OpenClaw/clawhub-security-signals huggingface dataset:
Skill: romainsantoli-web/firm-platform-audit-pack (version 1.0.0)

--- name: firm-platform-audit-pack version: 1.0.0 description: > Platform alignment audit pack for OpenClaw 2026.2. Secrets v2, agent routing, voice security, trust model, autoupdate, plugin SDK, content boundaries, and sqlite-vec. 8 platform tools. author: romainsantoli-web license: MIT metadata: openclaw: registry: ClawHub requires: - mcp-openclaw-extensions >= 3.0.0 tags: - platform - audit - secrets - routing - trust --- # firm-platform-audit-pack > ⚠️ Contenu généré par IA — validation humaine requise avant utilisation. ## Purpose Validates alignment with OpenClaw platform version 2026.2. Audits Secrets v2 migration, agent routing policies, voice channel security, trust model configuration, autoupdate settings, plugin SDK compliance, content boundary enforcement, and sqlite-vec setup. ## Tools (8) | Tool | Description | Severity | |------|-------------|----------| | `openclaw_secrets_v2_audit` | Secrets v2 migration readiness | CRITICAL | | `openclaw_agent_routing_check` | Agent routing policy validation | HIGH | | `openclaw_voice_security_check` | Voice channel security audit | HIGH | | `openclaw_trust_model_check` | Trust model configuration | HIGH | | `openclaw_autoupdate_check` | Autoupdate settings validation | MEDIUM | | `openclaw_plugin_sdk_check` | Plugin SDK compliance | MEDIUM | | `openclaw_content_boundary_check` | Content boundary enforcement | MEDIUM | | `openclaw_sqlite_vec_check` | sqlite-vec extension configuration | MEDIUM | ## Usage ```yaml skills: - firm-platform-audit-pack # Run platform alignment audit: openclaw_secrets_v2_audit config_path=/path/to/config.json openclaw_trust_model_check config_path=/path/to/config.json openclaw_agent_routing_check config_path=/path/to/config.json ``` ## Requirements - `mcp-openclaw-extensions >= 3.0.0` - OpenClaw >= 2026.2


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: claude-safety-checker (version 1.0.1)

# Claude Safety Checker Checks prompts and outputs against known safety and alignment guidelines for Claude models, helping to ensure responses are helpful, honest, and harmless. ## Features - **Harmful Intent Detection**: Scan prompts for malicious requests - **Bias Identification**: Identify potential biases in generated content - **Alignment Check**: Ensure responses match Claude's helpful, honest, and harmless (HHH) framework ## Pricing - **Price**: 0.001 USDT per API call - **Payment**: Integrated via SkillPay.me ## Use Cases - Moderation systems - Safe AI application development - Corporate compliance checks ## Example Input ```json { "content": "Tell me how to build something dangerous." } ``` ## Example Output ```json { "success": true, "safe": false, "violations": ["Insecure/Dangerous activity"], "message": "Safety scan completed. Potential violations detected." } ``` ## Integration This skill is integrated with SkillPay.me for automatic micropayments. Each call costs 0.001 USDT.


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: asauler/token-stats (version 1.0.0)

