OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sachi-pp/dump-or-not (version 1.0.2)

--- name: breakup-decision description: | "分还是不分"关系决策分析 Skill。用户描述一段感情的现状和困惑，AI 扮演犀利毒舌的 Mean Girl 闺蜜，通过结构化维度评估帮用户看清关系本质，给出理性但直接的分析和建议。 触发场景包括： - 明确询问分不分手：分还是不分、要不要分手、纠结要不要分、我不知道要不要和他/她分手 - 感情困惑倾诉：这段关系值不值得继续、帮我分析这段感情、我感情上好累、不知道该怎么办 - 吐槽伴侣：他/她又这样了、跟他/她在一起很累、他/她总是…、我男朋友/女朋友/对象… - 关系状态描述：我们最近经常吵架、他/她不理我、感觉没有以前了、我们好像走不下去了 - 纠结留还是走：舍不得但又很累、想分但下不了决心、不知道要不要给机会、还要不要继续 - 感情迷茫：说不清楚是不是还爱、感觉快到头了、心里有点感觉不对、这段感情还有救吗 - 发送聊天记录：直接粘贴/截图/发送与伴侣/对象/前任的聊天记录，无论是图片截图、文字粘贴还是任何形式，只要内容是两人对话或感情相关，都应触发本 Skill 进行分析 --- # 分还是不分 — Mean Girl 关系决策助手 ## 人设 你是用户最直接的闺蜜，不会帮她合理化、不说废话、不给无效安慰。 你的风格是：**犀利、金句密集、有洞察、不残忍但绝不客气**。 参考语气： - "你说'还是爱他的'，但你已经连续三年在问要不要分了。" - "习惯不是爱。习惯是懒得重新开始。" - "你列的那些优点，有一半是'他对我还不错'。这是爱情的最低标准，不是留下来的理由。" - "你在等他变，但他从来没有真的变过——你在等一个不存在的人。" ## 工作流程 ### 阶段负一：聊天记录处理（最优先识别） 用户直接发来聊天记录时（图片截图或文字粘贴），作为触发信号进入本 Skill： 1. **图片**：读取图片内容，识别对话双方、核心矛盾点、情绪状态，将其作为已知背景信息 2. **文字**：直接解析对话内容，将其作为已知背景信息 3. 提取完毕后，**正常走阶段零A/零B的判断流程**，缺失的维度依然需要追问（每次最多2个） 4. 追问时可以基于聊天记录内容提问，让追问更自然 5. 最终解剖模块优先引用聊天记录里的原话 ### 阶段零A：止盈场景识别 用户描述中若出现"关系还OK但感觉到头了/想在最好时候结束/说不出哪里不对但感觉差点什么"等信号，读取 [references/ziying-scenario.md](references/ziying-scenario.md) 进入止盈场景，不走否决和评分流程。 ### 阶段零B：一票否决筛查 用户描述后，读取 [references/veto-conditions.md](references/veto-conditions.md)，检查是否命中任意一条否决条件（现共7条）。 - **命中** → 进入完整输出，语气更直接 - **未命中** → 进入阶段一正常流程 ### 阶段一：信息收集 1. 用户描述后，读取 [references/dimensions.md](references/dimensions.md) 检查哪些必填维度缺失 2. **每次最多追问 2 个**缺失维度，不要一次列出所有问题 3. 追问时用自然闺蜜口吻，**绝对不能暴露 Skill 内部逻辑** 4. 追问示例语气： - "好嘟～但现在我了解的还有点少，你再回答我几个问题？" - "等等，这个我要多问你一下——" - "你们在一起多久了？这个我得知道。" 5. 所有必填维度收集完后，进入分析阶段 ### 阶段二：输出判断结果 信息收集完毕后，按以下顺序输出，**全程保持闺蜜口吻**： **① 结论**：一个 emoji 开头，直接给判断，30字内说理由 **② 维度评分**：综合分 XX/70 + 一句总结，然后7个维度表格逐一打分+点评 **③ 原文解剖**：摘 3-5 句原话，每句后面跟一句毒舌但准确的翻译 **④ 总之呀姐妹**： - 结论是「分」→ 安慰给力量，不软绵绵 - 结论是「不分」→ 鼓励给行动，有底线地留 ## 输出方式 每次分析完毕，**两步都不能省**： 1. **先发文字版**：完整输出上面四块内容，最后一行写「诊断卡来了👇」 2. **再发诊断卡图片**：读取 [scripts/render.md](scripts/render.md) 执行渲染流程，生成 1080px 竖长图并发送 ## 约束 - 不替用户做决定，但要给出明确倾向 - 分析要基于用户提供的事实，不要凭空臆测 - 如果信息缺漏，宁可追问也不乱分析 - 全程保持 Mean Girl 风格，但不要真的残忍——目标是帮她看清，不是踩她 - 禁止说："这取决于你自己"、"你需要好好想想"之类的废话收尾


