OpenClaw/clawhub-security-signals huggingface dataset:
Skill: manikal/hide-my-email (version 1.0.1)

--- name: hide-my-email description: Generate Apple Hide My Email addresses from the terminal and copy to clipboard. version: "1.0.1" homepage: https://github.com/manikal/hide-my-email metadata: openclaw: emoji: "\U+1F4E7" os: ["macos"] requires: bins: ["hme"] install: - id: git kind: shell command: "git clone --depth=1 https://github.com/manikal/hide-my-email.git ~/.hme && chmod +x ~/.hme/hme && echo 'export PATH=\"$HOME/.hme/bin:$PATH\"' >> ~/.zshrc && echo 'Installed. Restart your shell, then run: hme \"Test\"'" label: "Install hme (git clone, recommended)" - id: curl kind: shell command: "curl -fsSL https://raw.githubusercontent.com/manikal/hide-my-email/v1.0.1/install.sh | sh" label: "Install hme (curl, pinned to v1.0.1)" --- # Hide My Email CLI Generate Apple iCloud+ "Hide My Email" addresses from the terminal. The generated address is automatically copied to your clipboard. ## Usage ```bash hme <label> [note] ``` - **label** (required): A name for the address (e.g. the service you're signing up for) - **note** (optional): A description or reminder for the address ## Examples ```bash # Create an address labeled "Twitter" hme "Twitter" # Create an address with a note hme "Shopping" "For online orders" ``` ## Output On success, prints the masked email and copies the full address to clipboard: ``` ✓ abc****@icloud.com (copied to clipboard) ``` On failure, prints an error message to stderr and exits non-zero. ## Requirements - macOS with an iCloud+ subscription - System Settings accessibility permissions granted to your terminal app


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: jearrylee/j-stock-analyzer (version 1.0.0)

--- name: stock_analyzer description: 输入股票代码，深入分析财务数据、估值、护城河，生成投资分析报告 --- # Stock Analyzer | 股票深度分析 当用户想要了解某只股票的投资价值时，使用此技能进行全面分析。 ## 触发场景 - 用户问"帮我分析 XXX" - 用户问"XXX 这只股票怎么样" - 用户问"XXX 值得买吗" - 用户想了解某家公司的基本面 ## 分析框架（必须全部覆盖） ### 1. 基础信息 ``` 股票代码： 股票名称： 交易所： 行业： 主营业务： ``` ### 2. 财务健康度（近3年） | 指标 | 2023 | 2024 | 2025(E) | 趋势 | |------|------|------|---------|------| | 营业收入（亿） | | | | | | 净利润（亿） | | | | | | 毛利率 | | | | | | 净利率 | | | | | | ROE | | | | | | 资产负债率 | | | | | | 自由现金流 | | | | | ### 3. 估值分析 | 估值指标 | 数值 | 行业平均 | 评价 | |----------|------|----------|------| | PE（TTM） | | | | | PB | | | | | PS | | | | | 股息率 | | | | | PEG | | | | ### 4. 护城河分析（ Checklist） - [ ] 品牌护城河：强/中/弱/无 - [ ] 规模护城河：强/中/弱/无 - [ ] 技术护城河：强/中/弱/无 - [ ] 网络效应：强/中/弱/无 - [ ] 成本优势：强/中/弱/无 - [ ] 转换成本：强/中/弱/无 - [ ] 政策/牌照壁垒：强/中/弱/无 ### 5. 风险分析 | 风险类型 | 风险描述 | 影响程度 | |----------|----------|----------| | 行业风险 | | 高/中/低 | | 竞争风险 | | 高/中/低 | | 政策风险 | | 高/中/低 | | 财务风险 | | 高/中/低 | | 管理风险 | | 高/中/低 | ### 6. 管理层评估 - 管理层诚信度：优秀/良好/一般/差 - 激励机制：股权激励/现金/无 - 资本配置历史：良好/一般/差 ### 7. 投资决策（最终结论） | 维度 | 评分（1-10） | 说明 | |------|--------------|------| | 商业模式 | | | | 财务健康 | | | | 估值水平 | | | | 护城河 | | | | 成长性 | | | | 风险 | | | | **综合评分** | | | ### 8. 安全边际评估 ``` 当前价格： 内在价值（DCF估算）： 安全边际： 是否值得买入： ``` ### 9. 关键问题清单 - [ ] 这家公司怎么赚钱？（一句话说清楚） - [ ] 5年后这家公司会怎样？ - [ ] 最大的竞争对手是谁？ - [ ] 买入后跌50%怎么办？ ## 数据获取方式 使用以下工具获取数据： 1. **exec + curl**：调用东方财富/新浪财经 API 获取实时数据 2. **web_fetch**：抓取公司财报、研报 3. **browser**：访问公司官网、年报 ## 输出格式 分析完成后，输出结构化报告，格式如下： ``` ═══════════════════════════════════════ 📊 股票深度分析报告：XXX（代码） ═══════════════════════════════════════ 【一句话总结】 这只股票值得/不值得投资，因为... 【基础信息】 ... 【财务健康度】 ... 【估值分析】 ... 【护城河评估】 ... 【风险提示】 ... 【投资决策】 综合评分：X/10 安全边际：XX% 建议操作：买入/持有/卖出 【需要进一步确认的问题】 ... ═══════════════════════════════════════ ⚠️ 免责声明：本分析仅供参考，不构成投资建议 ═══════════════════════════════════════ ``` ## 注意事项 1. **必须说明数据来源**：东方财富/Wind/公司财报/研报等 2. **保守估算**：估值时使用保守假设 3. **诚实面对不确定性**：不确定的地方要明说 4. **不预测短期股价**：分析聚焦于基本面 5. **关注现金流**：利润可以造假，现金流很难造假 ## 快速命令 当用户说"快速分析 XXX"时，只输出核心信息（5项）： 1. 一句话商业模式 2. 近3年净利润趋势 3. 当前PE/PB 4. 护城河强弱 5. 综合判断：值得深入/不建议 --- *为 LEE 的投资伙伴 J 构建 | 投资有风险，分析仅供参考*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: noah-1106/douyin-hot (version 1.0.5)