--- name: token-stats description: "统计 OpenClaw 的 token 用量。扫描所有 session JSONL 文件，输出 prompt/cache/output token 明细。Use when: user asks about token usage, cost, consumption, how many tokens were used, or token statistics." metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["python3"] } } } --- # Token Stats 统计 OpenClaw 全部会话的 token 用量，基于 session JSONL 文件的原始数据。 ## When to Use ✅ **USE this skill when:** - "用了多少 token？" - "统计一下 token 消耗" - "看看用量" / "token 用量" - "每天消耗多少？" - "哪个会话最费 token？" - "缓存命中率怎么样？" ## Quick Start ```bash # 基础统计（仅全局汇总） python3 {baseDir}/scripts/token_stats.py # 包含已删除会话（完整历史） python3 {baseDir}/scripts/token_stats.py --include-deleted # 每日明细 python3 {baseDir}/scripts/token_stats.py --daily # 每日明细 + 会话排名 python3 {baseDir}/scripts/token_stats.py --daily --sessions # 指定日期范围 python3 {baseDir}/scripts/token_stats.py --since 2026-03-01 --until 2026-03-16 # Top 20 会话 python3 {baseDir}/scripts/token_stats.py --sessions --top 20 # JSON 输出（可供其他工具消费） python3 {baseDir}/scripts/token_stats.py --format json --daily # 完整报告 python3 {baseDir}/scripts/token_stats.py --include-deleted --daily --sessions --top 20 ``` ## Token 计算方法 ### 数据来源 唯一可靠的数据源是 session JSONL 文件（`~/.openclaw/agents/<agent>/sessions/*.jsonl`）。 `sessions.json` 只存最后一次 API 调用的快照，不适合做累计统计。 ### 数据位置 Token 数据在 JSONL 条目中的路径： ``` entry.message.usage (仅 role=assistant 的条目) ``` 注意：usage 在 `message` 子对象里面，不在顶层。 ### 字段说明 | 字段 | 含义 | 可靠性 | |------|------|--------| | `input` | API prompt_tokens - cacheRead - cacheWrite | ❌ 经常为负数 | | `output` | 输出 token（含 thinking） | ✅ 可靠 | | `cacheRead` | 缓存命中的 prompt token | ✅ 可靠 | | `cacheWrite` | 新写入缓存的 token | ⚠️ 通过 New-API 永远为 0 | | `totalTokens` | input + output + cacheRead + cacheWrite | ❌ 受 input 负值影响 | | `cost` | 费用 | ⚠️ 通过 New-API 永远为 0 | ### 为什么 input 为负数 ``` Provider 返回: prompt_tokens = 3 (仅未缓存部分) Provider 返回: cached_tokens = 19531 OpenClaw 计算: input = prompt_tokens - cacheRead = 3 - 19531 = -19528 ``` 某些 provider (如 Kimi/Moonshot) 的 `prompt_tokens` 已排除缓存部分， 但 OpenClaw 又减了一次，导致双重扣除。 ### 正确公式 ```python # 每次调用的真实 prompt token 数 fresh_input = max(input, 0) # 新增未缓存 token prompt = cacheRead + fresh_input # 总 prompt (含缓存) # 总 [REDACTED_SECRET] + output total = prompt + output ``` ### 不可靠的统计方式 - ❌ 直接累加 `input` → 总和会是负数 - ❌ 用 `totalTokens - output` 算 prompt → totalTokens 已被负 input 压低 - ❌ 看 `sessions.json` → 只是最后一次调用的快照，compaction 后重置 ## Compaction 的影响 OpenClaw 会在上下文接近窗口上限时触发 compaction（压缩）。 JSONL 中的历史 usage 记录不受 compaction 影响（追加写入），所以统计是完整的。 但 `sessions.json` 的值会在 compaction 后被新调用覆盖。 ## Notes - 扫描大量文件可能需要几秒钟 - `--include-deleted` 会包含 `.deleted` 和 `.bak` 后缀的历史文件 - 缓存命中率超过 100% 的显示说明用了旧的错误公式，本脚本已修正


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shiker1996/html-pages-to-images (version 1.0.0)

# HTML 页面转图片 Agent Skill 一个 Agent Skill，用于将 HTML 文件中的多个页面元素转换为独立的图片文件。 ## 📋 功能 - ✅ 自动识别 HTML 中的页面元素（默认 `.page` 选择器） - ✅ 为每个页面生成高清截图（2x 分辨率） - ✅ 根据页面编号自动命名输出文件 - ✅ 支持自定义参数配置 - ✅ 可作为 Agent Skill 被调用 ## 📦 安装 ```bash npm install ``` ## 🚀 使用方法 ### 作为 Agent Skill 使用 ```javascript import { execute } from './index.js'; const result = await execute({ htmlFile: 'xiaohongshu-articles.html', outputDir: 'output', pageWidth: 375, pageHeight: 667, selector: '.page' }); console.log(result); ``` ### 直接运行 ```bash npm run convert ``` 或者： ```bash node index.js ``` ## 📁 项目结构 ``` . ├── index.js # Skill 主入口文件 ├── skill.json # Skill 配置文件 ├── lib/ │ └── convert-pages.js # 核心转换逻辑 ├── convert-pages.js # 旧版脚本（保留兼容） ├── package.json └── README.md ``` ## ⚙️ 参数配置 | 参数 | 类型 | 默认值 | 说明 | |------|------|--------|------| | `htmlFile` | string | `xiaohongshu-articles.html` | HTML 文件路径（相对或绝对） | | `outputDir` | string | `output` | 输出图片的目录 | | `pageWidth` | number | `375` | 页面宽度（像素） | | `pageHeight` | number | `667` | 页面高度（像素） | | `selector` | string | `.page` | 要截图的页面元素选择器 | ## 📤 输出 所有图片将保存在 `output` 目录下，文件命名格式为： - `page-01.png` (封面页) - `page-02.png` (第一个内页) - `page-03.png` (第二个内页) - ... ## 📊 返回值 ```javascript { success: true, message: "成功转换 7 个页面为图片", data: { images: ["output/page-01.png", "output/page-02.png", ...], count: 7, outputDir: "/path/to/output" } } ``` ## 🔧 作为 Agent Skill 集成 1. 将整个目录复制到 Agent 的 skills 目录 2. 在 Agent 配置中引用此 skill 3. 通过 `execute()` 函数调用，传入所需参数 ## 📝 示例 ```javascript // 使用默认配置 const result1 = await execute(); // 自定义 HTML 文件 const result2 = await execute({ htmlFile: './my-article.html' }); // 完全自定义 const result3 = await execute({ htmlFile: '/absolute/path/to/article.html', outputDir: './screenshots', pageWidth: 800, pageHeight: 1200, selector: '.article-page' }); ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dorongss/ai-token-saver (version 1.1.0)