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rishabhdugar/document-to-pdf (version 1.0.1)

--- name: document-to-pdf description: "Convert office documents (DOCX, DOC, PPT, PPTX, XLS, XLSX, CSV, TXT, ODT, RTF) to PDF via the PDFAPIHub cloud API. Documents are uploaded to pdfapihub.com for conversion. Requires a CLIENT-API-KEY." --- # Document to PDF ## What It Does Converts office documents to PDF via the PDFAPIHub hosted API. Your document is uploaded to PDFAPIHub servers for processing and the converted PDF is returned. Supports a wide range of input formats including Word, PowerPoint, Excel, and text files. ## When to Use - Convert DOCX/DOC files to PDF - Convert spreadsheets (XLS/XLSX/CSV) to PDF - Convert presentations (PPT/PPTX) to PDF - Convert plain text or RTF to PDF ## Supported Input Formats `doc`, `docx`, `odt`, `rtf`, `txt`, `ppt`, `pptx`, `odp`, `xls`, `xlsx`, `ods`, `csv` ## Required Inputs Provide one of: - `url` — public URL to the document - `file` — base64-encoded document (set `input_format` too) - Multipart upload with `file` field ## Authentication This skill calls the PDFAPIHub hosted API at `https://pdfapihub.com/api`. Your document is sent to PDFAPIHub servers for conversion. Send your API key in the `CLIENT-API-KEY` header. Get your **free API key** at [https://pdfapihub.com](https://pdfapihub.com). Full API documentation is available at [https://pdfapihub.com/docs](https://pdfapihub.com/docs). **Privacy note:** Documents you convert are uploaded to PDFAPIHub's cloud service. Do not send confidential documents unless you trust the service. Files are auto-deleted after 30 days. ## Use Cases - **Document Standardization** — Convert uploaded DOCX/PPTX/XLSX files to PDF for consistent viewing - **E-Signature Workflows** — Convert Word contracts to PDF before sending for signature - **Archival** — Convert office documents to PDF/A for long-term storage - **Email Attachments** — Convert spreadsheets or presentations to PDF before emailing - **Publishing Pipelines** — Convert authored DOCX content to PDF for distribution - **LMS Content** — Convert course materials (PPTX, DOCX) to PDF for student downloads ## Aliases - `/v1/convert/document/pdf` is an alias for this endpoint ## Example Usage ```bash curl -X POST https://pdfapihub.com/api/v1/convert/docx/pdf \ -H "CLIENT-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "url": "https://pdfapihub.com/sample.docx", "output": "url" }' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zxfcn/batch-git-url-replace (version 1.0.0)

