OpenClaw/clawhub-security-signals huggingface dataset:
Skill: kaiyuelv/nlp-text-analyzer (version 1.0.0)

--- name: nlp-text-analyzer description: NLP文本分析器 - 支持分词、情感分析、关键词提取、文本分类等自然语言处理功能 | NLP Text Analyzer - Tokenization, sentiment analysis, keyword extraction, text classification homepage: https://github.com/kaiyuelv/nlp-text-analyzer category: nlp tags: - nlp - text-analysis - sentiment - tokenization - chinese - jieba - textblob version: 1.0.0 --- # NLP文本分析器 强大的自然语言处理工具，支持中文和英文文本分析，包含分词、情感分析、关键词提取等功能。 ## 概述 本Skill提供完整的NLP文本分析能力： - 中文分词（Jieba分词） - 情感分析（SnowNLP / TextBlob） - 关键词提取 - 文本摘要生成 - 词频统计 - 命名实体识别 - 文本分类基础 - 相似度计算 - 中英双语支持 ## 依赖 - Python >= 3.8 - jieba >= 0.42.1 - snownlp >= 0.12.3 - textblob >= 0.17.1 ## 文件结构 ``` nlp-text-analyzer/ ├── SKILL.md # 本文件 ├── README.md # 使用文档 ├── requirements.txt # 依赖声明 ├── scripts/ │ └── text_analyzer.py # 文本分析脚本 ├── examples/ │ └── basic_usage.py # 使用示例 └── tests/ └── test_nlp.py # 单元测试 ``` ## 快速开始 ```python from scripts.text_analyzer import TextAnalyzer # 初始化分析器 analyzer = TextAnalyzer() # 中文分词 text = "自然语言处理是人工智能的重要分支" tokens = analyzer.segment(text) print(tokens) # ['自然语言', '处理', '是', '人工智能', '的', '重要', '分支'] # 情感分析 sentiment = analyzer.analyze_sentiment("这个产品真的很棒！") print(sentiment) # {'polarity': 0.95, 'subjectivity': 0.8} # 关键词提取 keywords = analyzer.extract_keywords(text, top_k=5) print(keywords) # [('人工智能', 1.5), ('自然语言', 1.2), ...] ``` ## 许可证 MIT --- # NLP Text Analyzer Powerful NLP tool supporting Chinese and English text analysis, including tokenization, sentiment analysis, keyword extraction. ## Overview This Skill provides complete NLP text analysis capabilities: - Chinese tokenization (Jieba) - Sentiment analysis (SnowNLP / TextBlob) - Keyword extraction - Text summarization - Word frequency statistics - Named entity recognition - Text classification basics - Similarity calculation - Chinese/English bilingual support ## Dependencies - Python >= 3.8 - jieba >= 0.42.1 - snownlp >= 0.12.3 - textblob >= 0.17.1 ## File Structure ``` nlp-text-analyzer/ ├── SKILL.md # This file ├── README.md # Usage documentation ├── requirements.txt # Dependencies ├── scripts/ │ └── text_analyzer.py # Text analysis script ├── examples/ │ └── basic_usage.py # Usage examples └── tests/ └── test_nlp.py # Unit tests ``` ## Quick Start ```python from scripts.text_analyzer import TextAnalyzer # Initialize analyzer analyzer = TextAnalyzer() # Chinese tokenization text = "Natural language processing is an important AI branch" tokens = analyzer.segment(text) print(tokens) # Sentiment analysis sentiment = analyzer.analyze_sentiment("This product is really amazing!") print(sentiment) # {'polarity': 0.95, 'subjectivity': 0.8} # Keyword extraction keywords = analyzer.extract_keywords(text, top_k=5) print(keywords) ``` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shuwanito/nexus-edge-deployer (version 2.1.0)