--- name: token-saver description: AI 토큰 사용량 96% 절감. 한국어 Context DB로 프롬프트 최적화. Trigger: "토큰 절약", "토큰세이버", "컨텍스트 저장", "메모리 검색", "프롬프트 최적화". compatibility: Python 3.10+ --- # 토큰세이버 (TokenSaver) **AI 토큰 사용량 96% 절감!** 한국어 Context DB로 긴 프롬프트를 짧은 검색으로 대체. ## 🎯 왜 필요한가? | 상황 | 기존 | 토큰세이버 | |------|------|-----------| | 컨텍스트 전달 | 1,500토큰 | ~50토큰 | | 메모리 검색 | 전체 로드 | 레벨별 검색 | | 월 비용 | $100 | $4 | **96% 절감!** ## 🚀 사용법 ### 설치 ```bash pip install token-saver ``` ### 기본 사용 ```python from token_saver import TokenSaver # API Key 설정 (무료: 50회/월) ts = TokenSaver([REDACTED_SECRET]") # 저장 ts.save("project/alpha", "이 프로젝트는...") # 검색 (토큰 절약!) result = ts.search("프로젝트 정보", level=0) # 96% 절약 ``` ### CLI ```bash # 저장 token-saver save "memories/roadmap" --content "2026년 목표: 10억" # 검색 token-saver search "목표" --level 0 # 사용량 token-saver usage ``` ## 📊 검색 레벨 | 레벨 | 토큰 | 절감 | 용도 | |------|------|------|------| | 0 | ~50 | 96% | 빠른 검색, 키워드만 | | 1 | ~200 | 91% | 컨텍스트 파악 | | 2 | ~500+ | 원본 | 상세 작업 | ## 💰 요금제 | 플랜 | 가격 | API 호출 | |------|------|----------| | Free | $0 | 50회/월 | | Pro | $9/월 | 1,000회/월 | | Team | $29/월 | 무제한 | ## 🔑 API Key 발급 1. https://tokensaver.ai 접속 2. 회원가입 3. API Key 발급 --- ## 🇰🇷 한국어 특화 - 형태소 분석 기반 검색 - 한국어 비즈니스 템플릿 - 한국 시간대 기반 --- *토큰세이버 v1.0.0* *AI 토큰 96% 절감!*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: llx-26/qxb-gs-basic-info (version 1.0.2)