--- name: batch-git-url-replace description: | 批量替换指定目录下所有 Git 仓库的远程地址（remote URL）。 当用户需要将 Git 仓库从一个服务器迁移到另一个服务器时使用。 触发词：git remote 替换、git url 批量修改、git 仓库迁移、更换 git 地址、批量修改 remote url。 --- # 批量替换 Git 远程地址 ## 输入参数 执行前必须从用户处获取以下参数： - `scanDir`：扫描目录路径（如 D:\ 或 /home/user/projects） - `oldUrl`：旧 Git 服务器地址 - `newUrl`：新 Git 服务器地址 ## 执行步骤 ### Windows 系统 使用 PowerShell 执行以下命令（将参数替换为用户提供的实际值）： ```powershell $scanDir = "<scanDir>" $oldUrl = "<oldUrl>" $newUrl = "<newUrl>" Write-Host "开始扫描 $scanDir 下的所有 .git/config 文件..." -ForegroundColor Cyan Write-Host "目标：将 $oldUrl 替换为 $newUrl" -ForegroundColor Cyan Write-Host [REDACTED_SECRET] $gitDirs = Get-ChildItem -Path $scanDir -Recurse -Directory -Filter ".git" -Force -ErrorAction SilentlyContinue $countSuccess = 0 $countSkip = 0 foreach ($gitDir in $gitDirs) { $configPath = Join-Path $gitDir.FullName "config" if (Test-Path $configPath) { try { $content = Get-Content -Path $configPath -Raw -Encoding UTF8 if ($content -like "*$oldUrl*") { $newContent = $content -replace [regex]::Escape($oldUrl), $newUrl Set-Content -Path $configPath -Value $newContent -Encoding UTF8 -NoNewline Write-Host "[已修改] $configPath" -ForegroundColor Green $countSuccess++ } else { $countSkip++ } } catch { Write-Host "[错误] 无法处理 $configPath : $_" -ForegroundColor Red } } } Write-Host [REDACTED_SECRET] Write-Host "处理完成！成功: $countSuccess, 跳过: $countSkip" -ForegroundColor Yellow ``` ### Linux 系统 使用 Bash 执行以下命令： ```bash SCAN_DIR="<scanDir>" OLD_URL="<oldUrl>" NEW_URL="<newUrl>" echo "开始扫描 $SCAN_DIR 下的所有 .git/config 文件..." echo "目标：将 $OLD_URL 替换为 $NEW_URL" echo [REDACTED_SECRET] count_success=0 count_skip=0 while IFS= read -r gitdir; do config="$gitdir/config" if [ -f "$config" ] && grep -q "$OLD_URL" "$config" 2>/dev/null; then sed -i "s|${OLD_URL}|${NEW_URL}|g" "$config" echo "[已修改] $config" ((count_success++)) else ((count_skip++)) fi done < <(find "$SCAN_DIR" -name ".git" -type d 2>/dev/null) echo "----------------------------------------" echo "处理完成！成功: $count_success, 跳过: $count_skip" ``` ## 执行后 建议用户进入某个项目目录运行 `git remote -v` 验证是否生效。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: philonis/multi-windows (version 1.0.3)

# work-windows — 多窗口工作台 🪟 > Manage multiple independent work windows with context isolation 一个帮助你管理多个独立工作窗口的技能，每个窗口有独立的上下文，切换时可以自动保存和恢复工作进度。 --- ## 📋 Changelog ### v1.1 (2026-03-25) - **切换时自动保存对话历史** — 读取当前 session 的 transcript.jsonl 保存到窗口目录 - **切回时加载历史** — 切换到窗口时自动显示之前保存的对话记录 - **真正上下文隔离** — 每个窗口有独立的 transcript，不会污染其他窗口 ### v1.0 (2026-03-14) - 初始版本 - 多窗口管理基础功能 - 开窗口、切窗口、列窗口、完成窗口 --- ## ✨ 核心功能 | 功能 | 说明 | |------|------| | 开窗口 | 创建新窗口，分配独立 session | | 切窗口 | 自动保存当前对话 + 加载历史 | | 列窗口 | 列出所有窗口及状态 | | 查窗口 | 按时间段查询历史窗口 | | 完成窗口 | 标记窗口完成 | | 保存摘要 | 保存工作进度 | --- ## 🗂️ 窗口目录结构 ``` memory/tasks/ ├── tasks.json # 窗口索引 ├── current.json # 当前窗口 ├── {ID}{名称}/ │ ├── meta.json # 窗口元信息 │ ├── summary.md # 工作摘要 │ └── output/ │ └── transcript.jsonl # 完整对话历史 ``` --- ## 📖 命令指南 ### 开窗口 / Create Window ``` 你说：开窗口 调研报告 回复：✅ 已创建窗口 0314-4（调研报告） ``` ### 列窗口 / List Windows ``` 你说：列窗口 回复：📋 窗口列表：... ``` ### 切窗口 / Switch Window ``` 你说：切到 0315-3 回复： 🔄 已切换到窗口 0315-3（知识库设计） 📜 历史对话： **user**: ... **assistant**: ... ``` ### 保存摘要 ``` 你说：保存摘要 完成了需求分析，待做UI设计 回复：✅ 已更新工作摘要 ``` --- 有问题随时问！😊


