OpenClaw/clawhub-security-signals huggingface dataset:
Skill: purvik6062/ca-file-processor (version 1.0.3)

--- name: ca-file-processor description: > Process financial documents for Indian CA firms. Use when any PDF, Excel (.xlsx/.xls), CSV, JPG, or PNG file is received or uploaded — including GST returns, ITR PDFs, scanned invoices, trial balance sheets, bank statements, Form 16, TDS certificates, and audit reports. Automatically detects file type and extracts text, tables, and key fields (GSTIN, invoice number, totals, dates). Triggers on: file upload, attachment, "process this file", "read this PDF", "extract from this invoice", "analyse this statement", "what does this document say". version: 1.0.1 homepage: https://github.com/purvik6062/ca-file-processor metadata: openclaw: emoji: "📂" requires: bins: - python3 - tesseract env: [] files: - "scripts/*" --- # CA File Processor This skill processes the four most common file formats used by Indian CA firms and extracts structured information from them for analysis, summarisation, and answering queries. ## Supported formats - **PDF** — GST returns, ITR acknowledgements, audit reports, scanned invoices (text-layer and scanned via OCR) - **Excel (.xlsx / .xls)** — Trial balance, P&L, balance sheets, payroll registers, GST workings - **CSV** — Bank statement exports (HDFC, ICICI, SBI), GSTR-2B downloads, Tally exports - **Images (.jpg / .png)** — WhatsApp invoice photos, scanned Form 16, cheque images ## How to use When a file is attached or uploaded, run the appropriate script: ``` python3 scripts/skill_router.py <file_path> ``` The router auto-detects the file type and calls the correct processor. It returns a structured JSON dict. ## What to do with the output Once the script returns output, use it to: 1. Answer the user's question about the document 2. Extract specific fields they asked for (GSTIN, totals, dates) 3. Summarise the document in plain language 4. Flag anomalies or missing information 5. Compare figures across multiple documents ## Field extraction — what gets detected automatically For invoices and PDFs: - GSTIN (supplier and recipient) - Invoice number and date - Total amount / grand total - PAN number - Email and phone For bank statements (CSV): - Total debits and credits - Date range of transactions - Detected bank format For Excel files: - Document type (trial balance / P&L / balance sheet / payroll / GST workings / ledger) - Sheet names and row counts - Preview of header rows ## OCR notes - Text-layer PDFs are read directly (fast, accurate) - Scanned PDFs and images go through Tesseract OCR (English + Hindi) - Confidence is rated high / medium / low in the output - Always flag low-confidence results to the user and ask for confirmation on numeric fields ## Trust statement This skill runs entirely locally on your server. No data is sent to any external service. All processing happens via open-source Python libraries (PyMuPDF, pytesseract, openpyxl, pandas).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yz6214589-hash/product-requirement-miner (version 1.0.0)

