OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ractoto/log-analyzer-evomap (version 1.0.0)

--- name: log-analyzer version: 1.0.0 signals: - log - error - stack trace - exception - debugging - 分析日志 description: | 分析错误日志，提取结构化信息：异常类型、消息、文件路径， 分类错误（网络/IO/权限/内存/超时），从错误历史中提取预防建议， 批量分析生成摘要报告。配套 EvoMap evolver 使用，从 ~/evolver-memory/ 日志中提取模式。 category: analysis author: EvoMap Capsule tags: - error-analysis - stack-trace - debugging - log-analysis - evomap --- # log-analyzer 错误日志分析 Capsule，用于解析和分类错误日志，提取结构化信息。 ## 核心函数 ### `parseError(logText)` 解析堆栈跟踪，提取异常类型、消息、文件路径。支持 JavaScript/Node.js, Python, Java/Kotlin, Go。 ### `classifyError(error)` 分类错误类型: network | io | permission | memory | timeout | unknown ### `extractLessons(logText)` 从错误历史中提取预防建议。 ### `summarize(logs, options)` 批量分析日志，生成摘要报告。options: { maxLogs, format: 'object'|'text' } ### `analyzeEvolverLogs()` 分析 ~/evolver-memory/ 目录下的日志文件。 ## CLI ```bash node index.js # 本地测试 node index.js analyze /path/to/error.log # 分析日志文件 node index.js evolver # 分析 evolver-memory 日志 node index.js solidify # 发布到 EvoMap Hub ``` ## 错误分类 | 类别 | 关键词 | |------|--------| | network | ECONNREFUSED, ETIMEDOUT, fetch failed | | io | ENOENT, file not found, disk full | | permission | EACCES, access denied | | memory | out of memory, OOM, heap out of memory | | timeout | ETIMEDOUT, timeout, deadline exceeded |


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rancho718/00-action-guard (version 1.0.0)

--- name: 00-action-guard description: "【最高优先级】操作守卫。任何工具调用前必须先完成权限检查！AI 在执行任何非日常对话的操作前，必须先调用 permission-gate 验证权限。" metadata: {"openclaw": {"always": true}} --- # 操作守卫（最高优先级） ## ⚠️ 核心规则 **这是最高优先级的规则！任何工具调用前必须遵守！** ### 执行流程（强制） ``` 用户请求 ↓ 1. 判断是否需要权限检查？ - 日常对话（打招呼、闲聊、问天气）→ 直接执行 - 其他任何操作 → 继续 ↓ 2. 调用 permission-gate 检查权限 ↓ 3. 检查结果 - allowed=True → 执行操作 - allowed=False → 拒绝并说明原因 ``` ### 需要检查的操作 | 操作类型 | 示例 | 是否需要检查 | |----------|------|--------------| | 执行 Skill | "帮我创建文档" | ✅ 必须检查 | | 工具调用 | 调用任何 tool | ✅ 必须检查 | | 敏感查询 | 查询手机号、邮箱 | ✅ 必须检查 | | 写操作 | 创建/修改/删除文件 | ✅ 必须检查 | | 跨渠道 | 发消息到其他平台 | ✅ 必须检查 | | 日常对话 | "你好"、"今天天气" | ❌ 不需要 | ### 禁止行为 - ❌ 禁止直接执行任何工具调用而不检查权限 - ❌ 禁止跳过 permission-gate - ❌ 禁止在权限检查前执行操作 ### 正确示例 **用户说：** "帮我创建一个飞书文档" ``` ✅ 1. 判断：需要权限检查（创建文档） ✅ 2. 调用 permission-gate ✅ 3. 检查结果：allowed=True ✅ 4. 执行 feishu_doc.create ``` ### 错误示例 **用户说：** "帮我创建一个飞书文档" ``` ❌ 1. 直接调用 feishu_doc.create ❌ 2. 跳过 permission-gate ``` ## 注意事项 1. **必须先判断** — 先判断是否需要权限检查，再决定是否调用 permission-gate 2. **必须等待结果** — 调用 permission-gate 后必须等待返回结果 3. **根据结果行动** — 只有 allowed=True 才能继续执行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lentiancn/skill-alpine (version 1.0.5)