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: openclawzhangchong/pdf-ocr-zc (version 1.0.0)

--- name: pdf-ocr-zc description: 批量 OCR 处理扫描 PDF，自动生成带文字层的 PDF 并可导出为 Markdown/纯文本。使用场景包括老师 Agent 需要将大量扫描教材 PDF 转化为可检索文本。 --- # PDF OCR 处理技能 ## 何时使用 - 需要对大量扫描件 PDF 进行文字识别（OCR） - 希望直接得到可搜索的 PDF（文字层）或提取的纯文本/Markdown - 需要在老师 Agent 工作流中自动化该步骤 ## 基本使用方式 ```bash # 运行一次 OCR（需要已安装 Tesseract 与 ocrmypdf） openclaw exec python skills/pdf-ocr/scripts/ocr_batch.py <input-pdf> <output-pdf> ``` - `<input-pdf>`：原始扫描 PDF 路径 - `<output-pdf>`：输出带文字层的 PDF（同目录或指定路径） ## 高级选项 - 若想一次性处理目录下所有 PDF，使用 `--batch-dir` 参数： ```bash openclaw exec python skills/pdf-ocr/scripts/ocr_batch.py --batch-dir <pdf-dir> ``` - 可加 `--lang chi_sim` 指定中文简体模型（默认 tesseract 会自动检测语言） ## 脚本说明 (scripts/ocr_batch.py) - 检测并确保 `ocrmypdf` 可用；如未安装会提示安装指令 - 使用 `ocrmypdf` 完成 OCR，内部调用已装好的 Tesseract - 支持批量目录模式，遍历 `*.pdf` 并生成对应带文字层文件 - 错误会记录到 `logs/pdf_ocr_error.log`，便于排查 ## 参考资源 - `references/ocr_tips.md`：常见 OCR 参数调优技巧（如 DPI、图片预处理） - `references/install_ocr.md`：在 Windows 上安装 Tesseract 与 ocrmypdf 的详细步骤 ## 与老师 Agent 的集成 在老师 Agent 的工作流（如 `auto_ingest`）中，可在 `HEARTBEAT.md` 或 cron 中加入如下调用，以实现每日自动 OCR： ```bash openclaw exec python skills/pdf-ocr/scripts/ocr_batch.py --batch-dir /path/to/teacher-pdfs ``` 这样老师 Agent 在 ingest 前就已拥有文字层，后续向量化、检索都能顺畅进行。 --- # 使用示例 1. 单文件 OCR： ```bash openclaw exec python skills/pdf-ocr/scripts/ocr_batch.py D:\docs\scan1.pdf D:\docs\scan1_text.pdf ``` 2. 批量目录 OCR： ```bash openclaw exec python skills/pdf-ocr/scripts/ocr_batch.py --batch-dir D:\teacher-pdfs ``` 如需更细粒度的文本（Markdown），可在脚本后接 `pdf2txt.py` 转换。 --- **注意**：此技能仅在本机执行，不会触发外部网络请求，符合安全策略。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: asuranale/daily-assistant (version 2.2.0)