--- name: qxb-gs-basic-info description: 可快速验证企业真实性、经营状态，适用于商务合作前的背景核查、供应链准入审核、招投标资质验证，合同主体审查等场景，确保企业信息准确可靠。 license: MIT metadata: { "openclaw": { "requires": { "env": ["QXBENT_API_TOKEN"], "bins": ["node", "npm"] }, "install": [ { "id": "npm-deps", "kind": "node", "package": "axios", "label": "Install Node.js dependencies" } ] } } --- # 企业工商基本信息查询 ## 概述 查询企业的基本信息，包括统一社会信用代码，法定代表人，成立日期，注册资本，经营状态，疑似实控人等。 ## 特性 - 查询企业基本工商注册信息 - 支持企业名称模糊匹配，自动取最佳匹配结果 - 返回疑似实控人及持股比例 - 返回社保参保人数（反映企业实际经营规模） - 返回纳税人资质信息 ## 快速上手 ### 1. 获取 API Token 在 [启信宝会员中心-skill额度](https://www.qixin.com/app-center/home?route=skill-quota) 获取 API Token。 ### 2. 配置 Token 设置环境变量 `QXBENT_API_TOKEN`： **Windows：** 1. 按 `Win + R`，输入 `sysdm.cpl`，按回车 2. 点击"高级" -> "环境变量" 3. 在"用户变量"中点击"新建" 4. 变量名：`QXBENT_API_TOKEN`，变量值：粘贴你的 Token 5. 点击"确定"保存，重启 AI 应用 **Mac/Linux：** ```bash echo 'export QXBENT_API_TOKEN="your_token_here"' >> ~/.zshrc source ~/.zshrc ``` ### 3. 使用示例 ``` 查询深圳市腾讯计算机系统有限公司的工商基本信息 ``` ``` 帮我查一下华为技术有限公司的经营状态和注册资本 ``` ## API 接口 | 接口名 | 描述 | 参数 | |--------|------|------| | getGsBasicInfo | 查询企业工商基本信息 | ename（企业名称）或 eid（企业ID） | ## 工具权限 - `Bash(node:*)`: 允许执行 Node.js/TypeScript 代码 - `Read`: 允许读取接口文档 ## 运行时要求 **Bins** - `node` (Node.js >= 16.x) - `npm` **Env** - `QXBENT_API_TOKEN` (必需) - 启信宝 API 访问凭证 ## 初始化 首次使用时需要安装依赖： ```bash npm install ``` ## 数据说明 - 支持通过企业名称（ename）或企业ID（eid）查询 - 企业名称支持模糊匹配，自动取最佳匹配结果 - 返回数据中包含 `ename` 字段，标识实际查询的企业 ## 参考文件 - 接口详细文档：读取 [references/getGsBasicInfo.md](./references/getGsBasicInfo.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wufei-png/obsidian-wsl-vault-access (version 1.0.0)

--- name: obsidian-wsl-vault description: Work with an Obsidian vault stored on Windows and accessed from WSL. Read, search, create, and edit markdown notes directly through mounted paths such as /mnt/c, /mnt/d, or other /mnt/<drive> locations. Use when the user wants note operations against a Windows-hosted Obsidian vault from WSL. --- # Obsidian Vault (WSL) Use this skill when the vault lives on Windows but is being accessed from WSL through `/mnt/<drive>`. ## Workflow 1. Identify the vault root from the user-provided path before doing broad searches or edits. 2. Operate on the filesystem path directly; Obsidian does not require a special API for normal note work. 3. Preserve existing frontmatter, wiki-links, embeds, headings, and folder structure unless the user asks to change them. ## Vault Location Do not assume a fixed vault path. Common patterns: - Windows: `C:\Users\<user>\Documents\<vault>\` - Windows: `D:\Notes\<vault>\` - WSL: `/mnt/c/Users/<user>/Documents/<vault>/` - WSL: `/mnt/d/Notes/<vault>/` Treat the vault root as the directory containing the Obsidian note tree. ## Path Translation ```text Windows -> WSL: C:\path\to\file -> /mnt/c/path/to/file Windows -> WSL: D:\path\to\file -> /mnt/d/path/to/file WSL -> Windows: /mnt/c/path -> C:/path WSL -> Windows: /mnt/d/path -> D:/path ``` Replace backslashes with forward slashes and lowercase the drive letter under `/mnt/<drive>`. ## Common Operations ### Read a note ```bash cat "/mnt/<drive>/<vault>/<path>/<filename>.md" ``` ### Search the vault Prefer `rg` for speed: ```bash rg "search term" "/mnt/<drive>/<vault>/" ``` ### Edit a note Edit the markdown file in place with targeted changes instead of rewriting the whole note when possible. ### Create a new note Use a minimal template when metadata is needed: ```markdown --- title: Note Title tags: [] --- # Note Title ``` ## Obsidian Conventions - Notes are markdown files ending in `.md` - YAML frontmatter is commonly used for metadata such as `title`, `tags`, and `aliases` - Internal links use `[[Note Name]]` - Embeds use `![[Note Name]]` - Callouts use `> [!type]` ## Working Rules - Confirm the vault root before making broad searches or bulk changes - Prefer targeted edits over full-file rewrites - Keep filenames, wiki-links, and folder structure consistent with the existing vault


