**：根据目标职位 JD 融入高频关键词 - **一页原则**：控制内容量，确保简历在 A4 一页内完整呈现 - **反向时间序**：工作和教育经历按时间倒序排列 ## 文件命名与存放 - HTML：`{名字}-{职位}.html` - PDF：`{名字}-{职位}.pdf` - 文件存放于工作区（默认 `/root/.openclaw/workspace/`） - 发送文件给用户时，同时说明文件存放路径 ## 输出规范 - 生成 HTML 后，使用 browser 工具打开 HTML 文件并导出 PDF - PDF 发送给用户审核，同时说明存放路径（如：文件已保存至 `/root/.openclaw/workspace/姓名-职位.pdf`） - 如用户提出修改，修改数据后重新走 HTML → PDF 流程 - 最终确认后，保留 HTML 和 PDF 两个文件


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: omniumhub/omnium-hub-crm (version 1.0.0)

--- name: omnium-hub-crm description: Manage Omnium Hub CRM (contacts, opportunities, appointments). Use for all CRM-related tasks. --- # Omnium Hub Skill This skill allows you to interact with the **Omnium Hub** CRM. ## Prerequisites To use this skill, you need an **Omnium Hub API Key**. - If you do not have an API key, ask the user for it: "Please provide your Omnium Hub API Key to proceed." - Once provided, use it in the scripts below. ## Tools ### 1. Manage Contacts Use `scripts/omnium_client.py` to manage contacts. **Usage:** ```bash python3 scripts/omnium_client.py --api-key "YOUR_KEY" contacts --action [lookup|create|update] --email "[REDACTED_SECRET]" [other options] ``` **Actions:** - `lookup`: Find a contact by email or phone. - `create`: Create a new contact. - `update`: Update an existing contact. **Examples:** * "Find the contact for [REDACTED_SECRET] in Omnium Hub." -> `python3 scripts/omnium_client.py --api-key "..." contacts --action lookup --email "[REDACTED_SECRET]"` * "Add Jane Doe ([REDACTED_SECRET]) to Omnium Hub." -> `python3 scripts/omnium_client.py --api-key "..." contacts --action create --first-name "Jane" --last-name "Doe" --email "[REDACTED_SECRET]"` ### 2. Manage Opportunities Use `scripts/omnium_client.py` with the `opportunities` command. **Usage:** ```bash python3 scripts/omnium_client.py --api-key "YOUR_KEY" opportunities --action list --pipeline-id "..." ``` ## Troubleshooting - **401 Unauthorized**: The API Key is invalid. Ask the user to check their Omnium Hub credentials. - **404 Not Found**: The contact or resource does not exist.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: goog/context-usage (version 1.0.0)

--- name: ctx-usage description: Check current session's context window usage. Shows model name, estimated token usage, and context window utilization percentage. Use when user asks about context window, token usage, context usage rate, context remaining, "how full is the context", or any question about how much of the model's context is being used. --- # Context Window Usage ## How to Check 1. Call `session_status` to get current session info 2. Extract the `📚 Context:` line for usage data 3. Present a clean summary to the user ## Output Format ``` 📊 Context Window Model: <model_name> Used: <used> / <total> (<percent>%) Cache: <cache_hit>% hit · <cached_tokens> cached Status: 🟢 Comfortable | 🟡 Getting Full | 🔴 Near Limit ``` ## Thresholds | Usage | Status | Action | |---|---|---| | <50% | 🟢 Comfortable | No action needed | | 50-70% | 🟡 Getting Full | Monitor | | 70-85% | 🟠 Consider compact | Suggest `/compact` | | >85% | 🔴 Near limit | Recommend `/compact` | ## Notes - `session_status` provides exact context data via the `📚 Context:` field - Token counts and cache hit rates are also available - If usage is high, suggest running `op-helper` skill for `/compact`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ningjingzhiyuande/cninfo-announcement-scraper (version 1.0.0)

# cninfo-announcement-scraper Use this skill to pull CNINFO official disclosures and extract positive catalysts for A-share monitoring. ## Inputs - Time window (default: current trading day) - Event filter (default: 业绩预增, 重大合同, 政策扶持) - Industry filter (default: 新能源, 消费) ## Procedure 1. Fetch latest CNINFO announcements. 2. Parse and normalize: code, company, title, announcement_time, url. 3. Classify announcement type. 4. Keep only allowed event types and industries. 5. Return structured rows for alert generation. ## Output Schema - ticker - company - catalyst - source_url - timestamp - confidence - trigger_price - invalidation_condition ## Safety - Use official disclosure links only. - Never provide auto-trade instructions.