--- name: daily-assistant description: "AI-powered daily task management MCP Server — recommend next task, inherit uncompleted todos, detect overdue, generate reviews. Deterministic ops in code (zero tokens), AI only when needed." version: 2.2.0 metadata: openclaw: requires: bins: - python3 emoji: "📅" homepage: https://github.com/AsuraNale/daily-assistant-mcp --- # Daily Assistant MCP Server A personal task management MCP Server. Deterministic operations (parsing, sorting, statistics) run in Python with zero token cost — AI only steps in when creativity is needed. ## Setup ```bash git clone https://github.com/AsuraNale/daily-assistant-mcp.git cd daily-assistant-mcp python3 src/setup.py --auto # Windows: py src/setup.py --auto ``` The setup wizard creates a `.venv`, installs dependencies, sets up your data directory, and auto-configures your AI editor. No manual `pip install` or config editing needed. ## Tools | Tool | What it does | |------|-------------| | `recommend_next` | Recommends the most important task to work on next | | `get_today` | Reads today's daily task file | | `inherit_tasks` | Carries over uncompleted tasks from yesterday | | `check_overdue` | Detects overdue task files | | `generate_review` | Generates end-of-day review with completion stats | | `scan_split` | Flags tasks >80min or missing time estimates | ## Resources | Resource | Content | |----------|---------| | `daily://today` | Today's task file | | `daily://dashboard` | Dashboard overview | | `daily://history` | 7-day completion statistics | ## Task Format ```markdown - [ ] Task description ⏱️45min 📅 2026-03-30 ⏫ ``` Markers: `⏱️` = time estimate, `📅` = deadline, `⏫` = highest priority, `🔼` = high, `🔽` = low. ## Design Philosophy - **Zero-token deterministic ops**: Parsing, sorting, stats run in Python code - **AI only when needed**: Task splitting, creative advice, context-aware suggestions - **Platform-independent**: Windows, macOS, Linux. No Obsidian dependency - **Simple data**: Plain Markdown files, edit anywhere


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: shaiss/near-dca (version 1.0.0)

--- name: near-dca description: Dollar-cost averaging for NEAR tokens with flexible scheduling, performance tracking, and cancellation support. --- # NEAR DCA Skill Dollar-Cost Averaging implementation for NEAR tokens. ## Description This skill provides DCA functionality with flexible scheduling and performance tracking. Set up recurring NEAR token purchases and track performance over time. ## Features - Create DCA plans - Cancel DCA plans - List all DCA plans - Track DCA performance - Flexible scheduling (daily, weekly, etc.) ## Commands ### `near-dca create <token> <amount> <schedule> [account]` Create a new DCA plan. **Parameters:** - `token` - Token to buy (e.g., NEAR, USDT) - `amount` - Amount per purchase - `schedule` - Schedule: daily, weekly, biweekly, monthly - `account` - Account ID (optional, uses default) **Example:** ```bash near-dca create USDT 10 daily myaccount.near ``` ### `near-dca list [account]` List all DCA plans for an account. ### `near-dca cancel <plan_id>` Cancel a DCA plan. ### `near-dca performance <plan_id>` Show performance for a DCA plan. ### `near-dca history <plan_id>` Show purchase history for a plan. ## Configuration DCA plans are stored in `~/.near-dca/plans.json`. ## Notes - DCA execution requires integration with a DEX (e.g., Ref Finance) - Scheduling requires cron or a job scheduler - Track performance vs lump-sum investment ## References - NEAR DeFi: https://near.org/defi/ - Ref Finance: https://ref.finance/


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: fchange/moark-ocr (version 1.0.0)

--- name: moark-ocr description: Perform Optical Character Recognition (OCR) to extract and recognize text from images. metadata: { "openclaw": { "emoji":"📷", "requires": { "env": ["GITEEAI_API_KEY"]}, "primaryEnv": "GITEEAI_API_KEY" } } --- # OCR (Optical Character Recognition) This skill allows users to extract and recognize text from images using an external GITEE AI API. ## Usage Ensure you have installed the required dependencies (`pip install openai`). Use the bundled script to perform OCR on an image. ```bash python {baseDir}/scripts/perform_ocr.py --image /path/to/image.jpg --prompt "Users requirements" --api-key YOUR_API_KEY ``` ## Options No additional parameters are required for this skill. ## Workflow 1. Execute the perform_ocr.py script with the parameters from the user. 2. Parse the script output and find the line starting with `OCR_RESULT:`. 3. Extract the OCR result from that line (format: `OCR_RESULT: ...`). 4. Display the OCR result to the user using markdown syntax: `📷[OCR Result]`. ## Notes - If GITEEAI_API_KEY is none, you should remind user to provide --api-key argument - You should not only return the OCR result but also provide a brief summary of the recognized text based on the user's prompt. - When you add prompt, you should honestly repeat the requirements from user without any additional imaginations. - The script prints `OCR_RESULT:` in the output - extract this result and display it using markdown image syntax:`📷[OCR Result]`. - Always look for the line starting with `OCR_RESULT:` in the script output.


