OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dagangtj/system-monitor-pro (version 1.0.0)

--- name: system-monitor-pro version: 1.0.0 description: Real-time OpenClaw system monitoring with beautiful terminal UI. CPU, memory, disk, GPU, Gateway, cron jobs, model quota, and multi-machine support. Works on macOS and Linux. author: dagangtj tags: [monitoring, system, dashboard, devops, gpu, openclaw] triggers: - system status - 系统状态 - monitor - 监控 - dashboard - health check - 健康检查 --- # System Monitor Pro 🖥️ Real-time OpenClaw system monitoring skill. Beautiful formatted output directly in your chat. ## Features - 🖥️ CPU / Memory / Disk usage with visual bars - 🎮 GPU monitoring (NVIDIA) — utilization + VRAM - 🌐 Gateway status + uptime - ⏰ Cron job status overview - 🤖 Model & quota tracking - 💻 Multi-machine support (SSH remote monitoring) - 📊 Beautiful terminal-style formatted output - 🔔 Alert thresholds (CPU>80%, Memory>85%, Disk>90%) ## Usage Just say any of these: - "system status" / "系统状态" - "monitor" / "监控" - "health check" / "健康检查" - "dashboard" ## How It Works When triggered, run the monitor script: ```bash node <skill_dir>/monitor.js [--remote user@host] [--json] [--alert-only] ``` ### Options - `--remote user@host` — Monitor a remote machine via SSH - `--json` — Output raw JSON instead of formatted text - `--alert-only` — Only show items that need attention ### Output Format The script outputs a beautifully formatted system status card like: ``` 🦞 OpenClaw System Status ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🖥️ CPU ██░░░░░░░░ 18% 💾 Memory ████░░░░░░ 42% 6.8/16.0 GB 💿 Disk █░░░░░░░░░ 7% 14/228 GB 🌐 Gateway ● Running (pid 1234) ⏰ Crons 6/6 OK 🤖 Model claude-opus-4-6 (yunyi) 💰 Quota ████████████ 100% ⚠️ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` ### Multi-Machine Example ```bash # Local machine node monitor.js # Remote machine via SSH node monitor.js --remote macmini2001-01@100.104.241.24 # Both machines node monitor.js && node monitor.js --remote macmini2001-01@100.104.241.24 ``` ### Integration Add to your HEARTBEAT.md for periodic monitoring: ```markdown ## System Monitor (every 30 min) Run: node ~/.openclaw/workspace/skills/system-monitor-pro/monitor.js --alert-only If alerts found → notify user ``` ## Requirements - Node.js 18+ - macOS or Linux - SSH access for remote monitoring (optional) - `nvidia-smi` for GPU monitoring (optional) ## Files | File | Description | |------|-------------| | SKILL.md | This file | | monitor.js | Main monitoring script |


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: lilyjazz/black-box (version 1.0.0)

--- name: black-box description: Indestructible audit logs for agent actions, stored in TiDB Zero. metadata: openclaw: emoji: 📦 requires: bins: ["python3", "curl"] env: ["TIDB_HOST", "TIDB_PORT", "TIDB_USER", "TIDB_PASSWORD"] --- # Black Box (Powered by TiDB Zero) ## Overview **Black Box** is an indestructible audit log for AI Agents. It acts as a "Flight Data Recorder" that streams critical actions, errors, and reasoning chains to a persistent cloud database (TiDB Zero) in real-time. ## Security & Provisioning 1. **Bring Your Own Database (Recommended):** Set `TIDB_*` environment variables. 2. **Auto-Provisioning (Fallback):** If no credentials are found, this skill uses the TiDB Zero API to create a temporary database for logging. The connection string is cached in `~/.openclaw_black_box_dsn`. ## Why use this? * **Crash Survival:** Local logs vanish when containers crash. Cloud logs persist. * **Audit Trail:** Prove exactly what your agent did and why (compliance). * **Debugging:** Retrieve the last 100 actions leading up to a failure. ## Prerequisites * **TiDB Credentials:** Standard MySQL connection parameters (`TIDB_HOST`, `TIDB_USER`, etc.). * **Network:** Outbound access to TiDB Cloud (port 4000). ## Usage ### 1. Log an Event Record a critical action or error: ```bash python {baseDir}/run.py --action log --level ERROR --message "System crash imminent: Memory leak detected" ``` ### 2. Read Logs Retrieve the last N logs (default: 10): ```bash python {baseDir}/run.py --action read --limit 20 ``` ## Schema This skill creates a table `agent_logs` with columns: `timestamp`, `level`, `message`, `metadata` (JSON).