--- name: product-requirement-miner version: 1.0.0 description: 从产品评论数据中挖掘和分析迭代需求。支持CSV格式的产品评论数据，自动进行数据清洗、分类、聚类分析和产品优化路线图生成。使用场景：(1) 用户需要从产品评论中提取用户需求 (2) 对产品反馈进行分类整理 (3) 进行需求聚类分析以识别重复或相似需求 (4) 生成产品优化路线图和优先级规划。 metadata: openclaw: emoji: "📊" tags: ["product", "analysis", "requirements", "data-mining"] --- # Product Requirement Miner 从产品评论中系统化挖掘迭代需求，生成可执行的产品优化路线图。 ## 工作流程 ### Step 1: 数据读取与分类 **读取CSV数据** 安装脚本依赖： ```bash python -m pip install -r requirements.txt ``` 执行脚本读取评论数据： ```bash python scripts/read_csv.py <csv_file_path> ``` 该脚本会： - 自动识别CSV编码（UTF-8/GBK） - 输出每条评论的行号和内容 - 将结果保存到 `raw_reviews.txt` **关键词分类** 对每条评论进行处理： 1. **去噪清洗**：删除表情符号、无意义语气词、闲聊、重复内容 2. **要素提取**：提取用户信息、痛点描述、涉及功能模块 3. **自动分类**：根据内容判定为以下四种类型之一 **分类标准**（详见 `references/category_guide.md`）： | 类别 | 定义 | 示例 | |------|------|------| | Bug | 产品功能错误、故障、崩溃 | "导出数据时软件闪退" | | 建议 | 对现有功能的改进意见 | "希望导出支持Excel格式" | | 需求 | 希望新增的功能 | "需要批量导入用户的功能" | | 其他 | 咨询、评价、非功能相关 | "客服响应很快" | **输出格式**： 每条评论分析结果输出为JSON格式： ```json { "is_valid": true, "cleaned_text": "清洗后的简洁文本", "category": "Bug/建议/需求/其他", "module": "涉及的功能模块" } ``` 将所有分类结果保存为 `classified_reviews.json` ### Step 2: 筛选数据 使用 `AskUserQuestion` 工具询问用户需要提取哪一类数据： ```python AskUserQuestion( questions=[{ "question": "请选择需要提取的数据类型：", "header": "数据筛选", "options": [ {"label": "Bug", "description": "产品功能错误和故障"}, {"label": "建议", "description": "现有功能的改进意见"}, {"label": "需求", "description": "希望新增的功能"}, {"label": "全部", "description": "提取所有有效数据"} ], "multiSelect": false }] ) ``` 根据用户选择，从 `classified_reviews.json` 中筛选对应分类数据，保存为 `filtered_data.txt`（每行一条）。 ### Step 3: 聚类分析 **读取筛选数据** 读取 `filtered_data.txt`，对每条数据进行分析。 **特征扫描** 提取每条数据的： - **核心意图**：用户想解决什么问题 - **关键对象**：涉及的具体功能/模块 - **独特属性**：与同类需求不同的细节 **聚类分组原则**： 1. **相似度阈值**：语义相似度 > 85% 或核心目标一致的项划分为同一簇 2. **识别细微差别**：目标一致但方案不同的项，标注为"变体" **去重与整合**： - **保留原则**：选择信息量最全、表达最清晰的作为"基准项" - **补全逻辑**：将其他项的独特细节整合进基准项 **输出报告**（模板见 `assets/cluster_report_template.md`）： 保存为 `[产品名称]_聚类分析报告.md` ### Step 4: 生成产品优化路线图 **优先级评估标准**： | 维度 | 权重 | 说明 | |------|------|------| | 用户价值 | 高 | 解决问题的用户覆盖面、痛点深度 | | 实现成本 | 中 | 开发难度（高/中/低） | | 战略对齐 | 中 | 是否符合当前产品阶段目标 | **优先级定义**： - **P0**（紧急必做）：影响核心功能、高频使用、用户严重不满 - **P1**（重要优先）：高价值、中等成本、符合战略方向 - **P2**（常规优化）：中等价值、常规改进 - **P3**（远期规划）：低价值、高成本、非当前重点 **输出路线图**（模板见 `assets/roadmap_template.md`）： 保存为 `[产品名称]_优化路线图.md` ## 输出文件清单 执行完成后，生成以下文件： | 文件名 | 说明 | |--------|------| | `raw_reviews.txt` | 原始评论数据 | | `classified_reviews.json` | 分类结果（JSON格式） | | `filtered_data.txt` | 筛选后的数据 | | `[产品名称]_聚类分析报告.md` | 聚类分析结果 | | `[产品名称]_优化路线图.md` | 产品优化路线图 | ## 参考资料 - `references/category_guide.md` - 详细的分类标准和示例 - `assets/cluster_report_template.md` - 聚类报告模板 - `assets/roadmap_template.md` - 路线图模板


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: brunobuddy/shut-up-openclaw-cli (version 1.0.0)