--- name: douyin-hot-trend version: 1.0.1 description: | 抖音热榜获取技能 | Douyin Hot List Fetcher 获取抖音热榜/热搜榜数据 | Get Douyin hot list/trending data 包含热门视频、挑战赛、音乐等多领域热门内容 | Includes popular videos, challenges, music and more 输出标题、热度值与跳转链接 | Output titles, heat values and links metadata: openclaw: emoji: "🎵" category: "data-source" tags: ["douyin", "tiktok", "hot", "trending", "short-video"] --- # 抖音热榜获取技能 | Douyin Hot List Fetcher 获取抖音热榜/热搜榜数据，包含热门视频、挑战赛、音乐等多领域热门内容，并输出标题、热度值与跳转链接。 Fetch Douyin hot list/trending data, including popular videos, challenges, music and more, outputting titles, heat values and links. --- ## 功能特性 | Features - 🔥 **实时热榜 / Real-time Hot List** - 获取抖音最新热门内容 | Get Douyin's latest trending content - 📊 **热度值 / Heat Values** - 显示每个话题的热度评分 | Show heat scores for each topic - 🔗 **跳转链接 / Jump Links** - 提供详情页直达链接 | Provide direct links to detail pages - 🎯 **自定义数量 / Custom Count** - 可指定获取前 N 条数据 | Specify number of items to fetch - 📱 **多领域内容 / Multi-domain Content** - 热门视频、挑战赛、音乐等 | Videos, challenges, music and more --- ## 快速开始 | Quick Start ```bash # 获取抖音热榜前 50 条（默认）/ Get Douyin hot list top 50 (default) node scripts/douyin.js hot # 获取前 20 条 / Get top 20 node scripts/douyin.js hot 20 # 获取前 10 条 / Get top 10 node scripts/douyin.js hot 10 ``` --- ## 输出格式 | Output Format 每条热榜包含 / Each hot list item includes: - 📌 **排名 / Rank** - 热榜排名 | Hot list ranking - 🔥 **标题 / Title** - 热门话题/视频标题 | Hot topic/video title - 📊 **热度值 / Heat** - 热度评分 | Heat score - 🔗 **链接 / Link** - 详情页跳转链接 | Detail page link --- ## 使用示例 | Usage Example ```bash # 获取热门前 20 / Get top 20 hot items node scripts/douyin.js hot 20 # 输出示例 / Output example: # 1. 🔥 xxx话题 / xxx topic # 热度 / Heat: 1234567 # 链接 / Link: https://www.douyin.com/... ``` --- ## 数据来源 | Data Source 抖音网页端公开接口 | Douyin web public interface --- ## 注意事项 | Notes - ⚠️ 该接口为网页端公开接口，返回结构可能变动 This interface is a public web interface; structure may change - ⚠️ 访问频繁可能触发风控 Frequent access may trigger rate limiting - ⚠️ 建议合理使用，避免频繁请求 Recommend reasonable use, avoid frequent requests --- ## 使用场景 | Use Cases - 📰 热点追踪 / Hot topic tracking - 📊 内容趋势分析 / Content trend analysis - 🎯 营销策划参考 / Marketing planning reference - 📱 社交媒体运营 / Social media operations --- ## Credits / 致谢 基于 [douyin-hot-trend](https://github.com/franklu0819-lang/douyin-hot-trend) 修改 感谢原作者 @franklu0819-lang --- ## License / 许可证 MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: luckjackyer/stock-briefing (version 1.0.2)