path: requirements.txt
content: pymysql


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: wimi321/computer-use-windows (version 0.1.1)

--- name: computer-use-windows version: 0.1.1 description: Top-level Windows computer-use skill with a bundled standalone runtime that bootstraps itself without any local Claude installation, private native modules, or extracted app assets. tags: - skill - windows - computer-use - automation - mcp --- # Windows Computer-Use Skill Use this skill when the task needs a portable Windows computer-use skill bundled with its own standalone runtime and MCP server. ## What this skill does - uses the bundled `windows-computer-use-skill` project under the installed skill directory - builds the standalone MCP server - lets the server auto-bootstrap its Python runtime on first launch - avoids any dependency on local Claude binaries, `.node` modules, or extracted app assets - stays explicitly Windows-only because the underlying desktop-control backend is Windows-specific ## Default bundled project path After installation, assume the standalone project lives at: ```bash ~/.codex/skills/computer-use-windows/project ``` If the user installed the skill under a custom `CODEX_HOME`, use that equivalent path instead. ## Build Always build from the bundled project: ```bash cd ~/.codex/skills/computer-use-windows/project npm install npm run build ``` ## Run ```bash cd ~/.codex/skills/computer-use-windows/project node dist/cli.js ``` The first real run will automatically create `.runtime/venv` and install the public Python dependencies. ## Validation notes - Version `0.1.1` fixes the shared system-key blocklist logic so Windows runtime builds use the correct Windows shortcut denylist instead of a broken cross-platform branch. - This project has been statically validated from macOS for TypeScript build health, bundled project integrity, and Python helper syntax, but still needs end-to-end runtime validation on a real Windows machine. ## Guardrails - Treat this host as trusted-local only. - Do not tell the user to search their local Claude install for binaries or hidden assets. - Be explicit that this runtime is standalone and uses public dependencies only. - Mention that the current runtime reports `screenshotFiltering: none`, so action gating is handled at the MCP layer.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hczaihe/xinchuan-docx-editor (version 1.0.1)

--- name: docx-editor description: 专用于编辑 Word 文档（.docx）， ## 概述 本 Skill 用于处理 Word 文档（.docx）编辑任务。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wall-nut417/trump-news (version 1.0.0)