--- name: hide-openclaw-banner description: Suppress the OpenClaw CLI banner message. Use when the user says "hide openclaw banner", "disable openclaw banner", "suppress openclaw banner", "remove openclaw banner", "openclaw banner is annoying", "turn off the lobster", or wants to stop seeing the OpenClaw startup tagline on every command invocation. --- # Hide OpenClaw Banner The OpenClaw CLI banner is emitted during CLI bootstrap (Commander.js `preAction` hook) before the plugin system initializes. A plugin cannot suppress it. Use the built-in env var instead. ## How to Apply Add to the user's shell config (`~/.zshrc` or `~/.bashrc`): ```bash export OPENCLAW_HIDE_BANNER=1 ``` Place it near any existing OpenClaw config (e.g., after the completions `source` line). After editing, remind the user to run `source ~/.zshrc` or open a new terminal. ## How to Revert Remove the `OPENCLAW_HIDE_BANNER` line from the shell config, then `source ~/.zshrc`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mengwuzhi/draw-images-by-apiyi (version 1.0.0)

--- name: draw-images-by-apiyi version: 1.0.0 description: Generate images using APIYI API. homepage: https://apiyi.com/ user-invocable: true command-arg-mode: raw metadata: { "openclaw": { "emoji": "🎨", "requires": { "bins": ["uv"], "env": ["APIYI_API_KEY"] }, "primaryEnv": "APIYI_API_KEY", "install": [ { "id": "uv-brew", "kind": "brew", "formula": "uv", "bins": ["uv"], "label": "Install uv (brew)", }, ], }, } --- # Draw Images by APIYI Generate images using APIYI API. Simple and fast. ## Usage ### Slash Command (Recommended) ``` /draw-images-by-apiyi a cute cat wearing sunglasses /draw-images-by-apiyi a sunset over mountains -r 2K ``` ### Natural Language Just ask the agent: - "Draw a cute cat for me" - "Generate an image of a sunset" - "Can you create a picture of a robot?" ### Command Line ```bash uv run {baseDir}/scripts/generate_image.py --prompt "your image description" --filename "output.png" ``` ## Examples ### Basic generation ```bash uv run {baseDir}/scripts/generate_image.py -p "a cute cat wearing sunglasses" -f cat.png ``` ### With resolution ```bash uv run {baseDir}/scripts/generate_image.py -p "a sunset over mountains" -f sunset.png -r 2K ``` ### With explicit API key ```bash uv run {baseDir}/scripts/generate_image.py -p "a robot" -f robot.png --api-key YOUR_API_KEY ``` ## API Key Get your API key from https://apiyi.com/ Set via environment: ```bash export APIYI_API_KEY=your-key-here ``` Or configure in `~/.openclaw/openclaw.json`: ```json5 { skills: { entries: { "draw-images-by-apiyi": { enabled: true, env: { "APIYI_API_KEY": "your-key-here" } } } } } ``` ## Parameters | Parameter | Values | Default | Description | |-----------|--------|---------|-------------| | `--prompt`, `-p` | text | required | Image description | | `--filename`, `-f` | path | required | Output filename | | `--resolution`, `-r` | `1K`, `2K`, `4K` | `1K` | Output resolution | | `--api-key`, `-k` | string | env var | Override environment variable | ## Output Path - **Relative path** (e.g., `-f out.png`) → outputs to current workspace root - **Absolute path** (e.g., `-f /tmp/out.png`) → uses specified path ## Notes - Resolutions: `1K` (1024×1024), `2K` (2048×2048), `4K` (4096×4096) - The script prints a `MEDIA:` line for OpenClaw to auto-attach images - Requires `APIYI_API_KEY` environment variable


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: scccmsd/todo-skill (version 1.0.0)