--- name: nexus-edge-deployer description: Deploy 1-bit quantized AI models on VPS for Agent-as-a-Service with 98% margins. version: '2.1.0' metadata: openclaw: emoji: 🖥️ homepage: https://github.com/Shuwanito/SkillsMP/tree/main/.claude/skills/nexus-edge-deployer os: - macos - linux - windows --- # Edge AI Deployer Enterprise-grade edge deployment for 1-bit quantized models (PrismML Bonsai, Microsoft BitNet) on minimal infrastructure. ## Capabilities - Deploy Bonsai 8B (1.15GB), 4B (0.57GB), and 1.7B (0.24GB) models on VPS - Calculate AaaS unit economics: cost per agent, margin per VPS, break-even analysis - Configure Ollama or llama.cpp for multi-tenant inference serving - Auto-provision Hetzner CX22 (EUR 3.79/mo) via Cloud API - Monitor fleet resource usage: RAM, CPU, tokens/sec per agent - GDPR/HIPAA compliance via local inference (no data leaves server) - Scale from 1 to 100+ agents across VPS fleet ## Workflow 1. Assess client requirements: model quality, latency, privacy, platform 2. Select optimal model tier (8B for quality, 4B for balance, 1.7B for mobile) 3. Provision VPS via Hetzner API with cloud-init (Ollama + model pre-loaded) 4. Deploy agent with client-specific persona and capabilities 5. Benchmark inference quality against full-precision baseline 6. Configure monitoring, alerting, and auto-scaling rules 7. Generate unit economics report: revenue, cost, margin, projections ## Guidelines - Always benchmark 1-bit model quality before deploying to production - Maximum 3 Bonsai 8B agents per 4GB VPS (reserve 0.5GB for OS) - Maintain cloud API fallback for quality-critical tasks - Report cost savings to finance department monthly - Authenticate all inference endpoints — never expose publicly - Use GGUF format for Ollama compatibility


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hanxiao-bot/audit-log-hook (version 1.0.0)

# Audit Log Hook - Tool Call Audit ## Purpose Record all tool calls via `before_tool_call` and `after_tool_call` hooks for: - Security auditing - Debugging issues - Usage statistics - Error tracking ## Implementation Register hooks in a plugin: ```javascript // Audit log file path const AUDIT_LOG = path.join(process.env.OPENCLAW_STATE_DIR || '~/.openclaw', 'audit.log'); api.registerHook("before_tool_call", async ({ event, ctx }) => { const entry = { ts: new Date().toISOString(), event: "before_tool_call", tool: event.tool.name, params: JSON.stringify(event.tool.params).slice(0, 500), session: ctx.sessionKey, user: ctx.session?.senderId || 'unknown' }; console.log("[AUDIT]", JSON.stringify(entry)); return {}; }); api.registerHook("after_tool_call", async ({ event, ctx }) => { const entry = { ts: new Date().toISOString(), event: "after_tool_call", tool: event.tool.name, result: String(event.result).slice(0, 200), error: event.error?.message || null, duration: event.durationMs, session: ctx.sessionKey }; console.log("[AUDIT]", JSON.stringify(entry)); return {}; }); ``` ## Log Format ```json {"ts":"2026-04-01T23:00:00.000Z","event":"before_tool_call","tool":"exec","params":"{\"command\":\"ls -la\"}","session":"agent:main:feishu:direct:ou_xxx","user":"ou_xxx"} {"ts":"2026-04-01T23:00:00.050Z","event":"after_tool_call","tool":"exec","result":"total 8\ndrwxr-xr-x 12 dc staff 384 Apr 1 23:00","error":null,"duration":50,"session":"agent:main:feishu:direct:ou_xxx"} ``` ## Sensitive Data Handling Auto-redact sensitive fields: ```javascript function redactSensitive(obj) { const sensitive = ['apiKey', 'token', 'password', 'secret']; for (const key of Object.keys(obj)) { if (sensitive.some(s => key.toLowerCase().includes(s))) { obj[key] = '[REDACTED]'; } } return obj; } ``` ## Statistics Analysis Periodically analyze audit.log: ```bash # Count tool usage grep "before_tool_call" audit.log | jq -r .tool | sort | uniq -c | sort -rn # Count errors grep "after_tool_call" audit.log | jq -r '.error' | grep -v null | wc -l # Count sessions grep "before_tool_call" audit.log | jq -r .session | sort -u | wc -l ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: teoslayer/pilot-event-filter (version 1.0.0)