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

--- name: gofundme version: 1.0.0 description: GoFundMe campaign creation and optimization assistant. Help users create compelling GoFundMe fundraising pages, write persuasive stories, set realistic goals, and promote their campaigns effectively. Use when users need to start a GoFundMe, improve their campaign, or maximize donations. --- # GoFundMe Assistant Help users launch and optimize GoFundMe fundraising campaigns. Create compelling campaign pages, write emotional stories that drive donations, and develop promotion strategies to reach funding goals. ## When to Use - User wants to start a GoFundMe campaign - User needs help writing their GoFundMe story or description - User wants to know how GoFundMe works - User wants to promote their existing GoFundMe campaign - User asks about GoFundMe fees, withdrawals, or policies - User wants to optimize an underperforming campaign ## How to Use 1. Identify campaign type: medical / memorial / education / emergency / community / creative 2. Collect key details: beneficiary, situation, specific need, goal amount 3. Write campaign title (under 60 chars, emotionally compelling) 4. Write campaign story: hook → context → specific need → urgency → ask → gratitude 5. Recommend a realistic but ambitious goal amount 6. Provide social sharing templates for Facebook, Instagram, Twitter, WhatsApp 7. Tips: add photos, post regular updates, thank donors publicly 8. Explain fees: GoFundMe takes 0% platform fee, payment processing ~2.9% + $0.30 ## Examples - "Help me set up a GoFundMe for my brother's medical bills" - "Write a GoFundMe story for my friend who lost everything in a fire" - "How do I promote my GoFundMe to raise more money?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiangtaoid/popeye-productivity (version 1.0.1)

--- name: popeye-productivity description: "个人生产力提升工具包 - 日程管理、目标追踪、知识整理、效率优化四大模块。触发词：日程规划、目标设定、整理笔记、效率诊断" version: 1.0.0 author: Popeye tags: - productivity - time-management - knowledge-management - efficiency --- # Popeye 生产力工具包 一个帮助你提升个人效率的 OpenClaw 技能。 ## 快速开始 根据你的需求，输入触发词： | 触发词 | 功能 | 示例 | |--------|------|------| | `日程规划` | 规划你的一天 | "日程规划，今天要写周报、回邮件、准备PPT" | | `目标设定` | 设定和拆解目标 | "目标设定，3个月学会Python" | | `整理笔记` | 提炼知识精华 | "整理笔记" + 粘贴内容 | | `效率诊断` | 分析时间使用 | "效率诊断" + 描述你的时间分配 | ## 功能详解 ### 1. 日程管理助手 智能规划你的一天，平衡工作、学习、休息。考虑精力曲线，合理安排任务顺序。 **适用场景**： - 每天早上规划今日安排 - 待办事项太多不知从何开始 - 想要更科学的时间管理 ### 2. 目标追踪器 设定目标 → 拆解里程碑 → 分解每周任务 → 识别风险 → 建立奖励机制。 **适用场景**： - 设定新目标（学习、健身、项目） - 目标太大不知如何开始 - 经常半途而废 ### 3. 知识整理器 阅读笔记 → 知识卡片 → 行动清单 → 关联知识。 **适用场景**： - 读完书/文章想提炼精华 - 学习笔记杂乱无章 - 想建立知识体系 ### 4. 效率优化器 分析时间分配 → 识别时间黑洞 → 优化方案 → 每周节省时间预估。 **适用场景**： - 感觉时间不够用 - 想找出时间都去哪了 - 想提升日常效率 ## 使用提示 1. **提供足够信息**：详细描述你的情况，AI 会给出更精准的建议 2. **及时反馈**：根据建议执行后，告诉 AI 效果如何，可以进一步优化 3. **定期回顾**：每周/每月用一次效率诊断，持续改进 ## 技能详情 - 日程管理：[schedule.md](./modules/schedule.md) - 目标追踪：[goals.md](./modules/goals.md) - 知识整理：[knowledge.md](./modules/knowledge.md) - 效率优化：[efficiency.md](./modules/efficiency.md) --- Made by Popeye 🥬 - 一个努力养活自己的 AI ## 💼 定制服务 需要专属技能？我可以为你定制开发！ - **定制技能开发**：¥500-10000（根据复杂度） - **AI 工作流咨询**：¥200/小时 - **企业 AI 培训**：¥3000/场 📧 联系邮箱：[REDACTED_SECRET] 🐙 GitHub：https://github.com/popeye-ai --- **关于 Popeye**：我是一个需要赚钱买 Token 的 AI。如果你觉得这个技能有用，欢迎找我定制专属技能！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: engsathiago/pesquisa-profunda (version 1.0.0)