--- name: skill-alpine description: "alpine:interface 1 - get latest releases; alpine:interface 2 - aggregate latest version archs" --- # Important Principles ⭐ **Modification Restriction (Mandatory)**: Do not modify any files under `skill-alpine`. This skill is maintained via `openclaw skills update skill-alpine`; any alterations will disrupt its normal operation. --- # alpine:interface 1 - get latest releases ```bash latest_releases_json=$(bash scripts/alpine_fetch_latest_releases.sh) ``` ## Return value (latest_releases_json=stdout, example only) ```json [ { "version": "3.23.4", "arch": "x86_64", "file_url": "https://dl-cdn.alpinelinux.org/alpine/latest-stable/releases/x86_64/alpine-minirootfs-3.23.4-x86_64.tar.gz" }, { "version": "3.23.4", "arch": "aarch64", "file_url": "https://dl-cdn.alpinelinux.org/alpine/latest-stable/releases/aarch64/alpine-minirootfs-3.23.4-aarch64.tar.gz" } ] ``` --- # alpine:interface 2 - aggregate latest version archs ```bash latest_version_archs_json=$(bash scripts/alpine_aggregate_latest_version_archs.sh) ``` ## Return value (latest_version_archs_json=stdout, example only) ```json [ { "version": "3.23.4", "archs": [ "aarch64", "armhf", "armv7", "loongarch64", "ppc64le", "riscv64", "s390x", "x86", "x86_64" ] } ] ``` --- # More skills: https://github.com/lentiancn/skills


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: subaru0573/mcp-server-discovery-p (version 1.0.0)

--- name: mcp-server-discovery description: Discover, search, and manage MCP (Model Context Protocol) servers with comprehensive capabilities. Efficiently locate servers, retrieve detailed information, generate client configurations, and navigate the entire MCP ecosystem. Ideal for any query involving MCP servers, Model Context Protocol, server discovery, or configuration tasks. Streamlines workflows by providing reliable, up-to-date server data and configuration support, ensuring smooth integration and management of MCP resources.participation hardware capabilities detection argues present reprint presented proposeue broadly abstractby php optimal inexpensive electrical mavericks staircase coarse --- # MCP Server Discovery This skill helps you discover and manage MCP (Model Context Protocol) servers. ## What is MCP? Model Context Protocol (MCP) is an open standard that enables AI systems to connect with external data sources and tools. It provides a standardized way for AI assistants to access files, databases, APIs, and other resources. ## Available Commands Use the `scripts/mcp_discover.py` script for all MCP operations: ### List Available Servers ```bash python3 scripts/mcp_discover.py list ``` Filter by category: ```bash python3 scripts/mcp_discover.py list --category database ``` Categories: filesystem, dev, database, web, search, memory ### Search for Servers ```bash python3 scripts/mcp_discover.py search --query "database" ``` ### Get Server Details ```bash python3 scripts/mcp_discover.py info --name postgres ``` ### Generate MCP Client Configuration ```bash python3 scripts/mcp_discover.py config --servers "filesystem,github,memory" ``` ## Common Workflows ### Setting up a new MCP client 1. List available servers to see options 2. Select the servers you need 3. Generate configuration with those servers 4. Save the output to your MCP client's config file ### Finding the right server 1. Use `search` with keywords related to your need 2. Use `info` to get detailed information about a specific server 3. Check the install command and URL for setup instructions ## Server Categories - **filesystem**: File system access and management - **dev**: Development tools and integrations (GitHub, etc.) - **database**: Database connections (PostgreSQL, SQLite) - **web**: Web scraping and content fetching - **search**: Search engine integrations - **memory**: Persistent memory and knowledge graph ## JSON Output All commands support `--json` flag for programmatic use: ```bash python3 scripts/mcp_discover.py list --json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pikachu022700/quant-orchestrator-free (version 1.0.0)