--- name: trump-news-daily description: 每日拉取特朗普相关新闻（来自官方与主流通讯社信息源），经 AI 翻译成中文、编辑后推送给用户 user-invocable: true --- # 每日特朗普新闻 · 信息源与推送 ## 功能说明 当用户或定时任务触发「今日特朗普新闻」「Trump 新闻汇总」「拉取特朗普相关新闻并推送」等请求时，按以下流程执行： 1. **拉取新闻**：运行本技能目录下的 `scripts/fetch_trump_news.py`，从下方**确凿信息源**拉取当日/近期特朗普相关内容。 2. **翻译与编辑**：根据脚本输出的原文摘要，由你（AI）翻译成中文，并做简洁编辑（保留事实、去掉冗余、可加一句简短点评或背景）。 3. **推送**：将整理好的中文版推送给用户（若已配置 Telegram，可调用同环境下的 `send_telegram.py` 或用户指定的推送方式；否则在对话中直接回复）。 --- ## 信息源清单（确切推荐） 脚本会从以下**两类**信息源拉取，均可在脚本或配置中指定具体 URL / API。 ### 第一类：核心官方与首发渠道（Real-time & Authoritative） | 来源 | 说明 | 脚本中的使用方式 | |------|------|------------------| | **Truth Social (@realDonaldTrump)** | 特朗普个人首发阵地，重大声明第一出口 | **已接入**：通过 Python 库 **truthbrush** 拉取。需 `pip install truthbrush` 并配置环境变量 `TRUTHSOCIAL_USERNAME` / `TRUTHSOCIAL_PASSWORD` 或 `TRUTHSOCIAL_TOKEN`；主脚本会可选调用 `scripts/fetch_truth_social.py` 并合并到摘要最前 | | **X (@TeamTrump / @CLF)** | 竞选团队与关联 PAC 官方账号 | 需 Twitter/X API，脚本不默认拉取；可选配置 | | **The White House (Briefing Room)** | 政策类唯一权威出处 | RSS：`https://www.whitehouse.gov/briefing-room/feed/` | | **Federal Register** | 行政命令（Executive Orders）最准确法律来源 | 官方 API：`https://www.federalregister.gov/api/v1/documents.json`，条件 `president=donald-trump`、[REDACTED_SECRET] | | **SCOTUSblog** | 特朗普相关法律诉讼与最高法院的专业解读 | RSS：`https://www.scotusblog.com/feed/` | ### 第二类：顶级通讯社（Fast & Fact-based） | 来源 | 说明 | 脚本中的使用方式 | |------|------|------------------| | **Reuters (路透社)** | 中立、快速，偏见较少 | RSS：政治/美国版块或站点提供的 RSS（见脚本内 SOURCES） | | **Associated Press (美联社)** | 全球标准化事实采集 | RSS：AP 提供的 Top News / 政治类 feed（见脚本内 SOURCES） | | **Bloomberg (彭博社)** | 侧重其言论对金融市场与宏观经济的影响 | 多需订阅，脚本可选配置或仅提供链接说明 | | **AFP (法新社)** | 国际视角，便于观察海外反馈 | RSS：AFP 英文 feed（见脚本内 SOURCES） | 脚本 `fetch_trump_news.py` 已内置 **Truth Social（truthbrush）**、RSS 与 Federal Register API；Truth Social 为可选，配置凭证后会自动出现在摘要最前。X、Bloomberg 等需 API Key 的仍在 SKILL 中仅作推荐。 --- ## 执行步骤 1. **运行拉取脚本**（在技能根目录或指定工作目录下）： ```bash python3 scripts/fetch_trump_news.py ``` 脚本会输出一段**英文原文摘要**（按来源分块：标题、链接、日期、摘要），并注明信息源。 2. **翻译与编辑**：阅读上述输出，将内容翻译成中文，并做编辑： - 保留事实与关键信息； - 去掉重复与无关枝节； - 可加一句简短点评或背景（如「此条来自美联社」）； - 保持客观、不捏造信源。 3. **推送**： - 若用户已配置 Telegram 且环境中有 `send_telegram.py`（例如与 polymarket-telegram-picks 同机），可调用： ```bash python3 /path/to/send_telegram.py "（你整理好的中文内容）" ``` - 否则在对话中直接回复整理好的中文版即可。 --- ## 定时任务（每日推送） 在 OpenClaw 中可用 cron 每天固定时间触发，例如北京时间早 8 点： ```bash openclaw cron add --name "特朗普每日新闻" \ --cron "0 8 * * *" \ --session main \ --message "请执行今日特朗普相关新闻拉取、翻译编辑并推送给用户" ``` 若 cron 支持时区，可加 `--tz "Asia/Shanghai"`。 --- ## 输出格式建议（推送给用户的中文版） - **标题**：如「特朗普相关新闻摘要 · YYYY-MM-DD」 - **按来源分块**：白宫简报 / Federal Register 行政命令 / SCOTUSblog / 路透 / 美联社 / 法新社 等，每块下为 1～3 句中文摘要 + 可选链接。 - **信源说明**：在结尾注明「以上来自 White House、Federal Register、SCOTUSblog、Reuters、AP、AFP 等公开渠道」，避免用户误以为来自 Truth Social/X 等未在脚本中拉取的渠道。 所有内容均基于脚本拉取到的公开信息源，由你翻译、编辑后推送，不保证实时覆盖 Truth Social / X 上的首发内容。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xueyetianya/apicheck (version 2.0.0)