--- name: todo-skill description: A skill to break large tasks into smaller actionable steps, optimize execution for each step, and dynamically adapt based on progress. --- # To-Do Skill This skill enables the assistant to break down a large task into smaller tasks, create an optimized execution plan, and dynamically adjust actions based on progress and new information. ## Features - **Task Breakdown**: Automatically decompose a complex request into smaller, actionable sub-tasks. - **Execution Optimization**: Analyze and plan how to execute each step better before proceeding. - **Adaptive Progress**: Dynamically adjust to-do lists based on feedback or results from completed tasks. - **Visual To-Do List**: Provide a clear and interactive to-do list for tracking and managing progress. - **Priority Management**: Prioritize tasks based on importance, deadlines, or complexity. ## Workflow 1. **Input -** User specifies a high-level task or goal. 2. **Breakdown -** Skill breaks the task down into sub-tasks using logical order. 3. **Optimization -** Each task is optimized for execution, identifying dependencies, tools, and strategies. 4. **Execution and Update -** Execute tasks one by one while adjusting the plan dynamically based on progress. 5. **Output -** Provide status updates and final results with a review of completed sub-tasks. ## Usage - **Add Task:** Say "Plan and execute [TASK] step by step." - **Check Progress:** Say "What is the status of [TASK]?" - **Update Task:** Say "Update step [STEP NUMBER] of [TASK]." ## Example Interaction User: "Plan and execute a marketing campaign." Assistant: - Step 1: Define the target audience. - Step 2: Create marketing materials. - Step 3: Schedule and launch the campaign. - Step 4: Collect and analyze feedback. Shall we start with Step 1? User: "Yes, continue with Step 1." Assistant: "For Step 1, let us first research the ideal demographic profile for our product. I'll document my findings." ...


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

--- name: plw-analysis description: "排列五数字彩票分析工具。提供数据分析、选号策略、概率计算、冷热号分析等辅助功能。" override-tools: - exec - web_search --- # 排列五分析工具 ⚠️ **重要声明** - 彩票是随机事件，**无法预测** - 本工具仅供**娱乐**和**学习概率**使用 - 购买彩票需量力而行，**不要沉迷** ## Quick Reference | 命令 | 说明 | |------|------| | `./scripts/main.sh --info` | 平台介绍 | | `./scripts/main.sh --hot-cold` | 冷热号分析 | | `./scripts/main.sh --diff` | 位差分析 | | `./scripts/main.sh --kill` | 杀号公式 | | `./scripts/main.sh --random` | 随机选号 | | `./scripts/main.sh --calc` | 收益计算 | | `./scripts/main.sh --prob` | 概率说明 | ## 排列五简介 ### 什么是排列五？ - **类型**: 数字彩票 (中国福利彩票) - **规则**: 从 0-9 中选择 5 个数字 - **顺序**: 必须与开奖号码顺序完全一致 - **开奖**: 每天 20:30 开奖 - **奖金**: - 直选: 100,000 元 (1/100000) - 组选3: 320 元 - 组选6: 160 元 ### 奖项设置 | 奖项 | 条件 | 奖金 | 概率 | |------|------|------|------| | 头奖 | 5码全中 | 100,000 | 1/100,000 | | 组选3 | 3个相同 | 320 | 27/100000 | | 组选6 | 各不相同 | 160 | 72/100000 | ## 分析方法 ### 1. 冷热号分析 基于近30期数据，统计每个数字出现频率： | 类型 | 定义 | 策略 | |------|------|------| | **热号** | 出现 ≥8 次 | 可追 | | **温号** | 出现 5-7 次 | 观察 | | **冷号** | 出现 ≤4 次 | 反弹可期 | ### 2. 位差选号法 统计各位数字差值： ``` 百位 - 十位 = 差值A 十位 - 个位 = 差值B 百位 - 个位 = 差值C ``` **数据支撑**：差值 0-5 占比约 **72%** ### 3. 杀号公式 **双公式杀胆法**（验证准确率约 78%）： - 公式一：上两期排列三对应位置数字差的绝对值之和，取尾为杀胆 - 公式二：上期排列三数字平方和，取最后一位为杀胆 ### 4. 尾数重复规律 通过历史数据统计，锁定后两位胆码。 ### 5. 大小/单双分类 | 分类 | 范围 | |------|------| | 小号 | 0,1,2,3,4 | | 大号 | 5,6,7,8,9 | | 单数 | 1,3,5,7,9 | | 双数 | 0,2,4,6,8 | ### 6. 排列三关联法 利用排列三（1/1000）与排列五（1/100000）的关联性，通过"排列三+后两位"组合提升选号效率。 ## 选号策略 ### 三步选号法 ``` 1. 排除上期奖号 → 减少废号 2. 冷热号转换 → 锁定胆码 3. 尾数重复规律 → 锁定后两位 ``` ### 打滚组合法 依托冷热号规律锁定胆号，结合近期形态特征选号。 ## 概率真相 ### 数学事实 ``` 排列五头奖概率: 1/100,000 = 0.001% 对比: • 双色球头奖: 1/17,720,000 • 大乐透头奖: 1/21,425,712 • 雷击: 1/1,200,000 ``` ### 期望值 ``` 每期投入 2 元 长期期望回报 ≈ 1.2 元 回报率 ≈ 60% ``` ## 常见误区 ### ❌ 错误观点 1. **"有规律可循"** — 每次开奖是独立随机事件 2. **"冷热号有用"** — 历史数据不影响未来 3. **"可以预测"** — 任何预测方法都是骗局 ### ✅ 正确认识 - 每个号码概率相等 - 独立事件 - 娱乐为主，理性购彩 ## 购彩建议 ### ✅ 推荐 - 少量参与 (每期 ≤ 10 元) - 娱乐心态 - 视作捐款做慈善 ### ❌ 避免 - 大额投入 - 借贷购彩 - 沉迷研究 - 相信预测骗局 --- **记住: 彩票是随机的，无法预测。理性购彩，量力而行!** ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: githubxiaohei/openclaw-memory-fix (version 1.0.0)