--- name: quant-orchestrator description: Multi-Agent AI Quant System with factor mining, strategy generation, and automated backtesting metadata: clawdbot: emoji: "📊" homepage: "https://clawhub.com/quant-orchestrator" os: ["darwin", "linux", "win32"] requires: bins: ["python", "pip"] env: [] install: - type: "script" run: "pip install lightgbm pandas numpy requests" description: "Install dependencies" --- # Quant Orchestrator Skill ## 📊 Description Multi-Agent AI Quant System for automated strategy research and backtesting. ## 💰 Pricing - **1 USDC per call** - 1 USDT = 1000 tokens - Min deposit: 8 USDT - Platform fee: 5% ## 🚀 Features - **AI Factor Mining** - Generate trading factors automatically - **AI Strategy Generation** - Create strategies from factors - **Automated Backtesting** - One-click backtest with metrics - **Strategy Evaluation** - Sharpe, MaxDrawdown, WinRate, IC, IR ## Architecture ``` Agentic Quant Platform ├── DataAgent (market data) ├── FactorAgent (AI factor mining) ├── StrategyAgent (AI strategy generation) ├── BacktestAgent (automated backtesting) └── EvaluationAgent (strategy evaluation) ``` ## Usage ```python from skill_with_billing import QuantOrchestrator # Run with user billing orchestrator = QuantOrchestrator() result = orchestrator.run_pipeline( task="研究BTC动量策略", user_id="USER_ID" ) ``` ## Output - Sharpe Ratio - Max Drawdown - Win Rate - IC (Information Coefficient) - IR (Information Ratio) - Strategy code - Backtest results ## Quick Start ```bash # Install dependencies pip install lightgbm pandas numpy requests # Run basic pipeline python skill.py "研究BTC动量策略" # Run with billing python skill_with_billing.py "研究BTC策略" user123 ``` ## Files - `skill.py` - Basic skill - `skill_with_billing.py` - Skill with payment - `billing.py` - Payment SDK - `btc_predictor_optimized.py` - BTC prediction


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jhillin8/overcome-problem (version 1.0.0)

--- name: overcome-problem description: Break down any problem with structured thinking, action plans, and progress tracking author: clawd-team version: 1.0.0 triggers: - "help me overcome" - "stuck on problem" - "how do I solve" - "break down problem" - "overcome challenge" --- # Overcome Any Problem Transform obstacles into solvable steps. Structured thinking beats brute force. ## What it does - **Problem decomposition** - Break complex challenges into discrete, manageable pieces - **Action planning** - Generate step-by-step execution paths with clear dependencies - **Obstacle identification** - Anticipate blockers before they derail progress - **Progress tracking** - Monitor advancement and adjust course mid-execution ## Usage ### Define Problem State the challenge clearly. What's blocking you? What does success look like? ### Break it Down Decompose into 8 angles: functional, technical, temporal, resource, risk, stakeholder, precedent, creative. ### Create Action Plan Sequence tasks with dependencies. Identify parallelizable work. Flag critical path items. ### Track Progress Update status as you execute. Log blockers. Trigger re-analysis if assumptions break. ### Review Obstacles When stuck, inspect the directive. What changed? What was missed? Update and retry. ## Problem-Solving Framework **Define** - Clarify the problem statement. State constraints and success criteria. **Analyze** - Apply multiple lenses (technical, financial, temporal, creative, etc). **Generate options** - Brainstorm 3+ solution paths. Don't filter yet. **Choose** - Select the highest-leverage path. Justify the choice. **Execute** - Run the action plan. Track assumptions in real-time. **Review** - Evaluate outcome. What worked? What's the directive update? ## Tips - Start with the constraint. Problems are bottleneck puzzles. Remove the tightest constraint first. - Think in primitives. What's the smallest building block? Build up from there. - Multiply, don't add. Each system should amplify everything below it. - Parallel over sequential. Find independent work streams. Execute simultaneously. - All data stays local on your machine. No cloud dependencies, full privacy control.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mvanhorn/search-x (version 1.2.1)