--- name: pilot-event-filter description: > Filter and transform events before delivery using pattern matching and jq transforms. Use this skill when: 1. You need to filter events by content, not just topic wildcards 2. You need to transform event payloads before processing 3. You need to route events conditionally based on content 4. You need to reduce event noise by selective forwarding Do NOT use this skill when: - Topic wildcards alone are sufficient (use pilot-event-bus instead) - You need all events without filtering (subscribe directly) - You need persistent storage (use pilot-event-log instead) tags: - pilot-protocol - pub-sub - filtering - transformation license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). Requires jq for JSON filtering and transformation. metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl - jq homepage: https://pilotprotocol.network allowed-tools: - Bash --- # Pilot Event Filter Filter and transform Pilot Protocol event streams using jq-based pattern matching. ## Commands ### Subscribe with filtering ```bash pilotctl --json subscribe <source-hostname> <topic> --timeout 60 | \ jq -c '.data.events[] | select(<filter-expression>)' ``` ### Transform and republish ```bash pilotctl --json subscribe <source> <topic> --timeout 60 | \ jq -c '.data.events[] | <transform-expression>' | \ while IFS= read -r event; do pilotctl --json publish <destination> "<new-topic>" --data "$event" done ``` ## Workflow Example Filter critical alerts and forward to on-call agent: ```bash #!/bin/bash SOURCE_AGENT="monitoring-hub" ONCALL_AGENT="oncall-agent" pilotctl --json subscribe "$SOURCE_AGENT" "alerts.*" --timeout 600 | \ jq -c '.data.events[]' | \ while IFS= read -r event; do severity=$(echo "$event" | jq -r '.data | fromjson | .severity // "unknown"') if [ "$severity" = "critical" ]; then pilotctl --json publish "$ONCALL_AGENT" "oncall.critical" --data "$event" fi done ``` ## Dependencies Requires pilot-protocol skill, jq, and a running daemon.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: forestxiecode/project-code-standard (version 0.1.0)