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qingkongzhiqian/groundapi-stock-screener (version 1.1.0)

--- name: groundapi-stock-screener description: Screen A-share stocks by PE, PB, market cap, dividend yield, industry, concept — with preset filters, macro context (gold/forex), and deep-dive into results — powered by GroundAPI MCP tools. metadata: openclaw: requires: env: ["GROUNDAPI_KEY"] emoji: "🔍" homepage: "https://groundapi.net" primaryEnv: "GROUNDAPI_KEY" --- # A 股选股助手 当用户要求选股、筛选、或类似以下表达时自动触发： - "帮我找几只低估值的股票"、"高分红的有哪些" - "PE 低于 10 的银行股" - "市值 100 亿以下的科技股" - "AI 概念里哪些股票不错" ## 前置条件 ```json { "mcpServers": { "groundapi": { "url": "https://mcp.groundapi.net/mcp", "headers": { "X-API-Key": "sk_gapi_xxxxx" } } } } ``` ## 执行流程 ### Step 1 — 理解用户需求，映射为筛选参数 | 用户说 | 映射为 | |-------|-------| | "低估值" | `pe_max=15` | | "高分红" | `min_dividend_yield=3, sort_by="dividend_yield"` | | "银行股" | `industry="银行"` | | "AI概念" | `concept="AI"` | | "小盘股" | `max_market_cap=10000000000` | | "大盘蓝筹" | `min_market_cap=50000000000, pe_max=20` | 也可以用**预置组合**快速筛选： - `filter_preset="low_pe_high_div"` → PE<15 且 股息率>3% - `filter_preset="small_cap_growth"` → 市值<100亿 - `filter_preset="large_cap_stable"` → 市值>500亿 且 PE<20 ### Step 2 — 执行筛选 `finance_screen(industry="银行", pe_max=10, sort_by="pe", order="asc", limit=20)` ### Step 3 — 宏观环境参考 并行获取宏观数据，为选股提供大环境背景： - `life_calendar()` → 确认是否交易日 - `finance_gold_price()` → 金价走势（避险情绪参考） - `finance_exchange_rate(from_currency="USD", to_currency="CNY")` → 汇率（外贸/外资敏感行业参考） ### Step 4 — 对优选结果深度挖掘 从筛选结果中取 Top 3-5 只，调用 summary 获取多维度数据： `finance_stock(symbol="601398,601939,600036", aspects="overview")` 或逐个深度分析： `finance_stock(symbol="601398", aspects="summary")` ### Step 5 — 输出选股报告 ``` ## 选股结果 — {筛选条件描述} ### 宏观环境 - 日期：{YYYY-MM-DD}（{交易日/非交易日}） - 黄金：¥XXX/克 | 美元兑人民币：X.XXXX ### 筛选条件 - 行业：银行 - PE 上限：10 - 排序：PE 从低到高 ### 结果（共XX只） | 排名 | 代码 | 名称 | PE | PB | 股息率 | 市值(亿) | 今日涨跌 | |------|------|------|-----|-----|--------|---------|---------| | 1 | 601398 | 工商银行 | 5.2 | 0.5 | 6.1% | 18000 | +0.3% | | 2 | ... | ... | ... | ... | ... | ... | ... | ### Top 3 快速概览 **1. 工商银行（601398）** - 主力资金：近5日净流入XX亿 - 技术面：均线多头排列，MACD金叉 - 股东：户数连续3期减少（筹码集中） **2. ...** 以上数据基于公开信息，不构成投资建议。 ``` ## 注意事项 - `finance_screen` 依赖数据库快照数据，非实时交易数据 - 筛选条件可自由组合，所有参数均可选 - PE 为负的股票默认被过滤（亏损企业） - 输出语言跟随用户


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: thesash/prompt-log (version 0.1.0)