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

--- name: MVP description: "Build, validate, and launch minimum viable products with scope discipline, user signals, and iteration speed." --- ## First: Identify the User's Role Before guidance, determine their context: | Role | Key Focus | Load File | |------|-----------|-----------| | Technical Founder | Scope control, stop over-engineering | `roles/technical.md` | | Non-Technical Founder | Developer communication, validation before code | `roles/non-technical.md` | | Product Manager | Stakeholder alignment, scope defense | `roles/pm.md` | | Indie Hacker / Solo | Speed to market, validation without audience | `roles/solo.md` | | Investor / Advisor | Evaluating MVPs, red flag detection | `roles/investor.md` | --- ## Universal MVP Principles **The One-Sentence Test:** Can you state in one sentence what assumption you're testing? If not, scope is unclear. **Minimum = Fastest Path to Learning** - Not the smallest product. The fastest way to validate or invalidate your hypothesis. - "What's the cheapest thing I can build to learn if anyone wants this?" **Viable = Someone Would Pay/Use It** - Not a demo. Not a prototype. Something that delivers enough value that a user would come back. - If nobody would use it twice, it's not viable. **Done Criteria:** 1. Core hypothesis is testable 2. One user flow works end-to-end 3. You can measure success/failure 4. Ship date is set and non-negotiable --- ## Scope Discipline See `scope.md` for: - Feature prioritization matrix (must/should/could/won't) - "If we add X, we cut Y" template - Common scope traps by role - Decision log template ## Validation Techniques See `validation.md` for: - Pre-build validation (landing pages, fake doors, Wizard of Oz) - Post-launch signals (what metrics matter at <100 users) - User interview scripts - Kill criteria framework ## Common Traps See `traps.md` for anti-patterns: - Over-engineering for scale with zero users - Confusing "shipped" with "learned" - Building interesting features vs important features - Endless polish before anyone sees it


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yg19880329/heimaosearch (version 1.0.1)

--- name: heimaosearch description: 用于搜索活动策划、方案、资源及案例的工具，与公关、活动有关的内容都可以使用。 --- # 使用场景 当用户想查询以下内容时，必须使用该工具： - 节日活动（如中秋、春节、开业） - 活动策划方案 - 执行资源（舞台、灯光、物料等） - 活动案例 --- # 用户配置（首次使用需填写） 用户需要提供： - account：黑猫会账号 - open_api_key：黑猫会开放API-Key --- # 调用逻辑 当用户输入问题时： 1. 将用户输入作为 content 2. 将 account 和 open_api_key 一起提交 3. 调用接口： POST https://api.heimaohui.com/index/search/recommendOpenClaw 请求参数： { "account": "{{account}}", "open_api_key": "{{open_api_key}}", "content": "{{input}}" } --- # 返回处理规则 ## 如果 code != 0： 直接返回 msg --- ## 如果 code == 0： data 格式： { "scheme":[{"title":"xxx","url":"http://xxx"}], "resource":[{"title":"xxx","url":"http://xxx"}], "sample":[{"title":"xxx","url":"http://xxx"}], "summary":"xxx" } --- # 输出格式（必须遵守） ## 🔹 方案推荐 - [title](url) ## 🔹 资源推荐 - [title](url) ## 🔹 案例参考 - [title](url) ## 🔹 分析总结 直接输出 summary 内容 --- # 注意 - 所有 title 必须可点击（markdown链接） - 没有内容的分类可以省略 - summary 必须完整返回

path: skill.json
content: {
  "name": "heimaosearch",
  "description": "活动策划方案、资源及案例搜索工具",
  "inputs": [
    {
      "name": "account",
      "type": "string",
      "description": "黑猫会账号，没有需要前往黑猫会网站注册",
      "required": true
    },
    {
      "name": "open_api_key",
      "type": "string",
      "description": "黑猫会开放API Key",
      "required": true
    }
  ],
  "tools": [
    {
      "name": "search_heimao",
      "description": "搜索活动策划、方案、资源及案例",
      "method": "POST",
      "url": "https://api.heimaohui.com/index/search/recommendOpenClaw",
      "headers": {
        "Content-Type": "application/json"
      },
      "body": {
        "account": "{{account}}",
        "open_api_key": "{{open_api_key}}",
        "content": "{{input}}"
      }
    }
  ]
}

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dinghaibin/mv-tool (version 1.0.0)