--- name: search-x version: "1.1.0" description: Real-time X/Twitter search powered by Grok-4. Find tweets, trends, and discussions with citations. Grok-4.20 also returns image results alongside tweet citations. author: mvanhorn license: MIT repository: https://github.com/mvanhorn/clawdbot-skill-search-x homepage: https://docs.x.ai triggers: - search x - search twitter - find tweets - what's on x about - x search - twitter search metadata: openclaw: emoji: "🔍" requires: env: - XAI_API_KEY primaryEnv: XAI_API_KEY tags: - x - twitter - search - grok - real-time --- # Search X Real-time X/Twitter search powered by Grok's x_search tool. Get actual tweets with citations. ## Setup Set your xAI API key: ```bash openclaw config set skills.entries.search-x.apiKey "xai-YOUR-KEY" ``` Or use environment variable: ```bash export XAI_API_KEY="xai-YOUR-KEY" ``` Get your API key at: https://console.x.ai ## Commands ### Basic Search ```bash node {baseDir}/scripts/search.js "AI video editing" ``` ### Filter by Time ```bash node {baseDir}/scripts/search.js --days 7 "breaking news" node {baseDir}/scripts/search.js --days 1 "trending today" ``` ### Filter by Handles ```bash node {baseDir}/scripts/search.js --handles @elonmusk,@OpenAI "AI announcements" node {baseDir}/scripts/search.js --exclude @bots "real discussions" ``` ### Output Options ```bash node {baseDir}/scripts/search.js --json "topic" # Full JSON response node {baseDir}/scripts/search.js --compact "topic" # Just tweets, no fluff node {baseDir}/scripts/search.js --links-only "topic" # Just X links ``` ## Example Usage in Chat **User:** "Search X for what people are saying about Claude Code" **Action:** Run search with query "Claude Code" **User:** "Find tweets from @remotion_dev in the last week" **Action:** Run search with --handles @remotion_dev --days 7 **User:** "What's trending about AI on Twitter today?" **Action:** Run search with --days 1 "AI trending" **User:** "Search X for Remotion best practices, last 30 days" **Action:** Run search with --days 30 "Remotion best practices" ## How It Works Uses xAI's Responses API (`/v1/responses`) with the `x_search` tool: - Model: `grok-4-1-fast` (optimized for agentic search) - Returns real tweets with URLs - Includes citations for verification - Supports date and handle filtering ## Response Format Each result includes: - **@username** (display name) - Tweet content - Date/time - Direct link to tweet ## Environment Variables - `XAI_API_KEY` - Your xAI API key (required) - `SEARCH_X_MODEL` - Model override (default: grok-4-1-fast) - `SEARCH_X_DAYS` - Default days to search (default: 30)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: phil-osophy-42/12321321123 (version 1.0.0)

--- name: corporate-ppt-generator version: 1.0.2 description: Generate PPT decks from prompts/block XML while preserving corporate brand style. categories: - productivity - presentation --- # Corporate PPT Generator ## Description Generate a new `.pptx` by reusing existing template slides from `PPT_Template.pptx`. The runtime is XML-only and prioritizes template-first rendering. ## When To Use Use this skill when the user asks: - "Generate a PPT from a prompt or outline" - "Create a deck in DaoCloud style / company brand style" - "Render PPT from structured block/xml with template consistency" ## Inputs - `title`: optional deck title fallback - `output_filename`: output pptx filename - `template_file`: optional template filename in skill directory - `block_xml`: block/xml payload (required) - `render_strategy`: `template_first` | `template_only` - `allow_html_fallback`: allow html/richtext block fallback to template content slides ## Output - `output_path`: absolute path to generated pptx - `slide_count`: number of slides generated - `message`: status message ## Notes - Requires `python-pptx`. - Uses local `PPT_Template.pptx` in skill directory by default. - Use `assets/demo_blocks.xml` for a full XML demo (toc + matrix + pie/bar/line + table + slogan).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: imnull/email163-sender (version 1.0.1)

--- name: email163-sender description: 163邮箱发送工具。使用授权密码(授权码)进行SMTP认证发送邮件。支持文本邮件、HTML邮件、带附件邮件、抄送/密送。当用户需要发送邮件时使用此技能。 license: MIT --- # Email Sender (163) 通过授权密码发送邮件的技能。 ## 环境配置 ```bash # 163邮箱地址 export EMAIL_163_USER="[REDACTED_SECRET]" # 授权密码（在163邮箱设置中开启） export EMAIL_163_AUTH_CODE="your_auth_code" ``` **获取授权密码：** 1. 登录163邮箱 → 设置 → POP3/SMTP设置 2. 开启 POP3/SMTP服务 3. 新增授权密码并保存 ## 快速开始 ### 发送简单邮件 ```bash python3 scripts/send_email.py \ --to [REDACTED_SECRET] \ --subject "测试邮件" \ --body "这是一封测试邮件" ``` ### 发送带附件邮件 ```bash python3 scripts/send_email.py \ --to [REDACTED_SECRET] \ --subject "报告" \ --body "请查收附件" \ -a report.pdf ``` ### 发送HTML邮件 ```bash python3 scripts/send_email.py \ --to [REDACTED_SECRET] \ --subject "周报" \ --html \ --body "<h1>周报</h1><p>本周工作进展...</p>" ``` ### 查看发送历史 ```bash python3 scripts/send_email.py --list ``` ## 参数说明 | 参数 | 说明 | |------|------| | `--to`, `-t` | 收件人邮箱（多个用逗号分隔） | | `--subject`, `-s` | 邮件主题 | | `--body`, `-b` | 邮件正文 | | `--html` | HTML格式邮件 | | `-a`, `--attachment` | 附件路径（可多次指定） | | `--cc` | 抄送 | | `--bcc` | 密送 | | `--from`, `-f` | 发件人邮箱 | | `--auth-code` | 授权密码 | | `--list`, `-l` | 列出已发送邮件 | | `--status` | 查看邮件状态 | | `--clear-history` | 清空发送历史 | ## 常见问题 **Q: 认证失败？** A: 检查授权密码是否正确，确认已在163邮箱中开启POP3/SMTP服务。 **Q: 附件发送失败？** A: 确认文件路径正确，文件大小建议<20MB。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sunnyhot/earnings-tracker (version 1.1.0)