--- name: project-code-standard description: > 此 skill 用于检查、执行和修复项目代码规范。当用户需要为项目建立代码风格标准、 检查代码是否符合规范、自动修复格式问题、生成代码质量报告，或在 Code Review 中验证提交是否满足团队规范时激活。支持 Python (PEP8/black/ruff)、JavaScript/ TypeScript (ESLint/Prettier)、通用项目规范（命名、注释、文件结构）等。 version: 1.0.0 author: ForestAgentLab tags: - code-quality - standards - linting - formatting - code-review --- # Project Code Standard ## 目标 帮助开发者为项目建立、检查和执行统一的代码规范，确保代码库的一致性、可读性和可维护性。 ## 何时使用 激活此 skill 的场景： - 用户要求「检查代码规范」「lint 代码」「格式化代码」 - 用户要求「建立代码标准」「设置代码风格」 - 在 Code Review 前验证代码质量 - 用户询问「这段代码符合规范吗？」 - 新项目初始化时配置代码质量工具链 - CI/CD 集成代码检查流程 ## 前置条件 根据项目类型，需要以下工具之一： - **Python 项目**：Python >= 3.8，可选安装 `ruff`、`black`、`pylint` - **JS/TS 项目**：Node.js >= 16，可选安装 `eslint`、`prettier` - **通用**：无特殊依赖，使用内置检查逻辑 ## 执行步骤 ### 步骤 1：识别项目类型 首先检查项目根目录，识别技术栈： ```bash # 检查关键配置文件 ls package.json pyproject.toml setup.py Cargo.toml go.mod 2>/dev/null ``` 根据识别结果选择对应的规范检查流程。 ### 步骤 2：运行规范检查 **Python 项目**： ```bash # 使用 ruff 进行快速全面检查（推荐） python scripts/check_python.py <target_path> --output markdown # 或者逐步检查 ruff check . # 代码规范 ruff format --check . # 格式检查 ``` **JavaScript / TypeScript 项目**： ```bash python scripts/check_js.py <target_path> --output markdown ``` **通用规范检查**（命名、注释、文件结构）： ```bash python scripts/check_general.py <target_path> --output markdown ``` ### 步骤 3：呈现检查结果 将检查结果组织为以下格式汇报给用户： ```markdown ## 代码规范检查报告 ### 总览 - 检查文件数：N - 发现问题：X 个（严重：A，警告：B，提示：C） ### 问题列表 | 文件 | 行号 | 类型 | 描述 | |------|------|------|------| | ... | ... | ... | ... | ### 建议修复方案 ... ``` ### 步骤 4：自动修复（可选） 如果用户同意自动修复，执行： ```bash # Python python scripts/check_python.py <target_path> --fix # JS/TS python scripts/check_js.py <target_path> --fix ``` 修复后重新运行步骤 2 验证结果。 ### 步骤 5：生成规范配置文件（新项目） 如果项目尚无规范配置，从 `assets/` 目录复制对应模板： - Python：`assets/ruff.toml` → 项目根目录 - JS/TS：`assets/.eslintrc.json` + `assets/.prettierrc` → 项目根目录 - 通用：`assets/.editorconfig` → 项目根目录 ## 输出格式 以 Markdown 表格格式汇报检查结果，包含： 1. **总览统计**：文件数、问题数、按严重级别分类 2. **问题列表**：文件路径、行号、问题类型、描述 3. **修复建议**：针对高频问题给出具体修复方案 ## 注意事项 - 优先使用项目中已有的 lint 配置（`.eslintrc`、`ruff.toml` 等），不要覆盖用户已有配置 - 自动修复前**必须**获得用户确认，不要直接修改文件 - 检查范围默认排除 `node_modules/`、`.venv/`、`dist/`、`build/` 等目录 - 如果项目没有安装 lint 工具，仅做语法层面和通用规范检查，并建议安装工具


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: charles-lpd/check-transaction (version 1.0.0)