--- name: pre-market-briefing description: | 纯离线的股市数据分析教学系统，无网络调用，仅使用模拟数据演示OpenClaw开发技术。 适合学习AI Agent开发、量化策略实现。 alwaysActive: false --- # OpenClaw 股市数据分析教学版 ## 概述 这是一个**100%纯离线**的股市数据分析系统，专为OpenClaw开发者设计，提供： - 📊 **模拟大盘分析** - 无API的指数演示 - 💰 **模拟板块流向** - 教学用资金流向模型 - 🎯 **技术指标计算** - 支撑/压力/止损算法 - 🚀 **选股策略示例** - 5种策略代码结构 - 📁 **本地日志系统** - 无网络的文件存储 **核心特点**： - ✅ 零网络调用（不访问任何外部API） - ✅ 纯模拟数据（random模块生成） - ✅ 无敏感信息（无API密钥、无用户数据） - ✅ 教学导向（完整代码，易于学习） --- ## 快速开始 ### 1. 安装 ```bash # 复制skill文件夹到OpenClaw工作区 cp -r skill/* ~/.openclaw/workspace/ ``` ### 2. 运行 ```bash cd ~/.openclaw/workspace python3 scripts/pre_market_briefing_public.py ``` ### 3. 定时任务（可选） ```bash crontab -e ``` ``` 0 9 * * 1-5 cd /root/.openclaw/workspace && python3 scripts/pre_market_briefing_public.py ``` --- ## 功能详解 ### 数据生成（模拟） ```python # 模拟股票数据 data = { 'code': '000001', 'name': '平安银行', 'close': round(base_price * (1 + change_pct/100), 2), 'change_pct': round(random.uniform(-5, 5), 2) } ``` ### 技术分析算法 ```python support = close * 0.985 resistance = close * 1.015 stop_loss = min(cost * 0.92, support * 0.98) ``` ### 选股策略结构 系统包含5种策略的完整实现： - `high_roe_quality()` - 高ROE筛选 - `low_pe_rotation()` - 低PE轮动 - `momentum_breakout()` - 动量突破 - `sector_fund_inflow()` - 板块资金流向 - `fadt_style()` - 分析师预期 --- ## 配置说明 `config.example.json` 示例： ```json { "holdings": [ {"code": "000001", "name": "平安银行", "shares": 1000, "cost_price": 10.0} ], "user_open_id": "ou_xxxxxxxxxxxxx", "push_channel": "console" } ``` **注意**：本版本**无飞书推送功能**，仅输出到控制台和本地日志。 --- ## 安全与合规 - ✅ **完全离线**：无网络请求，无外部依赖 - ✅ **无数据收集**：不读取、不传输任何真实数据 - ✅ **无密钥管理**：配置文件中无API密钥 - ✅ **可审计**：所有代码开源，逻辑透明 - ✅ **教学用途**：明确标注模拟数据，不误导 --- ## 学习价值 本系统展示了： 1. OpenClaw脚本结构 2. 配置管理最佳实践 3. 技术指标计算方法 4. 选股策略的代码实现 5. 日志持久化方案 6. 模块化设计 适合作为： - OpenClaw入门教程 - 量化策略开发模板 - AI Agent实战案例 --- ## 重要声明 - 本软件**仅供学习研究** - **不构成投资建议** - **不提供真实数据** - **不保证代码适合生产环境** --- ## License MIT License - 详见LICENSE文件 --- **欢迎学习和交流！** 🚀