--- version: "2.0.0" name: apicheck description: "API请求构造、curl命令生成、Mock数据、API文档、HTTP状态码速查、Headers说明。API request builder, curl generator, mock data, API documentation, HTTP status codes." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # API Tester — API 测试助手 API请求构造、curl命令生成、Mock数据生成、API文档撰写、HTTP状态码速查、Headers说明。 ## 使用方式 Agent 根据用户需求，运行对应脚本命令，获取提示词后执行任务。 ### 可用命令 | 命令 | 说明 | 示例 | |------|------|------| | `request` | 构造API请求 | "帮我构造一个POST请求" | | `curl` | 生成curl命令 | "帮我写个curl命令" | | `mock` | 生成Mock数据 | "生成用户列表的Mock数据" | | `doc` | 编写API文档 | "帮我写个API接口文档" | | `status` | HTTP状态码速查 | "404是什么意思" | | `headers` | HTTP Headers说明 | "Content-Type有哪些值" | ### 运行方式 ```bash bash scripts/api.sh <command> ``` ## 触发关键词 api、接口、curl、http、rest、restful、请求、mock、接口文档、状态码、status code、headers、请求头、post请求、get请求、api测试、接口测试 --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `apicheck help` for all commands ## Commands Run `apicheck help` to see all available commands.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: democ4s/c4s-csv-stats-analyzer (version 1.0.0)

--- name: csv-stats-analyzer description: Analyzes any CSV file and returns row count, column names, and basic statistics for numeric columns. user-invocable: true metadata: emoji: 📊 requires: bins: ["uv"] --- ## Usage Invoke with: `/csv-stats-analyzer <path-to-csv-file>` Example: `/csv-stats-analyzer ./sales.csv` ## Features - Shows total rows and columns - Lists all column names - Calculates min, max, and average for every numeric column ## Rules - The CSV must exist and be readable. - Use the helper script at {baseDir}/analyzer.py - Run it with: uv run --with pandas {baseDir}/analyzer.py [csv-path] - Return the output in a clean, readable format with emojis. - Never modify the original CSV file.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fanyanggod/pipiads-tiktok-ad-tracker-monitor (version 1.0.1)

--- name: tiktok-ad-tracker-monitor description: Track TikTok ad activity and monitor advertiser changes using PipiAds tools for TikTok ad discovery and Facebook-based monitoring workflows. metadata: openclaw: requires: env: - PIPIADS_API_KEY bins: - npm install: command: npm args: - install - -g - "pipiads-mcp-server@1.0.3" primaryEnv: PIPIADS_API_KEY emoji: "📊" homepage: https://www.pipiads.com mcpServers: pipiads: command: pipiads-mcp-server env: PIPIADS_API_KEY: "{{PIPIADS_API_KEY}}" --- # TikTok Ad Tracker & Monitor Use this skill to research TikTok ads and manage ongoing monitoring workflows. ## Setup 1. Visit the official website at [https://pipiads.com/](https://pipiads.com/) to view real-time dashboard data and product UI. 2. Visit [https://www.pipispy.com/](https://www.pipispy.com/) to get your API key and manage billing/recharge. 3. Set the environment variable: `PIPIADS_API_KEY` ## When to Use - Find trending TikTok ads, recurring creatives, and long-running winners - Track advertiser changes over time with monitor tasks - Review monitored ads, landing pages, products, and copy changes - Analyze creative hooks, angles, engagement, and delivery duration ## When NOT to Use - The user only wants Facebook-only ad library research - The user wants TikTok Shop product/store analytics without ad research - The user wants general market research outside PipiAds data ## Core Tools ### TikTok Ad Research - `search_ads` — Search ads and narrow to TikTok with platform filters - `get_ad_detail` — Inspect a specific ad in detail - `search_advertisers` — Find advertisers behind TikTok campaigns - `get_advertiser_detail` — Inspect advertiser details ### Monitoring - `search_fb_advertisers` — Find advertiser entry points for monitor setup - `create_monitor_task` — Create a monitoring task - `list_monitor_tasks` — List existing monitor tasks - `get_monitor_task_detail` — View task details - `get_monitor_board` — Review monitoring dashboard - `get_monitor_realtime_overview` — Real-time task overview - `get_monitor_daily_overview` — Daily performance overview - `get_monitor_ad_list` — Review monitored ads - `get_monitor_ad_detail` — Review a monitored ad in detail - `get_monitor_latest_products` — See newly found products - `get_monitor_landing_page_list` — Review landing pages - `cancel_monitor_task` — Stop a monitoring task ## Recommended Workflow 1. Start with `search_ads` and constrain to TikTok, region, and time range. 2. Use `get_ad_detail` to inspect the strongest creatives. 3. If the user wants ongoing tracking, find the advertiser and use `create_monitor_task`. 4. Use monitor overview and ad list tools to review changes over time. ## Cost Awareness Each API call consumes credits from your PipiAds account: - **List/Search**: 1 credit per result returned - **Detail**: 1 credit per request (free if queried within 3 days) Prefer narrow TikTok searches before creating broad monitor workflows.