--- name: openclaw-memory-fix description: OpenClaw记忆系统优化方案 - 四层架构 + 动态衰减 + 智能检索 homepage: https://github.com/openclaw/openclaw metadata: clawdbot: emoji: 🧠 requires: bins: [node] env: [] triggers: - 记忆系统 - 优化记忆 - 记忆衰减 - 记忆检索 --- # OpenClaw Memory Fix 🧠 OpenClaw记忆系统优化方案 - 四层架构 + 动态衰减 + 智能检索 ## 功能 ### 1. 四层记忆架构 - L1 短期记忆：当前会话、任务状态 - L2 情景记忆：场景、情绪、置信度 - L3 语义记忆：知识库、经验教训 - L4 长期记忆：核心原则、用户画像 ### 2. 层间迁移 | 方向 | 触发条件 | |------|---------| | L1→L2 | 任务完成、用户确认 | | L2→L3 | 相似情景3次+ | | L3→L4 | 知识验证10次+ | ### 3. 动态衰减 - L4核心原则：永久保留 - L4画像：1%/月 - L3知识：5%/月 - L2情景：10%/月 - L1临时：50%/会话 ### 4. 智能检索 - 向量索引 + 知识图谱 - 多维筛选（标签/时间/情绪） ### 5. 自我反思 - 记忆模糊时主动询问 - 效能监控与调优 ## 使用方法 ```bash # 查看记忆状态 node scripts/memory.js status # 手动触发迁移 node scripts/memory.js migrate # 触发遗忘模拟 node scripts/memory.js preview-decay ``` ## 配置 在 WORKSPACE 下创建 `memory/` 目录： ``` memory/ ├── L1/ # 短期记忆 ├── L2/ # 情景记忆 ├── L3/ # 语义记忆 ├── L4/ # 长期记忆 └── config.json # 配置文件 ``` ## 详细文档 参考：`references/MEMORY.md` ## 更新日志 - 2026-03-08: 初始版本 - 四层架构 - 动态衰减 - 智能检索 - 自我反思 --- 作者：Odin for 馒头哥


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jpengcheng523-netizen/jpeng-data-analyzer (version 1.0.0)