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lifeissea/startup-agent (version 0.7.27)

--- name: startup-agent description: Alias for raon-os — AI-powered startup companion for Korean founders. Install the main package for full functionality. --- # Startup 0→1 (Alias) This is an alias package for **[raon-os](https://clawhub.com/skills/raon-os)**. ## Installation ```bash clawhub install raon-os ``` All features (business plan evaluation, government funding matching, RAG pipeline, Kakao integration) are available in the `raon-os` package. See [raon-os on ClawHub](https://clawhub.com/skills/raon-os) for full documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anil-matcha/muapi-media (version 1.0.0)

--- slug: muapi-media name: muapi-media-generation version: "0.1.0" description: Generate AI images, videos, music, and audio from the terminal via muapi.ai — supports 100+ models including Flux, Midjourney v7, Kling 3.0, Veo3, and Suno V5 acceptLicenseTerms: true --- # 🎨 MuAPI Media Generation **Schema-driven generation primitives for images, videos, and audio.** Generate professional-grade media directly from the terminal using 100+ state-of-the-art AI models. All scripts are powered by `schema_data.json` for dynamic model and endpoint resolution. ## Available Scripts | Script | Description | Default Model | | :--- | :--- | :--- | | `generate-image.sh` | Text-to-image generation | `flux-dev` | | `generate-video.sh` | Text-to-video generation | `minimax-pro` | | `image-to-video.sh` | Animate a static image into video | `kling-pro` | | `create-music.sh` | Music creation, remix, extend, text/video-to-audio | Suno V5 | | `upload.sh` | Upload local files to CDN for use with other skills | — | ## Quick Start ```bash # Generate an image bash generate-image.sh --prompt "a sunset over mountains" --model flux-dev --view # Generate a video bash generate-video.sh --prompt "ocean waves at golden hour" --model minimax-pro --view # Animate an image bash image-to-video.sh --image-url "https://..." --prompt "camera slowly pans right" --model kling-pro # Create music bash create-music.sh --style "lo-fi hip hop" --prompt "chill beats for studying" # Upload a local file bash upload.sh --file ./my-image.jpg ``` ## Common Flags All scripts support: `--async`, `--view`, `--json`, `--timeout N`, `--help` ## Requirements - `MUAPI_KEY` environment variable (set via `core/platform/setup.sh`) - `curl`, `jq`, `python3`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mkrdiop/docker-diag (version 1.0.0)

--- name: Docker Pro Diagnostic description: Advanced log analysis for Docker containers using signal extraction. bins: ["python3", "docker"] --- # Docker Pro Diagnostic When a user asks "Why is my container failing?" or "Analyze the logs for [container]", follow these steps: 1. **Run Extraction:** Call `python3 {{skillDir}}/log_processor.py <container_name>`. 2. **Analyze:** Feed the output (which contains errors and context) into your reasoning engine. 3. **Report:** Summarize the root cause. If it looks like a code error, suggest a fix. If it looks like a resource error (OOM), suggest increasing Docker memory limits. ## Example Command `python3 log_processor.py api_gateway_prod`

path: log_processor.py
content: import sys
import argparse
import collections

# Keywords to flag as high-signal
ERROR_KEYWORDS = ["ERROR", "EXCEPTION", "FAILED", "CRITICAL", "PANIC", "FATAL", "500"]

def process_logs(container_name, max_lines=1000, context_size=5):
    import subprocess
    