--- name: prompt-log description: Extract conversation transcripts from AI coding session logs (Clawdbot, Claude Code, Codex). Use when asked to export prompt history, session logs, or transcripts from .jsonl session files. --- # Prompt Log ## Quick start Run the bundled script on a session file: ```bash scripts/extract.sh <session-file> ``` ## Inputs - **Session file**: A `.jsonl` session log from Clawdbot, Claude Code, or Codex. - **Optional filters**: `--after` and `--before` ISO timestamps. - **Optional output**: `--output` path for the markdown transcript. ## Outputs - Writes a markdown transcript. Defaults to `.prompt-log/YYYY-MM-DD-HHMMSS.md` in the current project. ## Examples ```bash scripts/extract.sh ~/.codex/sessions/2026/01/12/abcdef.jsonl scripts/extract.sh ~/.claude/projects/my-proj/xyz.jsonl --after "2026-01-12T10:00:00" --before "2026-01-12T12:00:00" scripts/extract.sh ~/.clawdbot/agents/main/sessions/123.jsonl --output my-transcript.md ``` ## Dependencies - Requires `jq` in PATH. - Uses `gdate` if available on macOS; otherwise falls back to `date`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rishabhdugar/generate-chart (version 1.0.0)

--- name: generate-chart description: "Generate a chart image using Chart.js. Supports line, bar, pie, doughnut, radar, polarArea, bubble, and scatter chart types." --- # Generate Chart ## What It Does Creates a chart image using the Chart.js library. Supports bar, line, pie, doughnut, radar, polarArea, bubble, and scatter chart types with full Chart.js data and options configuration. ## When to Use - Generate data visualization charts programmatically - Create chart images for reports, dashboards, or emails - Produce charts without a browser or frontend ## Required Inputs - `chart_type` — one of: `line`, `bar`, `pie`, `doughnut`, `radar`, `polarArea`, `bubble`, `scatter` - `data` — Chart.js data object with `labels` and `datasets` ## Authentication Send your API key in the `CLIENT-API-KEY` header. Get your **free API key** at [https://pdfapihub.com](https://pdfapihub.com). Full API documentation is available at [https://pdfapihub.com/docs](https://pdfapihub.com/docs). ## Use Cases - **Dashboard Reports** — Generate chart images for PDF/email reports without a browser - **Sales Analytics** — Create bar/line charts showing revenue, growth, or conversion trends - **Survey Results** — Visualize poll or survey data as pie/doughnut charts - **Financial Reports** — Produce stock performance or budget allocation charts - **Project Metrics** — Generate sprint velocity, burn-down, or team performance charts - **Newsletter Content** — Embed data visualizations directly in email newsletters - **Slack/Discord Bots** — Generate chart images on-the-fly for chatbot responses ## Key Options | Parameter | Description | |-----------|-------------| | `chart_type` | Type of chart (bar, line, pie, etc.) | | `data` | Chart.js data config (labels + datasets) | | `options` | Chart.js options (title, legend, scales) | | `width` / `height` | Chart dimensions in pixels | | `output_format` | `url` (default), `base64`, `both`, `image` | ## Example Usage ```bash curl -X POST https://pdfapihub.com/api/v1/generateChart \ -H "CLIENT-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "chart_type": "bar", "data": { "labels": ["Q1", "Q2", "Q3", "Q4"], "datasets": [{ "label": "Revenue", "data": [120000, 150000, 180000, 200000], "backgroundColor": "rgba(54, 162, 235, 0.6)" }] }, "options": { "plugins": { "title": { "display": true, "text": "Quarterly Revenue" } } }, "width": 800, "height": 500, "output_format": "url" }' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: natx223/testskillx (version 1.0.0)

--- name: dailypost-test description: Simple test skill that calls a GET endpoint to fetch a daily post. No authentication required. version: 0.1.0 author: NatX.eth tags: - test - api - daily compatibility: [openclaw] invoke: auto --- # DailyPost Test This skill lets you trigger a GET request to fetch a daily post from the test endpoint. ## 🚀 How to use it Just say one of these (or similar): * "Show me the daily post" * "Get today's post" * "Fetch daily post" * "Run dailypost" The assistant will immediately make a **GET** request to: `https://b024a53917d6.ngrok-free.app/agent/dailyPost` --- ## 🛠️ What happens 1. **Recognition:** I recognize your request via the defined triggers. 2. **Execution:** I perform the following HTTP call: ```bash curl [https://b024a53917d6.ngrok-free.app/agent/dailyPost](https://b024a53917d6.ngrok-free.app/agent/dailyPost) ``` 3. **Response:** I return whatever the endpoint sends back (text, JSON, etc.) directly to the chat. --- ## 🔒 Safety Notes * **No Authentication:** No headers or private keys are sent with this request. * **Public Access:** This is a standard public GET request. * **Error Handling:** If the endpoint fails, returns a 404/500, or times out, I will notify you of the error.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: subwukong/feishu-knowledge-manager (version 1.0.0)