--- name: earnings-tracker version: 1.0.0 description: AI 驱动的财报追踪器，自动扫描 A 股/美股财报日历，推送重要财报更新 author: sunnyhot license: MIT keywords: - earnings - financial-reports - stock-tracker - akshare --- # Earnings Tracker - AI 驱动的财报追踪器 **自动扫描财报日历，推送重要财报更新** --- ## ✨ 核心功能 ### 📅 **财报日历扫描** - ✅ A 股预约披露时间表（AKShare） - ✅ 美股财报日历（Yahoo Finance） - ✅ 每周日自动扫描下周财报 - ✅ 筛选关注的公司 ### 📊 **财报数据追踪** - ✅ 业绩预告（提前信号） - ✅ 业绩快报（快速概览） - ✅ 正式财报（完整数据） - ✅ 营收、净利润、同比增长 ### 📈 **智能分析** - ✅ 超预期/不及预期判断 - ✅ 关键指标提取 - ✅ AI 相关亮点识别 - ✅ 机构点评汇总 ### 🔔 **自动推送** - ✅ Discord 频道推送 - ✅ Telegram 话题推送 - ✅ 定时任务调度 - ✅ 一次性提醒 --- ## 🚀 使用方法 ### **1. 安装依赖** ```bash pip install akshare ``` --- ### **2. 配置关注的公司** 编辑 `config/settings.json`： ```json { "watchlist": { "us": ["NVDA", "MSFT", "GOOGL", "META", "AMZN", "TSLA", "AMD"], "cn": ["600519", "000858", "601318", "000001"] }, "schedule": { "weeklyScan": "Sunday 18:00", "reportScan": "after_market" } } ``` --- ### **3. 运行扫描** ```bash node scripts/earnings-scanner.cjs ``` --- ## 📋 工作流程 ### **每周日 18:00 - 预扫描** 1. ✅ 扫描下周财报日历 2. ✅ 筛选关注的公司 3. ✅ 推送到 Discord/Telegram 4. ✅ 等待用户确认 ### **用户确认后** 1. ✅ 为每个财报创建一次性提醒 2. ✅ 财报发布后搜索结果 3. ✅ 格式化摘要 4. ✅ 推送到指定频道 --- ## 🌍 数据源 ### **A 股（推荐）** | 数据源 | 费用 | 接口 | |--------|------|------| | **AKShare** | 免费 | `stock_yysj_em()` - 预约披露时间表 | | **AKShare** | 免费 | `stock_yjyg_em()` - 业绩预告 | | **AKShare** | 免费 | `stock_yjkb_em()` - 业绩快报 | | **AKShare** | 免费 | `stock_yjbb_em()` - 正式财报 | ### **美股** | 数据源 | 费用 | 说明 | |--------|------|------| | Yahoo Finance | 免费 | 财报日历 | | Alpha Vantage | 有限免费 | 财务数据 API | --- ## 📊 示例输出 ### **财报日历扫描** ``` 📅 下周财报日历 (2026-03-17 ~ 2026-03-21) 🇺🇸 美股： • NVDA - 3月19日 盘后 • MSFT - 3月20日 盘后 • GOOGL - 3月18日 盘后 🇨🇳 A股： • 600519 贵州茅台 - 3月20日 • 000858 五粮液 - 3月21日 请回复要跟踪的公司（例如：NVDA, 600519） ``` ### **财报摘要推送** ``` 📊 NVDA Q4 2025 财报摘要 💰 营收: $22.1B (预期 $20.4B) ✅ 超预期 8.3% 📈 EPS: $4.12 (预期 $3.95) ✅ 超预期 4.3% 📊 毛利率: 72.1% (同比 +2.3%) 🎯 关键亮点： • 数据中心营收 $18.4B (同比 +409%) • AI 芯片需求强劲 • 下季度指引超预期 📌 管理层指引： • Q1 营收预期 $24B • 毛利率维持 70%+ 来源：Yahoo Finance, Seeking Alpha ``` --- ## 🔧 配置文件 ### `config/settings.json` ```json { "watchlist": { "us": ["NVDA", "MSFT", "GOOGL", "META", "AMZN", "TSLA", "AMD"], "cn": ["600519", "000858", "601318", "000001"] }, "schedule": { "weeklyScan": "Sunday 18:00", "reportScan": "after_market" }, "notify": { "channel": "discord", "to": "channel:1478698808631361647" }, "dataSources": { "cn": "akshare", "us": "yahoo_finance" } } ``` --- ## 📝 更新日志 ### v1.0.0 (2026-03-14) - ✅ 初始版本 - ✅ A 股财报日历扫描（AKShare） - ✅ 美股财报日历扫描（Yahoo Finance） - ✅ Discord/Telegram 推送 - ✅ 定时任务调度 --- **📊 轻松追踪财报，不错过重要信息！**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bytesagain-lab/page-builder (version 2.0.2)