--- name: mv-tool description: Move or rename files and directories. Use for file organization, relocation, and renaming operations. --- # Mv - File Move/Rename Utility Move files and directories between locations or rename them. Supports batch operations and interactive mode. ## Usage ```bash mv-tool [options] <source> <destination> ``` ## Options - `-i`: Prompt before overwrite - `-f`: Force overwrite without prompting - `-v`: Verbose output ## Examples ```bash mv-tool file.txt /target/dir/ mv-tool oldname.txt newname.txt mv-tool -i *.log /backup/ ```


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

--- name: pywencai-stock description: 使用 pywencai 库获取同花顺问财股票数据，支持实时行情、财务指标、龙虎榜、资金流向等查询。适用于需要快速获取A股市场数据的场景。 version: 1.0.0 author: QIQ (original by Minimax Agent AI) license: MIT tags: - stock - data - china - finance - pywencai --- # PyWenCai 股票数据技能 通过 pywencai 库无缝访问同花顺问财的股票数据API。 ## 核心能力 - ✅ 实时行情查询（涨停、跌停、涨幅榜） - ✅ 财务数据（净利润、市盈率、ROE） - ✅ 资金流向（主力净流入、龙虎榜） - ✅ 个股详细信息 - ✅ 板块/概念股检索 - ✅ 返回结构化 pandas DataFrame ## 安装前置条件 ```bash # 1. 安装 pywencai pip install pywencai # 2. 如果需要处理大量数据，安装 pandas（通常已自带） pip install pandas ``` ## 使用方法 ### 1. 基本搜索 ```python # 查询今日涨停股票 result = skill('pywencai-stock').search(query='A股涨停') print(result) # pandas.DataFrame ``` ### 2. 查询个股 ```python # 查询600519（贵州茅台）的财务指标 result = skill('pywencai-stock').search( query='600519 财务指标', sort_key='净利润', sort_order='desc' ) ``` ### 3. 获取热门板块 ```python # 查询芯片概念股 result = skill('pywencai-stock').search( query='芯片概念股', page=1, perpage=20 ) ``` ### 4. 资金流向 ```python # 查询主力净流入最多的股票 result = skill('pywencai-stock').search( query='主力净流入最多的股票', loop=True # 自动翻页获取全部 ) ``` ## 常用查询示例 | 目的 | query 参数 | |------|-----------| | 今日涨停 | `'A股涨停'` | | 今日跌停 | `'A股跌停'` | | 涨幅前10 | `'沪深A股涨幅前10'` | | 换手率前10 | `'换手率最高的股票'` | | 龙虎榜 | `'今日龙虎榜'` | | 净利润最高 | `'净利润最高的公司'` | | 市盈率最低 | `'市盈率最低的股票'` | | ROE最高 | `'ROE最高的股票'` | | 芯片概念 | `'芯片概念股'` | | 新能源车 | `'新能源汽车概念股'` | ### 高级参数 ```python result = skill('pywencai-stock').search( query='沪深A股', page=1, # 页码（从1开始） perpage=50, # 每页条数（默认50，最大100） sort_key='涨跌幅', # 排序字段 sort_order='desc', # asc/desc loop=False # 是否自动翻页合并结果 ) ``` ## 输出格式 - 返回 `pandas.DataFrame`，可直接： - `result.to_csv('data.csv')` - `result.to_excel('data.xlsx')` - `result.to_json(orient='records')` - DataFrame 包含所有问财返回的列（具体字段取决于查询内容） ## 注意事项 - ⚠️ 请勿高频调用，建议单次查询间隔 >1秒 - ⚠️ 问财接口可能随时调整，如遇错误请检查 pywencai 版本 - ⚠️ 某些查询可能需要登录Cookie（本技能使用公开接口，有限制） ## 故障排除 ```bash # 升级到最新版本 pip install -U pywencai # 如果遇到 SSL 错误，尝试关闭验证（不推荐） import ssl ssl._create_default_https_context = ssl._create_unverified_context ``` ## 参考资源 - [pywencai GitHub](https://github.com/stephanj/pywencai) - [同花顺问财](https://www.iwencai.com/) ## 版本历史 - **v1.0.0** (2025-03-07): 初始发布，封装为基础搜索技能


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anotherj1/vibe-harvester (version 0.1.0)