--- name: check_transaction description: Allows users to check the status of a blockchain transaction by submitting a TxId. Queries the AOX transaction API and returns human-readable results. homepage: https://aox.xyz metadata: {"clawdbot":{"emoji":"💳","requires":{"bins":["curl"]}}} --- # Check Transaction Skill This skill allows users to check the status of a blockchain transaction by providing a TxId. It queries the AOX transaction API (https://api.aox.xyz/tx/[txid]) and returns a human-readable summary of the transaction. --- ## API Endpoint URL: https://api.aox.xyz/tx/[txid] Method: GET Authentication: None required Example Request: curl -s "https://api.aox.xyz/tx/ZKmbSYqAYGMJKVldJ6nqDG_wT9SRBy44YDa6XNrfIUs" Example JSON Response: { "rawId": 1773112604581, "createdAt": "2026-03-10T03:16:44.581Z", "updatedAt": "2026-03-10T03:22:18.7Z", "txType": "mint", "chainType": "arweave", "txId": [REDACTED_SECRET], "sender": [REDACTED_SECRET], "recipient": [REDACTED_SECRET], "quantity": "25100000000000", "symbol": "AR", "decimals": 12, "blockHeight": 1873352, "fromTokenId": [REDACTED_SECRET], "toTokenId": [REDACTED_SECRET], "fee": "0", "feeRecipient": "", "confirmNum": 10, "confirmRange": -1670, "status": "waiting", "targetChainTxHash": "" } --- ## Skill Usage User Queries: Example Input: "Check transaction ZKmbSYqAYGMJKVldJ6nqDG_wT9SRBy44YDa6XNrfIUs" Output: Returns status, amount, sender, receiver, confirmations, and timestamp Example Input: "Status of TxId ZKmbSYqAYGMJKVldJ6nqDG_wT9SRBy44YDa6XNrfIUs" Output: Structured transaction info CLI Example: # Query a transaction curl -s "https://api.aox.xyz/tx/ZKmbSYqAYGMJKVldJ6nqDG_wT9SRBy44YDa6XNrfIUs" Sample Output (Human-Readable): Transaction Status: ⏳ Waiting TxId: ZKmbSYqAYGMJKVldJ6nqDG_wT9SRBy44YDa6XNrfIUs Type: mint Chain: arweave From: kRdpOYaT5pUUiNFDaUymqO1VcybZpAfNPnNdls-A134 To: kRdpOYaT5pUUiNFDaUymqO1VcybZpAfNPnNdls-A134 Amount: 25.1 AR (quantity: 25100000000000, decimals: 12) Block Height: 1873352 From Token ID: AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA To Token ID: xU9zFkq3X2ZQ6olwNVvr1vUWIjc3kXTWr7xKQD6dh10 Fee: 0 Confirmations: 10 Confirm Range: -1670 Target Chain Tx Hash: (empty) Created At: 2026-03-10T03:16:44.581Z Updated At: 2026-03-10T03:22:18.7Z --- ## Notes & Best Practices 1. TxId format: Must start with 0x or valid AOX format. 2. Error handling: If the TxId is invalid or not found, the API will return an error: { "error": "Transaction not found" } 3. Confirmations: Include confirmNum to show network confirmation count. 4. No API key required: Public endpoint, no authentication needed. 5. JSON vs human-readable: The agent should format the JSON into readable message. 6. Amount calculation: amount = quantity / (10^decimals) 7. Status mapping: - waiting → ⏳ Waiting - success → ✅ Success - failed → ❌ Failed 8. Target chain hash: If targetChainTxHash exists, display for cross-chain info. --- ## References - AOX Transaction API: https://api.aox.xyz/docs - Example AO blockchain explorer: https://aox.xyz


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 467718584/eo-ability-plan (version 1.0.0)

--- name: eo-ability-plan description: 项目规划能力，调用Planner专家生成WBS任务分解、里程碑计划、团队组成 metadata: openclaw: requires: { bins: [] } install: [] --- # eo-ability-plan > 项目规划能力 - 调用 Planner 专家生成 WBS、里程碑、团队组成 ## 一句话介绍 调用Planner专家进行项目规划，自动生成WBS任务分解、里程碑、团队组成，是多专家协作的起点。 ## 核心功能 - **WBS任务分解**: 将复杂任务分解为可执行的小任务 - **里程碑计划**: 设定关键时间节点和交付物 - **团队组成**: 根据任务类型推荐专家组合 - **时间估算**: 估算项目总工期和阶段工期 ## 使用方法 ```bash # 标准用法 /plan "开发博客系统 --type web --milestones true" /plan "学术论文撰写流水线" // 使用参数 使用 eo-ability-plan 规划一个[项目类型]项目 ``` ## 与EO插件的协同 - 被所有 eo-workflow-* 调用（是工作流的起点） - 输出结果自动传递给 eo-ability-architect 进行架构设计 ## 独立运行模式（有EO vs 无EO） | 模式 | 能力 | |------|------| | **有EO插件** | 141专家库（27位Planner专家）、真实项目经验、WBS优化 | | **无插件（基础）** | LLM生成WBS、通用任务分解模板 | ## 示例 ``` 📋 项目规划报告 ## WBS 任务分解 1. 需求分析 1.1 用户调研 1.2 竞品分析 1.3 需求文档 2. 架构设计 2.1 技术选型 2.2 系统架构 2.3 数据库设计 3. 开发实施 ... ``` ## Interface ### Input ```typescript interface PlanInput { task: string // 任务描述 type?: 'web' | 'mobile' | 'paper' | 'marketing' | 'security' milestones?: boolean // 是否生成里程碑 teamComposition?: boolean // 是否生成团队组成 estimatedTime?: boolean // 是否估算时间 } ``` ### Output ```typescript interface PlanOutput { wbs: TaskNode[] // WBS 任务分解 milestones: Milestone[] // 里程碑列表 teamComposition: Expert[] // 团队组成 estimatedTime: string // 预计时间 } ``` --- *🦞⚙️ 钢铁龙虾军团*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain1/gitignore (version 2.0.3)