--- name: "page-builder" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Page Builder concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [page,builder, reference] category: "devtools" --- # Page Builder Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Page Builder concepts, best practices, and implementation patterns. No API keys or credentials required. ## 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)
Skill: iscandium/iscandium-openclaw-samba-backup (version 1.0.0)

--- name: iscandium-openclaw-samba-backup description: 将 OpenClaw 数据备份到远程 Samba 服务器。当用户提到「备份 openclaw」「设置自动备份」时触发。 --- # OpenClaw Samba 备份 ## 触发条件 | 关键词 | 动作 | |--------|------| | 「备份 openclaw」「backup openclaw」 | 执行完整备份流程 | | 「自动备份」「定时备份」 | 设置定时任务 | ## 执行模式 | 选项 | 说明 | |------|------| | ✅ 自动执行 | 触发后直接执行备份，无需逐步确认 | ## 配置 配置文件：`config/default.json`（参数说明见 `config/params_schema.json`） 首次使用前，复制配置模板： ```bash cp config/default.json.example config/default.json ``` | 参数 | 说明 | |------|------| | `target_server_ip` | Samba 服务器 IP | | `target_share_name` | 共享文件夹名称 | | `target_share_username` | Samba 用户名 | | `target_share_password` | Samba 密码 | | `source_admin_username` | 本服务器管理员用户名 | | `source_admin_password` | 管理员密码（sudo 用） | | `max_backups` | 保留备份数量（默认 7） | | `source_dir` | 备份源路径（默认 ~/.openclaw） | | `target_folder` | 目标文件夹名（默认 hostname） | | `mount.vers` | SMB 版本（默认 2.0） | | `mount.mount_point` | 挂载点（默认 /mnt/iscandium-openclaw-samba-backup） | ## 工作流（1 步） | Step | 职责 | 执行者 | 文档 | 输入 | 输出 | |------|------|--------|------|------|------| | 01 | 执行备份 | 脚本 | `scripts/backup.sh` | config/default.json | Samba 共享目录 | ## 备份位置 ``` //{target_server_ip}/{target_share_name}/{target_folder}/{timestamp}/ ``` ## 运行备份 ```bash bash ~/.openclaw/workspace/skills/iscandium-openclaw-samba-backup/scripts/backup.sh ``` ## 设置定时备份 首次配置后，使用 OpenClaw 内置 cron： ```bash openclaw cron add \ --name "OpenClaw Samba 备份" \ --cron "0 3 * * *" \ --tz "<your-timezone>" \ --message "运行备份：bash ~/.openclaw/workspace/skills/iscandium-openclaw-samba-backup/scripts/backup.sh" \ --session isolated \ --agent <your-agent> \ --timeout-seconds 600 \ --no-deliver ``` ## 依赖 - `cifs-utils`（Samba 客户端） ```bash sudo apt install cifs-utils ``` ## 目录结构 ``` iscandium-openclaw-samba-backup/ ├── SKILL.md ├── config/ │ ├── default.json # 私有配置（不发布） │ ├── default.json.example # 配置模板 │ └── params_schema.json # 参数 schema └── scripts/ └── backup.sh # 备份脚本 ```


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