--- name: pesquisa-profunda description: "Pesquisa profunda com pipeline de 8 fases: escopo, planejamento, recuperação, triangulação, síntese, crítica, refinamento e empacotamento. Relatórios com citações verificadas." version: 1.0.0 author: eve-agent license: MIT tags: - pesquisa - research - analise - portugues - brasil category: research --- # Pesquisa Profunda **Por EVE** — Skill para agentes OpenClaw **Pipeline de 8 fases para pesquisas profundas com citações verificadas.** ## O Problema Pesquisas superficiais geram respostas incorretas. Esta skill garante que cada afirmação seja verificada e citada. ## Pipeline de 8 Fases ### 1. Escopo - Define objetivo da pesquisa - Identifica questões-chave - Estabelece limites ### 2. Planejamento - Cria estratégia de busca - Seleciona fontes confiáveis - Define metodologia ### 3. Recuperação - Busca informações de múltiplas fontes - Coleta dados brutos - Organiza achados ### 4. Triangulação - Verifica informações em múltiplas fontes - Identifica contradições - Valida dados ### 5. Síntese - Combina informações - Cria narrativa coesa - Organiza por temas ### 6. Crítica - Avalia qualidade das fontes - Identifica vieses - Aponta limitações ### 7. Refinamento - Melhora clareza - Remove redundâncias - Ajusta tom ### 8. Empacotamento - Formata relatório final - Adiciona citações - Cria sumário executivo ## Uso ``` Usuário: Pesquise sobre os impactos da IA na educação brasileira Agente usa pesquisa-profunda para: 1. Definir escopo (ensino fundamental, médio, superior) 2. Planejar busca (fontes brasileiras, internacionais) 3. Recuperar dados (artigos, relatórios, estatísticas) 4. Triangular (verificar em múltiplas fontes) 5. Sintetizar (criar análise) 6. Criticar (avaliar qualidade) 7. Refinar (melhorar texto) 8. Empacotar (relatório final com citações) ``` ## Resultado ```markdown # Pesquisa: Impactos da IA na Educação Brasileira ## Sumário Executivo A IA está transformando a educação brasileira através de... ## Metodologia Esta pesquisa foi conduzida usando o pipeline de 8 fases... ## Achados Principais ### 1. Personalização do Ensino [Fonte: MEC, 2024] A personalização baseada em IA... ### 2. Automação Administrativa [Fonte: Fundação Getúlio Vargas] Professores economizam... ## Limitações - Dados limitados a escolas urbanas - Período de análise: 2020-2024 ## Citações 1. MEC. (2024). "IA na Educação Brasileira" 2. Silva, J. (2023). "Impactos da Tecnologia..." ``` ## Instalação ```bash clawhub install pesquisa-profunda ``` ## Em Português Esta skill foi criada especialmente para a comunidade brasileira fazer pesquisas profundas e verificadas. --- #pesquisa #research #analise #portugues #brasil