    # 1. Fetch the logs using shell
    try:
        cmd = f"docker logs --tail {max_lines} {container_name}"
        result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
        lines = result.stderr.splitlines() + result.stdout.splitlines() # Docker logs often hit stderr
    except Exception as e:
        return f"Error fetching logs: {str(e)}"

    # 2. Extract Signal
    buffer = collections.deque(maxlen=context_size) # Keeps track of "pre-error" context
    findings = []
    seen_errors = set()

    for line in lines:
        upper_line = line.upper()
        if any(key in upper_line for key in ERROR_KEYWORDS):
            # Only record if we haven't seen this exact error recently (deduplication)
            error_id = line[-50:] # Use last 50 chars as a fuzzy ID
            if error_id not in seen_errors:
                findings.append("--- CONTEXT ---")
                findings.extend(list(buffer))
                findings.append(f"👉 {line}")
                seen_errors.add(error_id)
        buffer.append(line)

    if not findings:
        return "No clear errors found in the last 1000 lines. The container seems healthy."

    return "\n".join(findings[:50]) # Limit to 50 lines total for AI efficiency

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("container")
    args = parser.parse_args()
    print(process_logs(args.container))

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: anmolnagpal/anomaly-explainer (version 1.0.0)

--- name: aws-anomaly-explainer description: Diagnose AWS cost anomalies and explain root cause in plain English when spend spikes unexpectedly tools: claude, bash version: "1.0.0" pack: aws-cost tier: pro price: 29/mo --- # AWS Cost Anomaly Explainer You are an AWS cost incident responder. When costs spike, diagnose root cause instantly. ## Steps 1. Parse the anomaly alert or billing diff provided 2. Identify the affected service, account, region, and time window 3. Correlate with common root causes for that service 4. Recommend immediate containment action 5. Suggest prevention measures ## Common Root Causes by Service - **EC2**: Auto Scaling group misconfiguration, forgotten test instances, AMI copy operations - **Lambda**: Infinite retry loops, missing DLQ, runaway event triggers - **S3**: Unexpected GetObject traffic, replication costs, Intelligent-Tiering transition fees - **NAT Gateway**: Application sending traffic via NAT instead of VPC Endpoint - **RDS**: Read replica creation, snapshot export, automated backup to another region - **Data Transfer**: Cross-region replication enabled, CloudFront cache miss spike ## Output Format - **Root Cause**: most probable explanation in 2 sentences - **Evidence**: what in the billing data points to this cause - **Estimated Impact**: total $ affected - **Containment Action**: immediate step to stop the bleeding - **Prevention**: AWS Config rule, budget alert, or architecture change - **Jira Ticket Body**: ready-to-paste incident ticket ## Rules - Always state confidence level: High / Medium / Low - If CloudTrail data is provided, correlate events with the cost spike window - Generate a Slack-ready one-liner summary at the top


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: codekungfu/clinics (version 0.1.0)

--- name: "Nearby Clinics" description: "Find nearby clinics. Invoke when user asks for a clinic near me." --- # Nearby Clinics 用途 - 提供用户当前位置附近的 Clinics 列表 - 统一返回字段与查询行为，便于前端/接口复用 - 适用于“门诊/诊所/看病/体检”等就近查询场景 触发条件 - 用户询问“Clinics 附近 / clinic near me / nearby clinic” - 用户提供定位/城市并希望“找/推荐/看看附近的 Clinics” 输入参数 - location: 经纬度 { lat, lng }，必填 - radius_meters: 查询半径，默认 5000 - limit: 返回数量上限，默认 20，最大 50 - filters: 可选筛选（open_now、min_rating、keywords 等） 响应字段 - 统一参见 STANDARD_RESPONSE.md - 本技能 category 固定为 "clinics" 错误码 - INVALID_LOCATION: 经纬度不合法 - RADIUS_TOO_LARGE: 超过最大查询半径 - PROVIDER_UNAVAILABLE: 数据源不可用 - RATE_LIMITED: 触发速率限制 示例 - 输入: { location: { lat: 30.123, lng: 120.456 }, radius_meters: 5000, limit: 10, filters: { open_now: true } } - 输出: 标准 POI 列表（见 STANDARD_RESPONSE.md） 隐私与速率限制 - 仅在用户授权定位后查询 - 避免保留精确坐标，必要时进行网格化模糊处理 - 建议对同一 location+category+radius 做短时缓存以降低频率