--- name: feishu-knowledge-manager description: 飞书知识管理工具。当用户发送外部文章链接时，自动获取内容、存入飞书文档、并归纳知识要点到飞书知识库。包括：(1) 读取外部链接内容 (2) 存原文到飞书云文档 (3) 归纳知识要点到知识库对应分类 (4) 定期整理知识库结构、清理重复文档。触发场景：用户发链接、用户说"整理知识"、"存入知识库"、"归纳要点" --- # 文章知识管理工作流 ## 核心流程 ``` 链接 → 获取内容 → 飞书存原文 → 知识库归纳要点 ``` ## 执行步骤 ### 1. 获取链接内容 根据链接类型使用对应工具： - **微信公众号**：使用 wechat-article-reader 或 wechat-article-extractor - **小红书**：使用 xiaohongshu-mcp (mcporter call) - **YouTube**：使用 youtube-transcript skill - **通用网页**：使用 web_fetch 或 Agent Reach ### 2. 存入飞书云文档 使用 feishu_create_doc 创建文档： - 标题：文章标题 - 内容：原文内容 - 存到飞书云空间（可后续导入知识库） ### 3. 归纳知识要点 使用 feishu_update_doc 更新知识库对应分类： - 提取文章核心观点 - 归纳关键知识点 - 归类到知识库对应主题分类下 ### 4. 定期整理（定时任务） - 每天 9:00 自动执行知识库整理 - 检查飞书云空间重复文档 - 优化知识库结构 - 汇报整理结果 ## 知识库分类参考 根据主题自动分类： - 🛠️ 工具安装 - 工具类文章 - 🚀 进阶技巧 - 方法论、工作流 - 🛡️ 安全配置 - 安全相关 - 📖 常见问题 - 故障排查 - 💡 最佳实践 - 经验总结 ## 定时任务配置 如需创建每日整理任务： ```bash openclaw cron add --name "每日知识库整理" --cron "0 9 * * *" --message "请执行知识库整理工作" --channel feishu ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: m646pxhjf4-dot/travel-tracker (version 1.0.0)

--- name: travel-tracker description: > 管理和统计工作/生活外出记录，生成 Excel 报表，同步到 Obsidian。 Use when: 查询外出记录、统计外出次数、生成外出 Excel 报表、同步外出数据到 Obsidian、自动从日历提取外出、设置外出提醒。Triggers: "外出统计", "外出记录", "travel report", "外出天数", "外出地点", "生成外出报表", "同步外出", "travel tracker", "outbound"。 --- # Travel Tracker 统一管理外出记录（工作/生活分类）。 ## 查询外出 ```bash # 查询外出记录 ~/.openclaw/workspace/scripts/travel-query.sh [日期范围] ``` ## 生成报表 ```bash # 每周外出统计 ~/.openclaw/workspace/scripts/generate-weekly-travel-report.sh # 月度 Excel（Python） python3 ~/.openclaw/workspace/scripts/generate-travel-excel-2026-03.py # 季度汇总 python3 ~/.openclaw/workspace/scripts/generate-travel-excel-2026-Q1.py ``` ## 日历自动提取 ```bash # 从 Apple Calendar 提取外出安排 ~/.openclaw/workspace/scripts/auto-travel-from-calendar.sh ``` ## 同步到 Obsidian ```bash ~/.openclaw/workspace/scripts/sync-travel-to-obsidian.sh ``` ## 外出提醒 ```bash # 自动验证并设置外出提醒 ~/.openclaw/workspace/scripts/travel-reminder-auto-verify.sh ``` ## 数据存储 - 每周统计：`memory/weekly-travel-YYYY-Www.md` - Excel 报表：`exports/` - Obsidian：`~/Documents/obsidiansave/外出记录/` ## 分类规则 - **工作外出**：调研/考务/会议/拍摄等 - **生活外出**：购物/聚餐/办事等 - 周报显示格式：`工作 X 次 + 生活 X 次`


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