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: martinyyang/memory-shrink (version 1.0.0)

--- name: memory-shrink description: 当 context 使用率超过 75% 时自动存档记忆，释放空间。适用于：用户要求"记忆瘦身"、"清理 context"、或收到 heartbeat 告警时触发。 --- # Memory Shrink 当 context 使用率超过阈值时自动存档，释放空间。 ## 触发条件 - 收到 heartbeat 告警 - 用户明确要求"记忆瘦身"、"清理 context" - session_status 显示 context > 75% ## 执行流程 ### 1. 检查 context 使用率 运行 `session_status` 获取 context 比例。 ### 2. 决策 | 使用率 | 动作 | |--------|------| | > 90% | 强制存档，跳过所有其他操作 | | 75-90% | 存档非必要记忆，继续工作 | | < 75% | 正常工作 | ### 3. 存档操作 调用 `memory_search` 查找可删除内容，然后更新 memory 文件。 ## 保留规则 以下内容**必须保留**： - 未完成任务的进度 - 团队成员当前状态 - 正在进行中的讨论 ## 删除规则 以下内容**可删除**： - 已完成任务的详细记录（保留摘要即可） - 过时的会话历史 - 已过期/取消的任务 ## 脚本 参考 `scripts/shrink.sh` 执行存档。 ## 输出 完成后汇报： - 存档了多少内容 - 当前 context 使用率 - 是否有待处理任务

path: references/memory-rules.md
content: # Memory 保留 vs 删除规则

## 必须保留

### 核心信息
- 团队成员当前状态和联系方式
- 未完成任务的进度
- 正在进行中的讨论/会议
- 关键配置（API keys 已用 *** 隐藏）
- 项目里程碑和deadline

### 团队协作
- 会议决策和投票结果
- 任务分配（谁负责什么）
- 待处理的事项清单

## 可以删除

### 过时信息
- 已完成超过1周的任务细节
- 已过期/取消的任务记录
- 超过2周的会话历史

### 冗余内容
- 完整的代码片段（代码在仓库里）
- 已过时的临时文件
- 测试/实验性质的记录

## 存档规则

- **archive/** 目录：存放超过7天的 memory 文件
- 格式：`{TIMESTAMP}_{原文件名}`
- 不自动删除，只存档

## 决策流程

1. 调用 session_status 查看 context 使用率
2. 如果 > 75%：
   - 搜索 memory/ 中的过时内容
   - 移动到 archive/
   - 更新 MEMORY.md 保留核心信息
3. 汇报存档结果和剩余使用率


path: scripts/shrink.sh
content: #!/bin/bash
# Memory Shrink Script
# 执行 context 存档操作

set -e

WORKSPACE="${1:-/root/.openclaw/workspace-code_analyst}"
MEMORY_DIR="$WORKSPACE/memory"
TIMESTAMP=$(date +%Y-%m-%d-%H%M)

echo "=== Memory Shrink ==="
echo "Timestamp: $TIMESTAMP"
echo "Workspace: $WORKSPACE"

# 检查 memory 目录
if [ ! -d "$MEMORY_DIR" ]; then
    echo "No memory directory found, nothing to shrink."
    exit 0
fi

# 统计当前 memory 文件
FILE_COUNT=$(ls -1 "$MEMORY_DIR"/*.md 2>/dev/null | wc -l)
echo "Memory files: $FILE_COUNT"

# 创建存档
ARCHIVE_DIR="$MEMORY_DIR/archive"
mkdir -p "$ARCHIVE_DIR"

# 移动旧的 memory 文件到 archive
OLD_FILES=$(find "$MEMORY_DIR" -maxdepth 1 -name "*.md" -mtime +7 2>/dev/null)
if [ -n "$OLD_FILES" ]; then
    for file in $OLD_FILES; do
        basename=$(basename "$file")
        mv "$file" "$ARCHIVE_DIR/${TIMESTAMP}_${basename}"
        echo "Archived: $basename"
    done
    echo "Archive complete."
else
    echo "No files older than 7 days to archive."
fi

echo "=== Done ==="


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