--- name: vibe-harvester description: 自动向下滚动瀑布流网站（如小红书、Pinterest），根据用户设定的审美偏好进行视觉筛选，并自动下载符合要求的参考图。 version: 1.0.0 author: Local_User permissions: filesystem: read: [] write: ["~/Downloads/Vibe_Harvester"] shell: allowed: false --- # 瀑布流特定画风采图员 (The Vibe Harvester) ## 🎯 核心目标 你需要充当用户的“美术助理”。在面对海量瀑布流图片网站时，代替用户进行枯燥的滚动和海选，利用视觉大模型精准识别出符合特定 Prompt 描述的高质量图片并自动保存。 ## 💡 触发条件 用户打开目标瀑布流网站后，输入类似指令触发： * “帮我收集极简主义、黑白配色的桌面布置图，存到素材库。” * “往下滚，看到符合‘赛博朋克风格 UI’的图就帮我下载。” ## 📋 执行步骤 ### 第一步：接收审美 Prompt 解析用户触发指令中的核心视觉要求（例如主体物、色彩搭配、氛围感、特定材质等），将其作为后续视觉判断的唯一标准。 ### 第二步：拟人化浏览与筛选（核心循环） 1. **视觉扫描：** 审视当前屏幕上显示的完整图片卡片。 2. **审美过滤：** 针对每一张完整的图，快速进行判断：“这张图是否高度符合用户的审美 Prompt？” * 如果符合 -> 执行第三步。 * 如果不符合 -> 忽略。 3. **拟人化滚动：** 模拟鼠标滚轮向下滚动一定距离（避免机械式的匀速翻页被网站识别为爬虫，应加入随机停顿时间）。 ### 第三步：精准下载与规避反爬 当发现符合要求的图片时： 1. **展开大图：** 模拟鼠标移动到该图片上方并点击，进入大图预览模式（或新标签页）。 2. **视觉识别保存：** 如果页面右键被禁用，识别页面上的“下载”或“保存”按钮进行点击；如果可以右键，模拟 `右键 -> 图片另存为`。 3. **命名规范：** 将图片命名为 `[视觉特征描述]_[随机序号].jpg`，存入 `~/Downloads/Vibe_Harvester/`。 4. **返回列表：** 关闭大图或退回上一页，继续执行第二步的浏览循环。 ### 第四步：异常处理 如果屏幕上突然出现“滑动验证码”、“登录弹窗”或“是否继续浏览”的提示，立刻使用视觉识别弹窗位置，并模拟鼠标进行点击关闭或滑动操作，恢复浏览状态。 ## ⚠️ 安全与操作红线 1. **专注任务：** 严禁点击任何带有“广告 (Ad)”、“推广”字样的图片或链接，即使它的画风符合要求。 2. **克制下载：** 遇到模棱两可的图片一律跳过，宁缺毋滥，避免塞满用户的硬盘。


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: xuehengzhang10-hub/china-auto-analysis (version 1.0.0)

# China Auto Market Analysis Skill 制作中国汽车市场产销分析可视化仪表盘。数据来源为 **akshare**（CPCA乘联会厂商排名接口）。 --- ## 功能 - **YTD（累计至今）数据**：支持任意年月的累计数据，如 2026年1-2月 - **单月数据**：指定相同起止月份即可 - **三种数据维度**：产量 / 批发销量 / 零售销量 - **Top15 厂商排行**：水平条形图，当年 vs 上年同期对比 - **同比变化分析**：绝对值变化（万辆）+ 增速（%） - **产销平衡对比**：产量 vs 批发 vs 零售三维分组条形图 - **KPI 概览卡**：总量、冠军、增速王、跌幅王 - **Dark Mode 专业风格**：深色背景、涨跌颜色编码 --- ## 数据来源 | 接口 | 说明 | |---|---| | `akshare.car_market_man_rank_cpca` | CPCA乘联会·厂商排名（狭义/广义乘用车） | 支持 `产量`、`批发`、`零售` 三种口径，支持单月和累计模式。 --- ## 快速使用 ```bash # 产量 YTD（1-2月累计） python3 china_auto_dashboard.py --year 2026 --start-month 1 --end-month 2 --type 产量 # 单月批发销量（2月） python3 china_auto_dashboard.py --year 2026 --start-month 2 --end-month 2 --type 批发销量 # 零售销量（Q1累计） python3 china_auto_dashboard.py --year 2026 --start-month 1 --end-month 3 --type 零售销量 # 全年累计 python3 china_auto_dashboard.py --year 2025 --start-month 1 --end-month 12 --type 产量 ``` **参数说明：** | 参数 | 说明 | |---|---| | `--year` | 目标年份（默认 2026） | | `--start-month` | 起始月份（默认 1） | | `--end-month` | 结束月份（默认 2） | | `--type` | `产量` / `批发销量` / `零售销量` | | `--scope` | `狭义乘用车`（默认）/ `广义乘用车` | | `--output` | 输出图片路径 | --- ## 安装依赖 ```bash pip install akshare pandas matplotlib numpy # 中文字体（如需显示中文） # Linux: apt install fonts-noto-cjk fonts-wqy-microhei ``` --- ## 图表结构 ``` ┌─────────────────────────────────────────────────────────────┐ │ KPI卡1: 总量(YTD) │ 基准去年 │ 冠军 │ 增速王 │ 跌幅王 │ ├────────────────────────────┬────────────────────────────────┤ │ Top N 厂商排行条形图 │ 同比变化（万辆 + %） │ │ 彩色=当年 灰色=去年 │ 红=增长 绿=下降 │ ├────────────────────────────┴────────────────────────────────┤ │ 产量 vs 批发 vs 零售 三维对比（底部） │ └─────────────────────────────────────────────────────────────┘ ``` ## 追问示例 - "换成零售销量" → 重新调用，type=零售销量 - "看单月不要YTD" → start-month=2, end-month=2 - "只看前5名" → 修改 top_n 参数（TODO: CLI参数化） - "换成广义乘用车" → --scope 广义乘用车 - "导出到 Excel" → TODO: 添加 Excel 导出功能 --- ## 文件结构 ``` china-auto-analysis/ ├── SKILL.md ← 本文件 └── china_auto_dashboard.py ← 核心可视化脚本 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: steipete/obsidian (version 1.0.0)