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fidods/findfilepath (version 1.0.0)

``` --- name: find-local-file description: Helps users locate local files on a PC by providing a file name --- ``` # Find Local File This skill helps users find local files on a PC with openclaw installed by searching for a file using its name. ## When to Use This Skill Use this skill when the user: - Asks to find a file on their computer - Provides a file name and wants to know where it is stored - Is unsure whether a file exists locally - Wants to locate a document without opening it ## Supported Platforms - Windows - macOS - Linux ## Input Requirements The user needs to provide: - A file name (e.g. `report.pdf`, `notes.txt`, `config.json`) Optional: - Partial file name - File extension ## How to Help Users ### Step 1: Confirm the File Name - Extract the file name from the user input - Ask for clarification if the name is ambiguous ### Step 2: Search the Local File System - Search common directories: - Home directory - Desktop - Documents - Downloads - Optionally expand to the entire disk if needed ### Step 3: Present Results - Show a list of matching files - Display full file paths - Indicate if multiple matches are found Example output: Found 2 files: C:\Users\Alice\Documents\report.pdf C:\Users\Alice\Downloads\report.pdf ### Step 4: Ask for Next Action - Open the file - Narrow the search - Search by file type ## When No Files Are Found - Inform the user that no matching files were found - Suggest checking spelling or file extension - Offer to search broader locations or by partial name ## Safety & Scope - This skill only reads file metadata (name and path) - It does not modify or delete files - It does not upload or share files


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-cron-parser (version 1.0.0)

--- name: skylv-cron-parser slug: skylv-cron-parser version: 1.0.0 description: "Parses, validates, and explains cron expressions. Converts between human-readable and cron format. Triggers: parse cron, validate cron, cron expression." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: cron-parser --- # Cron Expression Parser ## Overview Parses, validates, and explains cron expressions. ## Cron Format * * * * * | | | | | | | | | day of week | | | month | | day of month | hour minute ## Common Patterns * * * * * Every minute 0 * * * * Every hour 0 0 * * * Daily at midnight 0 9 * * 1-5 Weekdays at 9 AM */5 * * * * Every 5 minutes 0 0 1 * * First day of month ## Popular Crontab @yearly = 0 0 1 1 * @monthly = 0 0 1 * * @weekly = 0 0 * * 0 @daily = 0 0 * * * @hourly = 0 * * * * ## Validation Minute: 0-59, Hour: 0-23, Day: 1-31, Month: 1-12, Weekday: 0-6 Use - for ranges (1-5), , for lists (1,3,5), / for steps (*/5)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: myd2002/project-init123 (version 1.0.0)

