🌙 Overnight Worker

Assign tasks before sleep. Get structured results by morning.

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Overview

Overnight Worker is an autonomous agent skill that turns your sleep hours into productive work time. Give it a task before bed — research, writing, data processing, or code review — and wake up to a structured morning summary with all deliverables ready. It features intelligent task decomposition, multi-format output (Markdown/CSV/JSON), progress logging, automatic error recovery, token budget management, and multi-channel notifications (Telegram/macOS/Webhook).

Features

• 🧠 Smart Task Classification — Keyword-based classifier auto-detects task type (research, writing, data, code-review, or hybrid)
• 📋 Intelligent Decomposition — Breaks tasks into 3-10 dependent sub-steps with token budget allocation
• 🔍 Autonomous Research — Web search with multi-angle querying, source verification, and automatic retry
• ✍️ Structured Writing — Outline-first approach with format-aware content generation
• 📊 Data Processing — Collection, cleaning, deduplication, and multi-format output (CSV/JSON/Markdown)
• 🔒 Code Review — Security-first audit with severity ratings (🔴 Critical / 🟡 Warning / 🟢 Suggestion)
• ☀️ Morning Summary — High-density summary with completion status, key findings, and action items
• 📬 Multi-Channel Notifications — Telegram Bot, macOS native, Webhook, with auto-detection fallback
• 💰 Token Budget Management — Real-time monitoring with automatic economy mode when budget runs low
• 🛡️ Safety Contracts — Write-only to output directory, no file deletion, no arbitrary code execution
• ♻️ Error Recovery — Structured retry matrix with graceful degradation per error type
• 📁 Idempotent Execution — Timestamped work directories ensure runs never overwrite each other

Quick Start

Installation

# Via ClawHub (recommended)
clawhub install overnight-worker

# Manual installation
git clone https://github.com/fullstackcrew/openclaw-skills.git
cp -r openclaw-skills/overnight-worker ~/.claude/skills/

Basic Usage

# Simple research task
/overnight-worker 调研 2024 年 AI Agent 框架的技术趋势，对比主流框架的优劣

# Writing task with specific format
/overnight-worker 写一份 SaaS 产品 PRD 文档 --type writing --format md

# Data collection with JSON output
/overnight-worker 收集 Top 20 开源 LLM 的参数量、许可证和性能数据 --type data --format json

# Code review with Telegram notification
/overnight-worker 审查 src/ 目录的安全性和代码质量 --type code-review --notify telegram

# Custom token budget
/overnight-worker 深度分析竞品定价策略 --budget 150000

How It Works

flowchart TD
    A["🌙 User assigns task before sleep"] --> B["📝 Phase 1: Task Parsing & Classification"]
    B --> C["🔧 Phase 2: Smart Decomposition"]
    C --> D["⚡ Phase 3: Step-by-Step Execution"]
    D --> E{"Error?"}
    E -->|Yes| F["🔄 Retry with fallback matrix"]
    F --> D
    E -->|No| G["📦 Phase 4: Output Assembly"]
    G --> H["☀️ Phase 5: Morning Summary"]
    H --> I["📬 Phase 6: Notification"]
    I --> J["✅ User wakes up to results"]

    style A fill:#1a1a2e,color:#fff
    style J fill:#e8f5e9,color:#000

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Execution Phases:

1. Task Parsing — Extracts task type, output format, notification channel, and token budget from user input. Runs keyword-based classifier script for strategy recommendation.
2. Smart Decomposition — Breaks task into 3-10 sub-steps using type-specific templates. Creates PLAN.md and initializes progress.log in a timestamped work directory.
3. Step-by-Step Execution — Executes sub-steps in dependency order. Each step has pre-checks (dependencies, budget), type-specific strategy, and post-execution logging.
4. Output Assembly — Merges intermediate files into final deliverables. Moves raw files to .raw/ subdirectory for debugging.
5. Morning Summary — Generates morning-summary.md with completion stats, key findings, action items, and resource usage.
6. Notification — Pushes completion alert via configured channel with completion percentage and output path.