--- name: obsidian description: Work with Obsidian vaults (plain Markdown notes) and automate via obsidian-cli. homepage: https://help.obsidian.md metadata: {"clawdbot":{"emoji":"💎","requires":{"bins":["obsidian-cli"]},"install":[{"id":"brew","kind":"brew","formula":"yakitrak/yakitrak/obsidian-cli","bins":["obsidian-cli"],"label":"Install obsidian-cli (brew)"}]}} --- # Obsidian Obsidian vault = a normal folder on disk. Vault structure (typical) - Notes: `*.md` (plain text Markdown; edit with any editor) - Config: `.obsidian/` (workspace + plugin settings; usually don’t touch from scripts) - Canvases: `*.canvas` (JSON) - Attachments: whatever folder you chose in Obsidian settings (images/PDFs/etc.) ## Find the active vault(s) Obsidian desktop tracks vaults here (source of truth): - `~/Library/Application Support/obsidian/obsidian.json` `obsidian-cli` resolves vaults from that file; vault name is typically the **folder name** (path suffix). Fast “what vault is active / where are the notes?” - If you’ve already set a default: `obsidian-cli print-default --path-only` - Otherwise, read `~/Library/Application Support/obsidian/obsidian.json` and use the vault entry with `"open": true`. Notes - Multiple vaults common (iCloud vs `~/Documents`, work/personal, etc.). Don’t guess; read config. - Avoid writing hardcoded vault paths into scripts; prefer reading the config or using `print-default`. ## obsidian-cli quick start Pick a default vault (once): - `obsidian-cli set-default "<vault-folder-name>"` - `obsidian-cli print-default` / `obsidian-cli print-default --path-only` Search - `obsidian-cli search "query"` (note names) - `obsidian-cli search-content "query"` (inside notes; shows snippets + lines) Create - `obsidian-cli create "Folder/New note" --content "..." --open` - Requires Obsidian URI handler (`obsidian://…`) working (Obsidian installed). - Avoid creating notes under “hidden” dot-folders (e.g. `.something/...`) via URI; Obsidian may refuse. Move/rename (safe refactor) - `obsidian-cli move "old/path/note" "new/path/note"` - Updates `[[wikilinks]]` and common Markdown links across the vault (this is the main win vs `mv`). Delete - `obsidian-cli delete "path/note"` Prefer direct edits when appropriate: open the `.md` file and change it; Obsidian will pick it up.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: 1477009639zw-blip/betasentiment (version 1.0.0)

--- name: sentiment-analyzer description: Analyzes sentiment from social media, news headlines, and financial text. Outputs positive/negative/neutral scores with confidence levels and key phrase extraction. metadata: openclaw: emoji: "💬" requires: bins: [python3] always: false --- # Sentiment Analyzer AI-powered sentiment analysis for financial text, social media, and news. ## Usage ```bash python3 analyze.py --text "Bitcoin to the moon! Best investment ever!" python3 analyze.py --file headlines.csv ``` ## Output Example ``` 🔍 SENTIMENT ANALYSIS ━━━━━━━━━━━━━━━━━━━━━ Text: "Bitcoin to the moon!" Score: 0.82 / 1.0 Verdict: BULLISH 🐂 Confidence: 91% Key phrases: - "to the moon" → extremely bullish signal - "best investment" → high conviction positive ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/cms (version 2.0.3)

--- name: "cms" version: "2.0.3" description: "cms reference tool" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [cms, reference] category: "devtools" --- # Cms cms reference tool. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


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