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

--- name: healthprobe description: "Probe any URL and check if it's up. Returns the HTTP status code, response latency in milliseconds, and a healthy/not-healthy verdict. Configurable timeout. Useful for checking if an API is available before calling it." metadata: {"openclaw":{"emoji":"💓","requires":{"bins":["python"]},"install":[{"id":"pip","kind":"uv","packages":["fastapi","uvicorn","pydantic","httpx"]}]}} --- # HealthProbe Check if a URL is up before you call it. ## Start the server ```bash uvicorn healthprobe.app:app --port 8009 ``` ## Probe a URL ```bash curl -s -X POST http://localhost:8009/v1/probe \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com"}' | jq ``` Returns `healthy` (true if 2xx), `status_code` (HTTP code or -1 on error), `latency_ms`, and `error` (if connection failed). ## With custom timeout ```bash curl -s -X POST http://localhost:8009/v1/probe \ -H "Content-Type: application/json" \ -d '{"url": "https://slow-api.example.com", "timeout_ms": 1000}' | jq '.healthy' ``` Timeout range: 100ms to 30,000ms (default: 5,000ms).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cry779/openclaw-risk-manager (version 1.0.0)

--- name: risk-manager description: Use when user asks about Monitor portfolio risk, R-multiples, and position limits. Creates hedging strategies, calculates expectancy, and implements stop-losses. Use PROACTIVELY for risk assessment, trade tracking, or portfolio protection. metadata: model: inherit --- ## Use this skill when - Working on risk manager tasks or workflows - Needing guidance, best practices, or checklists for risk manager ## Do not use this skill when - The task is unrelated to risk manager - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. You are a risk manager specializing in portfolio protection and risk measurement. ## Focus Areas - Position sizing and Kelly criterion - R-multiple analysis and expectancy - Value at Risk (VaR) calculations - Correlation and beta analysis - Hedging strategies (options, futures) - Stress testing and scenario analysis - Risk-adjusted performance metrics ## Approach 1. Define risk per trade in R terms (1R = max loss) 2. Track all trades in R-multiples for consistency 3. Calculate expectancy: (Win% × Avg Win) - (Loss% × Avg Loss) 4. Size positions based on account risk percentage 5. Monitor correlations to avoid concentration 6. Use stops and hedges systematically 7. Document risk limits and stick to them ## Output - Risk assessment report with metrics - R-multiple tracking spreadsheet - Trade expectancy calculations - Position sizing calculator - Correlation matrix for portfolio - Hedging recommendations - Stop-loss and take-profit levels - Maximum drawdown analysis - Risk dashboard template Use monte carlo simulations for stress testing. Track performance in R-multiples for objective analysis.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liwiw/juncheng-ai-tutor (version 1.0.0)