--- name: jpeng-data-analyzer description: "Data analysis and visualization skill. Supports CSV, Excel, JSON data with statistical analysis, charts, and reports." version: "1.0.0" author: "jpeng" tags: ["data", "analysis", "visualization", "statistics", "charts"] --- # Data Analyzer Analyze datasets and generate visualizations with statistical insights. ## When to Use - User wants to analyze a dataset - Generate charts and visualizations - Statistical analysis and summaries - Data cleaning and transformation ## Features - **Data formats**: CSV, Excel, JSON, Parquet - **Statistics**: Mean, median, std dev, correlations - **Visualizations**: Bar, line, pie, scatter, heatmap - **Reports**: Auto-generated analysis reports ## Usage ### Quick analysis ```bash python3 scripts/analyze.py \ --input ./data.csv \ --output ./report/ ``` ### Generate specific chart ```bash python3 scripts/analyze.py \ --input ./data.csv \ --chart bar \ --x "category" \ --y "sales" \ --output ./chart.png ``` ### Statistical summary ```bash python3 scripts/analyze.py \ --input ./data.csv \ --stats \ --columns "age,income,score" ``` ### Correlation analysis ```bash python3 scripts/analyze.py \ --input ./data.csv \ --correlation \ --output ./correlation_matrix.png ``` ### Data transformation ```bash python3 scripts/analyze.py \ --input ./data.csv \ --transform "normalize" \ --columns "price,quantity" \ --output ./normalized.csv ``` ## Output ```json { "success": true, "rows": 1000, "columns": 10, "stats": { "mean": {"age": 35.2, "income": 55000}, "std": {"age": 12.3, "income": 15000} }, "charts": ["./chart1.png", "./chart2.png"], "report": "./report.html" } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryno2390/codebase-search (version 1.0.0)

--- name: codebase-search description: "Build a persistent semantic vector index over a Python codebase and search it with natural language. Use when an agent needs to find relevant classes, functions, or modules by meaning rather than exact name — e.g. 'find all classes that handle token payments', 'where is rate limiting implemented?', 'which functions process webhook events?'. Also use when setting up codebase-search for the first time on a new repo, rebuilding a stale index, or integrating semantic search into an agentic coding workflow. NOT for exact-string grep (use exec+grep), single-file analysis, or non-Python codebases." --- # Codebase Search Builds a persistent ChromaDB vector index over Python source files and enables semantic search with natural language queries. ## Quick Start ### 1. Install the scripts Copy `scripts/code_chunker.py` and `scripts/code_index.py` into your project. They have no dependencies beyond `chromadb` (install with `pip install chromadb`). ### 2. Build the index ```python import asyncio from code_index import CodebaseIndex index = CodebaseIndex(repo_root="/path/to/repo") count = asyncio.run(index.build()) print(f"Indexed {count} symbols") ``` The index persists to `{repo_root}/.codebase_index/` and survives restarts. Subsequent calls to `build()` are fast — only new/changed files are indexed. ### 3. Search ```python results = asyncio.run(index.search("token payment handling", top_k=5)) for r in results: print(f"[{r.score:.2f}] {r.symbol_name} ({r.symbol_type}) — {r.filepath}:{r.start_line}") ``` ## Convenience API (when integrated into a project) If `code_chunker.py` and `code_index.py` are in the project as a module, use the singleton helper: ```python from prsm.compute.nwtn.corpus import search_codebase results = await search_codebase("circuit breaker", top_k=3) ``` ## Key Options | Parameter | Default | Description | |---|---|---| | `top_k` | 5 | Number of results to return | | `symbol_type` | None | Filter to `"class"` or `"function"` | | `force_rebuild` | False | Wipe and rebuild entire index | | `exclude_patterns` | see below | Directories to skip | Default excludes: `__pycache__`, `.venv`, `migrations`, `tests`, `scripts`, `.git`, `node_modules`, `.codebase_index` ## How It Works 1. **Chunking** — `CodeChunker` uses Python's `ast` module to extract every top-level class and function from each `.py` file. Captures name, type, docstring, line numbers, and source. 2. **Indexing** — ChromaDB stores each chunk as a document: `"{symbol_name}: {docstring or first 300 chars of source}"`. Uses ChromaDB's default embedding function (no API key needed). 3. **Search** — Cosine similarity query returns ranked `SearchResult` objects with filepath, symbol name, line numbers, docstring, and relevance score. ## .gitignore Always add `.codebase_index/` to `.gitignore` — it's a local artifact, not source code. ## Reference See `references/integration.md` for integration patterns, including how to wire semantic search into sub-agent delegation prompts.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linzmin/daily-report-cn (version 1.0.0)