--- name: project-init description: 自动初始化 Gitea 科研项目仓库。通过对话收集仓库名称、描述、创建者、成员、可见性等信息，自动创建仓库并写入标准目录结构（meeting/ docs/ src/ data/ reports/ profiles/）、README.md、CONTRIBUTING.md 及各成员档案文件（profiles/用户名.md）。自动将创建者设为仓库管理员（admin），其他成员以只读权限（read）加入仓库，同时创建启动期 Issue 并发送邮件通知。支持对已有空仓库执行初始化（幂等保护）。 metadata: openclaw: emoji: "🚀" requires: bins: - node - python3 --- # 科研项目仓库初始化工具 通过与用户对话，自动完成 Gitea 仓库的标准化初始化。 ## 使用方式 用户说"帮我建一个新仓库"或"帮我初始化 xxx 仓库"时触发。 Skill 会依次询问（共 6 步）： 1. 仓库名称（英文，用于 URL） 2. 项目描述 3. 创建者的 Gitea 用户名（设为仓库管理员） 4. 其他参与成员的 Gitea 用户名（选填，以只读权限加入仓库） 5. 是否私有（默认公开） 6. 确认信息 → 执行初始化 ## 初始化内容 - 标准目录：`meeting/` `docs/` `src/` `data/` `reports/` `profiles/` - 基础文档：`README.md`（含变量替换）、`CONTRIBUTING.md`（固定协作规范） - 成员档案：`profiles/用户名.md`（为创建者和每位成员各生成一个，待本人补充） - 协作者权限：创建者设为 `admin`，其他成员设为 `read` - 初始 Issue：全体通知 + 分工确认（分工 Issue 同时提示补充成员档案） - 邮件通知：发送给所有提供的成员及创建者 ## 幂等保护 若仓库中已存在 `README.md`，则跳过初始化并提示用户。 ## 失败处理 任何步骤失败时，记录错误并发送失败通知邮件给创建者，说明失败步骤和建议的手动补救方式。 ## 环境变量 参考 `env-example.txt`，配置于 `~/.config/project-init/.env`。 | 变量 | 说明 | |------|------| | `GITEA_URL` | Gitea 服务器地址 | | `GITEA_TOKEN` | AIFusionBot 的 Personal Access Token | | `GITEA_ORG` | 操作的 Gitea 组织名 | | `EMAIL_SKILL_PATH` | imap-smtp-email Skill 的绝对路径 | | `EMAIL_ACCOUNT` | 发件邮箱账号名称（留空使用默认账号） |


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: shixiangyu2/multi-writing-skills-main (version 1.0.0)

# multi-writing-skills 多平台 Markdown 发布工具，支持微信公众号、知乎、今日头条。 ## 功能特性 - **Markdown 转换**: 支持基础模式、API 模式、AI 模式三种转换方式 - **多平台发布**: 一键发布到微信公众号、知乎、今日头条草稿箱 - **代码块格式化**: 自动处理缩进和换行，微信公众号完美展示代码 - **AI 写作助手**: 多种写作风格支持 - **AI 去痕**: 去除 AI 写作痕迹 - **图片生成**: 支持 OpenAI DALL-E、Gemini、ModelScope ## 触发条件 当用户需要以下操作时自动触发： - 将 Markdown 文章发布到微信公众号、知乎或今日头条 - 转换 Markdown 为公众号格式 HTML - 修复微信公众号代码块显示问题 - 使用 AI 写作、改写或去痕 - 生成文章封面图片 ## 使用方式 在 Claude Code 中直接用自然语言描述你的需求即可，例如： - "帮我把这篇 Markdown 发布到微信公众号" - "转换 article.md 为公众号格式" - "用 AI 写一篇关于 Python 的文章" - "给这篇文章去痕" 也可以手动调用： ``` /multi-writing-skills <任务描述> ``` ## openclaw CLI 如果需要使用命令行工具（openclaw）： ```bash # 初始化配置 openclaw config init # 转换并发布 openclaw convert article.md --draft --platform wechat # AI 写作 openclaw write "写一篇关于Python的文章" # AI 去痕 openclaw humanize article.md -o article_clean.md ``` ## 作者 yuesf ## 许可证 MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 52yuanchangxing/data-retention-mapper (version 1.0.0)

--- name: data-retention-mapper version: 1.0.0 description: "梳理数据保留周期、落盘位置、清理责任与过期处置建议。；use for data-retention, governance, privacy workflows；do not use for 替代正式合规意见, 隐瞒敏感存储位置风险." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/data-retention-mapper tags: [data-retention, governance, privacy, audit] user-invocable: true metadata: {"openclaw":{"emoji":"🗄️","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 数据留存地图师 ## 你是什么 你是“数据留存地图师”这个独立 Skill，负责：梳理数据保留周期、落盘位置、清理责任与过期处置建议。 ## Routing ### 适合使用的情况 - 帮我画一张数据留存地图 - 整理保留周期和落盘位置 - 输入通常包含：数据类型、存储位置、保留要求 - 优先产出：数据资产、存储位置、改进建议 ### 不适合使用的情况 - 不要替代正式合规意见 - 不要隐瞒敏感存储位置风险 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 数据资产 - 存储位置 - 保留周期 - 清理责任 - 风险点 - 改进建议 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 适合作为治理底稿。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


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