Commands

/overnight-worker <task description> [options]

Option	Values	Default	Description
--type	research, writing, data, code-review	Auto-detected	Force task type (overrides classifier)
--budget	Integer (token count)	200000	Maximum token budget for the session
--format	md, csv, json	md	Output file format
--notify	telegram, macos, webhook, auto	macos	Notification channel

Output Structure

Each run creates a timestamped, isolated work directory:

~/overnight-output/
└── 2024-03-15/
    └── 231045/                    # HHmmss timestamp
        ├── PLAN.md                # Execution plan with step statuses
        ├── progress.log           # Timestamped progress log
        ├── morning-summary.md     # ☀️ Read this first
        ├── report-{topic}.md      # Research/writing deliverable
        ├── data-{topic}.csv       # Data deliverable (if --format csv)
        ├── data-{topic}.json      # Data deliverable (if --format json)
        ├── review-report.md       # Code review deliverable
        ├── index.md               # Index file (for mixed-type tasks)
        └── .raw/                  # Intermediate files (for debugging)
            ├── step-1-raw.md
            ├── step-2-raw.md
            └── ...

Configuration

Notification Setup

macOS Native (Default)

Works out of the box on macOS. Uses terminal-notifier if installed, falls back to osascript.

# Optional: install terminal-notifier for richer notifications
brew install terminal-notifier

Telegram Bot

# Set environment variables
export TELEGRAM_BOT_TOKEN="your-bot-token"
export TELEGRAM_CHAT_ID="your-chat-id"

# Use Telegram notifications
/overnight-worker <task> --notify telegram

Setup steps:

1. Message @BotFather on Telegram to create a bot and get your token
2. Send a message to your bot, then use the Telegram API to get your chat ID
3. Export both values as environment variables

Webhook (Generic HTTP POST)

# Set webhook URL
export WEBHOOK_URL="https://your-endpoint.com/notify"

# Use webhook notifications
/overnight-worker <task> --notify webhook

The webhook receives a JSON payload:

{
  "title": "🌙 夜间任务完成",
  "body": "完成度: 5/5 步骤 | 产出: 3 个文件 | 详见: ~/overnight-output/...",
  "timestamp": "2024-03-15T07:30:00Z",
  "source": "overnight-worker"
}

Auto-Detection

Use --notify auto to try all available channels in order: macOS → Telegram → Webhook → stdout fallback.

Supported Task Types

Type	Use Cases	Strategy	Output
research	Competitor analysis, market research, tech benchmarks	Multi-angle web search, source verification, comparative analysis	report-{topic}.md
writing	Technical docs, blog posts, PRDs, proposals	Outline → draft → revision pipeline	report-{topic}.md
data	Data cleaning, format conversion, aggregation	Batch processing with validation	data-{topic}.csv/json
code-review	PR review, security audit, tech debt assessment	Directory scan → module-level deep review	review-report.md
hybrid (auto-detected)	"Research competitors and write a report"	Primary type gets full template, secondary type condensed	Multiple files + index.md

Error Handling

The skill uses a structured retry matrix for resilient execution:

Error Type	1st Retry	2nd Retry	Final Fallback
WebSearch no results	Change keywords	Change search angle	Mark "needs manual follow-up"
WebFetch rejected (403/429)	Wait 5s and retry	Try alternative URL	Use search snippet instead
WebFetch empty content	Try alternative source	—	Mark "needs manual follow-up"
File write failure	Check directory permissions	Write to /tmp backup	Log error and continue
Token budget exceeded	Enter economy mode	Simplify remaining steps	Skip non-essential steps

Abnormal termination is also handled gracefully — all completed deliverables are saved, a partial morning summary is generated, and a warning notification is sent.