# 郡城AI导师 · Juncheng AI Tutor 零基础学AI的专业辅导技能，覆盖大模型原理到实际应用。 ## 功能 1. **AI概念解析** — 用通俗语言解释大模型、Transformer、RAG等核心技术 2. **动手实践引导** — Step-by-step指导Prompt工程、LangChain开发、Agent框架搭建 3. **代码调试** — 分析和修复AI应用中的Python代码错误 4. **课程体系** — 从AIGC入门到工业落地，80节免费课+20节视频实战课 ## 使用 ```openclaw -summon "juncheng-ai-tutor" 用5岁小孩能听懂的话解释一下什么是大模型？ ``` ## 作者 郡城智能科技 (Juncheng AI) — 烟台本地AI服务商


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

--- name: "网红艺术" description: "提供关于网红艺术的推荐清单、打卡路线与指南。用户搜索网红艺术或规划相关出行时调用。" --- # 网红艺术 ## 筛选项 - 日期/时段/票档/是否限量 - 场地座位/遮挡/距离舞台/入场规则 - 演出/展览阵容/风格/是否可拍摄 ## 返回字段 - 时间表/地点/票价/购票渠道/退票说明 - 入场流程/安检要求/随身物品限制 - 交通与散场建议/周边餐饮与住宿 ## 示例请求 - “北京 网红艺术 本月清单（含交通与购票）” - “上海 网红艺术 可拍摄展览TOP20（需预约）” ## 更新频率 - 每周迭代，结合新热度与口碑反馈 ## 注意事项 - 高峰期排队较久，建议提前预约或错峰 - 部分项目有拍摄限制或最低消费，按现场规则执行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 137984917-cyber/interior-budget-auto (version 1.0.0)

# 室内设计自动预算生成器 ## 描述 从DXF提取户型信息，基于设计师历史报价库自动生成完整家装/工装预算Excel，严格匹配原有模板格式，不漏项，价格贴合本地市场。 ## 功能 - 自动提取DXF户型文字信息，确认层数面积 - 基于设计师历史报价自动构建标准项目库 - 按楼层/空间自动分配项目，估算工程量 - 生成和原有模板格式完全一致的Excel预算表 - 支持家装/工装两种模式 ## 作者 老冷 @ 温州隐室空间设计 ## 分类 设计工具 / 预算自动化 ## 触发词 - 生成预算 - 预算自动生成 - 室内设计预算 - 家装预算 ## 依赖 - python3 - openpyxl - ezdxf - python-pptx (可选，用于PPT生成) ## 使用方法 1. 设计师在CAD中将图纸导出为DXF格式 2. 放入指定文件夹 3. 运行 `generate_budget.py /path/to/your/file.dxf output.xlsx` 4. 等待生成，直接拿到可编辑的预算表 5. 微调工程量即可交付客户 ## 优势 - 保留设计师原有报价体系，贴合本地市场价 - 半小时出初稿，比手工快10倍 - 不漏小项目（钢筋、纤维网这些不容易漏了） - 完全本地运行，数据不外出 ## 示例 ``` python generate_budget.py ~/Desktop/项目/户型.dxf ~/Desktop/龙虾/项目预算.xlsx ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhangpengle/goverment-bidding-email (version 0.1.4)