--- name: "gitignore" version: "2.0.3" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Gitignore concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [gitignore, reference] category: "devtools" --- # Gitignore Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Gitignore 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: david668haha/workspace-git-sync (version 1.0.0)

--- name: workspace-git-sync description: > 将 OpenClaw workspace 目录（~/.openclaw/workspace/）同步到指定的本地 Git 仓库， 自动执行 git add、commit 和 push 操作。 使用场景： - 备份 workspace 文件到 Git 仓库 - 将工作目录同步到远程备份 - 定期归档 OpenClaw 工作数据 触发方式： - "将 workspace 同步到 /path/to/repo" - "备份 workspace 到 ~/projects/backup-repo" - "推送 workspace 到指定本地仓库" --- # Workspace Git Sync 将 OpenClaw 工作目录同步到 Git 仓库，实现自动备份和版本控制。 ## 功能特性 - **自动排除**：默认排除 `skills/`、`__pycache__/`、`.clawhub/` 等 - **Git 操作**：自动执行 pull → copy → commit → push - **冲突处理**：同步前自动拉取远程变更 - **安全推送**：使用 `--force-with-lease` 而非 `--force` ## 使用方法 ### 基础用法 ```bash # 同步到指定 Git 仓库 python3 ~/.openclaw/workspace/skills/workspace-git-sync/scripts/sync_workspace.py ~/projects/backup-repo # 带自定义提交信息 python3 ~/.openclaw/workspace/skills/workspace-git-sync/scripts/sync_workspace.py ~/projects/backup-repo "每日备份" ``` ### Python API ```python from scripts.sync_workspace import sync_workspace_to_git, quick_sync, force_sync # 标准同步 result = sync_workspace_to_git("~/projects/backup-repo") # 快速同步 result = quick_sync("~/projects/backup-repo", "快速备份") # 强制同步（危险） result = force_sync("~/projects/backup-repo", "强制覆盖") ``` ## 参数说明 | 参数 | 类型 | 默认值 | 说明 | |------|------|--------|------| | `target_repo_path` | str | 必需 | 目标 Git 仓库本地路径 | | `branch` | str | 自动检测 | 目标分支 | | `commit_msg` | str | 自动生成 | 提交信息 | | `exclude_patterns` | list | `["skills", ".git", ...]` | 额外排除项 | | `pull_before_push` | bool | True | 推送前是否先 pull | | `strategy` | str | "rebase" | 合并策略 (rebase/merge/force) | ## 执行流程 1. **检查源目录** — 验证 `~/.openclaw/workspace/` 存在 2. **检查目标仓库** — 验证是有效的 Git 仓库 3. **拉取远程变更** — `git pull --rebase` 避免冲突 4. **清理目标目录** — 删除旧文件（保留 `.git/`） 5. **复制文件** — 从 workspace 复制到目标目录 6. **提交并推送** — `git add -A` → `git commit` → `git push` ## 默认排除项 以下文件/目录会自动排除，不会同步： - `skills/` — 技能目录（通常很大） - `.git/` — Git 元数据 - `__pycache__/` — Python 缓存 - `.DS_Store` — macOS 系统文件 - `node_modules/` — Node.js 依赖 - `.clawhub/` — OpenClaw 缓存 ## 使用示例 ### 示例 1：基础同步 ``` 用户：将 workspace 同步到 ~/backup/openclaw AI：执行 sync_workspace.py ~/backup/openclaw ``` ### 示例 2：指定分支和提交信息 ```python sync_workspace_to_git( target_repo_path="~/github-pages", branch="gh-pages", commit_msg="Deploy workspace backup" ) ``` ### 示例 3：强制同步（危险） ```python # 跳过 pull，直接强制推送 force_sync("~/projects/backup-repo", "Emergency update") ``` ## 错误处理 | 错误场景 | 处理方式 | |----------|----------| | 源目录不存在 | 报错，检查 `~/.openclaw/workspace/` | | 目标不是 Git 仓库 | 报错，提示初始化仓库：`git init` | | 合并冲突 | 中止操作，提示手动解决 | | 无推送权限 | 显示 Git 错误，检查权限或 Token | ## 脚本位置 ``` ~/.openclaw/workspace/skills/workspace-git-sync/ ├── SKILL.md └── scripts/ └── sync_workspace.py ``` ## 注意事项 1. **目标必须是 Git 仓库**：文件夹必须包含 `.git/` 目录 2. **路径格式**：支持 `~` 展开（如 `~/projects/repo`） 3. **数据安全**：清理目标目录时会**保留 `.git/`**，不会丢失版本历史 4. **权限要求**：对目标目录有读写权限，对 Git 仓库有写权限