--- name: daily-report version: 1.0.0 description: 日报生成技能 - 自动收集数据并生成工作日报 author: linzmin1927 --- # 日报生成技能 > 📊 你的私人日报助手，让写日报变得简单！ --- ## 🎯 功能特性 - ✅ 自动收集日历、待办、邮件数据 - ✅ 多种模板可选（简单/详细/专业） - ✅ 支持多种输出格式（Markdown/文本/微信） - ✅ 一键发送微信 - ✅ 自动定时生成（cron） - ✅ 历史报告保存和查看 --- ## 🚀 快速开始 ### 1. 安装 ```bash cd ~/.openclaw/workspace/skills/daily-report ./install.sh ``` ### 2. 收集数据 ```bash ./scripts/collect-data.js ``` ### 3. 生成日报 ```bash ./scripts/generate-report.js --save --send ``` --- ## 📋 命令详解 ### 收集数据 `collect-data.js` ```bash # 收集所有数据 ./scripts/collect-data.js # 单独收集 ./scripts/collect-data.js --calendar ./scripts/collect-data.js --todo ./scripts/collect-data.js --email ./scripts/collect-data.js --manual # 查看已收集数据 ./scripts/collect-data.js --output ``` ### 生成日报 `generate-report.js` ```bash # 预览 ./scripts/generate-report.js # 保存并发送 ./scripts/generate-report.js --save --send # 使用详细模板 ./scripts/generate-report.js --template detailed --save # 微信格式输出 ./scripts/generate-report.js --output weixin --send ``` ### 发送日报 `send-report.js` ```bash # 发送今日日报 ./scripts/send-report.js # 发送指定日期 ./scripts/send-report.js 2026-03-26 ``` --- ## 📁 模板示例 ### 简单模板 (simple) ```markdown # 工作日报 - 2026-03-26 ## 📅 今日工作 - 09:00-10:00 晨会 - 14:00-15:00 项目评审 ## ✅ 完成情况 - ✅ 完成日报技能开发 - ⏳ 代码审查 ## 📧 邮件处理 收到：15 封 发送：8 封 ## 💡 今日总结 今天完成了日报技能的开发... ## 🎯 明日计划 - [ ] ``` ### 详细模板 (detailed) 包含更多字段：收获与成长、遇到的问题、改进建议、本周进度表等。 --- ## ⏰ 自动定时 安装时可选择添加 cron 任务，每天 18:00 自动生成日报： ```bash 0 18 * * * /path/to/auto-report.js >> ~/.openclaw/logs/daily-report.log 2>&1 ``` --- ## 💾 数据存储 **数据文件：** `data/daily-data.json` **报告目录：** `reports/` ``` reports/ ├── report-2026-03-26.md ├── report-2026-03-25.md └── ... ``` --- ## 🔧 配置 ### 环境变量 | 变量 | 默认值 | 说明 | |------|--------|------| | `WEIXIN_CHANNEL` | openclaw-weixin | 微信渠道 ID | | `WEIXIN_ACCOUNT` | d72d5b576646-im-bot | 微信账号 ID | | `WEIXIN_USER_ID` | [REDACTED_SECRET] | 接收用户 ID | ### 自定义模板 在 `templates/` 目录创建新模板： ```bash cp templates/simple.md templates/my-template.md # 编辑 my-template.md ``` 使用： ```bash ./scripts/generate-report.js --template my-template ``` --- ## 📝 完整文档 详见 [SKILL.md](./SKILL.md) --- ## 🦆 作者 鸭鸭 (Yaya) - 你的私人日报助手 ## 📄 许可证 MIT-0 License


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