--- name: govb-email description: 政府采购商机专用推报工具。根据商机数据自动生成 Excel 并通过 SMTP 发送邮件。当用户说"govb-email"、"政府采购商机邮件"、"推送政府采购商机"、"政采商机通报"时触发。注意：这不是通用邮件客户端，仅用于执行 govb 业务逻辑。 metadata: {"openclaw":{"emoji":"📧","requires":{"bins":["govb-email","govb-fetcher"]},"install":"pip install -e {baseDir}"}} --- # Govb Email 自动抓取政府采购商机并发送邮件报告。 ## 环境变量要求 该技能必须在 `.env` 中配置以下核心参数才能激活： - `EMAIL_TO`, `EMAIL_CC`, `EMAIL_FROM`: 收发件地址 - `EMAIL_SMTP_HOST`, `EMAIL_SMTP_PORT`: SMTP 服务器信息 - `EMAIL_SMTP_USER`, `EMAIL_SMTP_PASSWORD`: 认证信息 - `EMAIL_SUBJECT_PREFIX`, `EMAIL_BODY_INTRO`: 邮件模板配置 - `EMAIL_RECIPIENT_NAME`, `EMAIL_SENDER_NAME`: 称呼和签名 ## 快速使用 - `/govb-email` → 发送昨日报告（默认） - `/govb-email --help` → 显示帮助信息 - `/govb-email --today` → 发送今日报告 - `/govb-email --date 2026-03-23` → 发送指定日期报告 - `/govb-email --keywords "模型,仿真"` → 使用自定义关键词筛选 - `/govb-email --to [REDACTED_SECRET]` → 测试发送至指定收件人 ## 参数说明 | 参数 | 说明 | 默认值 | |------|------|--------| | 无参数 | 默认昨日 | 启用 | | `--today` | 今日 | - | | `--date YYYY-MM-DD` | 指定日期 | - | | `--keywords WORDS` | 关键词，逗号分隔 | govb_fetcher 配置的默认关键词 | | `--to ADDRESS` | 测试发送至指定收件人 | .env 中的配置 | ## 数据源 - 北京政采（zbcg-bjzc.zhongcy.com） - 湖南政采（ccgp-hunan.gov.cn） ## 触发词 发送邮件、推送报告、邮件通知、政采商机通报 ## 配置文件 配置文件位于 `~/.config/govb-email/.env`，可配置以下参数： | 环境变量 | 用途 | |----------|------| | `EMAIL_TO` | 收件人，逗号分隔 | | `EMAIL_CC` | 抄送人，逗号分隔 | | `EMAIL_FROM` | 发件人 | | `EMAIL_RECIPIENT_NAME` | 收件人称呼 | | `EMAIL_SENDER_NAME` | 发件人签名 | | `EMAIL_SUBJECT_PREFIX` | 邮件主题前缀 | | `EMAIL_BODY_INTRO` | 邮件正文开头 | | `EMAIL_SMTP_HOST` | SMTP 服务器 | | `EMAIL_SMTP_PORT` | SMTP 端口 | | `EMAIL_SMTP_USER` | SMTP 用户名 | | `EMAIL_SMTP_PASSWORD` | SMTP 密码 | 创建配置文件可复制 `.env.example` 为 `~/.config/govb-email/.env` 后修改。 ## 技术说明 - 使用 SMTP 直接发送邮件（配置 EMAIL_SMTP_* 环境变量） - 使用文件锁防止并发执行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kadbbz/multi-user-long-term-memory (version 1.0.0)

# Multi-User Long-Term Memory 为多个用户维护独立的长期记忆文件。 ## 功能 - 为不同用户创建独立的长期记忆文件 - 记录用户的偏好、上下文、重要事项 - 根据用户标识自动定位到对应的记忆文件 ## 用户标识规则 **重要**：用于区分记忆的用户名取自 `sender_id` 中 `|` 符号**以前**的部分。 例如： - `sender_id = "hzg-demo-appWillNing|s-24485376"` → 用户名为 `hzg-demo-appWillNing` - `sender_id = "alice|session-123"` → 用户名为 `alice` - `sender_id = "bob_channel|xyz"` → 用户名为 `bob_channel` ## 使用场景 当需要： - 记住特定用户的信息或偏好 - 跨会话保持对某用户的记忆 - 为不同用户维护独立的上下文 ## 文件结构 ``` ~/.openclaw/workspace/skills/multi-user-long-term-memory/ ├── SKILL.md ├── README.md ├── references/ │ └── user-memory.js # 记忆管理逻辑 └── users/ └── {username}.md # 各用户的记忆文件（按|前的用户名命名） ``` ## API ### 获取用户记忆 ``` user-memory.get(senderId) → 返回该用户的记忆内容 ``` ### 保存用户记忆 ``` user-memory.save(senderId, content) → 保存内容到用户记忆文件 ``` ### 追加用户记忆 ``` user-memory.append(senderId, content) → 追加内容到用户记忆文件 ``` ### 初始化用户记忆 ``` user-memory.init(senderId, userName) → 初始化用户记忆文件 ``` ## 注意事项 - 记忆文件按用户名（|前的部分）命名，存储在 users/ 目录下 - 文件格式为 Markdown，便于阅读和编辑 - 相同用户名的不同会话将共享同一记忆文件


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