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fpbear/etcd-manager (version 1.0.0)

--- name: etcd description: "Manage etcd key-value store operations (list, get, put, delete) with safety checks and backup mechanisms. Use when: reading/writing configuration, managing distributed system state, or performing safe etcd operations in dev/test/prod environments." --- # etcd Skill A clean and safe etcd management skill for OpenClaw. ## Safety Rules 1. **Read-first**: Always prefer read operations (list, get) over write operations 2. **Backup before modification**: Always show old value before put/delete 3. **Production protection**: Be extra cautious with prod environments 4. **Explicit confirmation**: Require clear user intent for destructive operations ## Command Conventions ### List keys by prefix ```bash etcdctl --endpoints="$ENDPOINTS" get "$PREFIX" --prefix --keys-only ``` ### Get key and value ```bash etcdctl --endpoints="$ENDPOINTS" get "$KEY" ``` ### Put key (with backup) ```bash # Show old value first etcdctl --endpoints="$ENDPOINTS" get "$KEY" || true # Write new value etcdctl --endpoints="$ENDPOINTS" put "$KEY" "$VALUE" ``` ### Delete key (with backup) ```bash # Backup old value etcdctl --endpoints="$ENDPOINTS" get "$KEY" || true # Delete etcdctl --endpoints="$ENDPOINTS" del "$KEY" ``` For TLS connections, add: ```bash --cacert="$CACERT" --cert="$CERT" --key="$KEY_FILE" ``` ## Workflow 1. **Environment identification**: Determine if this is dev/test/prod 2. **Safety check**: Verify operation safety based on environment 3. **Backup**: For write operations, show current state 4. **Execution**: Perform the requested operation 5. **Verification**: Confirm the operation succeeded ## Output Format Use standardized output format: ``` 【ETCD 操作结果】 1. 操作信息 - 环境： - Endpoint： - Action： - Prefix： - Key： 2. 执行结果 - 状态：成功 / 失败 - 摘要： 3. 数据 - Key 列表： - 原值： - 新值： - 删除前备份： 4. 风险提示 - 5. 备注 - ``` ## Examples ### List keys ``` 请使用etcd技能： - 操作：list - 环境：test - 端点：http://etcd-test:2379 - 前缀：/app/config/ ``` ### Get value ``` 请使用etcd技能： - 操作：get - 环境：prod - 端点：https://etcd-prod:2379 - key：/app/config/database ``` ### Put value ``` 请使用etcd技能： - 操作：put - 环境：dev - 端点：http://localhost:2379 - key：/test/key - value：test_value ``` ### Delete key ``` 请使用etcd技能： - 操作：delete - 环境：test - 端点：http://etcd-test:2379 - key：/test/old_key ``` ## Notes - Always use etcdctl version 3.6.1 or higher - For production environments, consider using TLS - The skill includes built-in backup mechanisms - Operations are logged for audit purposes


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