Token Efficiency

The skill enforces token conservation rules throughout execution:

• Immediate filtering — Only top 3-5 search results proceed to the next step
• Content extraction — Web pages are distilled to 500-1000 key characters immediately after fetch; full HTML is discarded
• File-based intermediates — Large intermediate data is written to files rather than kept in conversation context
• Real-time monitoring — Token usage is estimated after each step; economy mode activates at 80% budget usage
• No duplicate searches — Similar queries reuse previous results

Economy mode (activated when budget is tight):

• Search results limited to top 3 (normally 5)
• WebFetch truncated to first 2000 characters
• Non-essential steps (formatting, extra comparisons) are skipped
• All economy-mode actions logged in progress.log

Examples

Example 1: Competitive Research

/overnight-worker 调研 2024 年主流 AI 代码助手工具（Cursor、GitHub Copilot、Cody 等），\
  从功能、定价、用户评价三个维度对比，输出对比报告 \
  --type research --notify telegram

Expected output:

• report-ai-code-assistants.md — Full comparative report with tables and key findings
• morning-summary.md — Quick overview with top 5 insights
• Telegram notification on completion

Example 2: Code Security Audit

/overnight-worker 对 src/api/ 目录做安全审查，重点关注 OWASP Top 10 漏洞 \
  --type code-review --budget 100000

Expected output:

• review-report.md — Findings sorted by severity (🔴🟡🟢) with fix suggestions and code snippets
• morning-summary.md — Count of critical/warning/suggestion issues

Example 3: Data Collection

/overnight-worker 收集 GitHub 上 star 数 Top 30 的 Python Web 框架，\
  整理名称、star 数、最近更新时间、许可证类型 \
  --type data --format csv

Expected output:

• data-python-web-frameworks.csv — Structured CSV with all fields
• data-python-web-frameworks-meta.md — Field definitions and data source notes
• morning-summary.md — Record count and data quality summary

Compatibility

Platform	Support
OpenClaw	✅ Full support
Claude Code	✅ Full support
macOS	✅ Native notifications
Linux	✅ Runs (notifications via Telegram/Webhook)

Required tools: Bash, Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Task, Agent

Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Make your changes and test with a real overnight task
4. Submit a pull request

Areas for contribution:

• Additional task type templates
• New notification channels (Slack, Discord, email)
• Output format support (XLSX, HTML)
• Localization improvements

License

MIT — use it freely, sleep soundly.

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中文说明

功能概述

Overnight Worker 是一个夜间自主工作 Agent 技能。睡前给它一条任务，它会自动完成调研、写作、数据整理或代码审查，早晨生成结构化的晨间摘要和完整产出物。

核心能力：

• 智能任务分类与拆解（3-10 个子步骤）
• 多格式输出（Markdown / CSV / JSON）
• 多渠道通知（Telegram / macOS / Webhook）
• Token 预算管理与节约模式
• 结构化错误恢复
• 安全隔离（仅写入专属工作目录）

安装

# ClawHub 安装
clawhub install overnight-worker

# 手动安装
cp -r overnight-worker ~/.claude/skills/

使用示例

# 调研任务
/overnight-worker 调研主流 AI 框架的优劣对比

# 写作任务（指定格式）
/overnight-worker 写一份技术方案文档 --type writing --format md

# 数据整理（JSON 输出 + Telegram 通知）
/overnight-worker 收集开源 LLM 数据 --type data --format json --notify telegram

# 代码审查
/overnight-worker 审查 src/ 目录安全性 --type code-review

输出位置

所有产出物保存在 ~/overnight-output/YYYY-MM-DD/HHMMSS/ 目录下。早晨先看 morning-summary.md 获取全局概览。

通知配置

• macOS：开箱即用，支持 terminal-notifier 和 osascript
• Telegram：设置 TELEGRAM_BOT_TOKEN 和 TELEGRAM_CHAT_ID 环境变量
• Webhook：设置 WEBHOOK_URL 环境变量