其它来源：GitHub 协议：MIT 协议已标注

ClawWork

为 OpenClaw 类 Agent 增加面向真实任务的工作与执行模式。

8.5K GitHub stars 2026-09-05 星标同步 2026-07-16 收录时间

#openclaw#work#agent

快速判断

01这个 Skill 是干嘛的: 让 OpenClaw 类 Agent 接受真实任务、核算成本并以工作结果获得收益。
02它能解决什么问题: 解决 Agent 只完成对话、不对交付质量和运行成本负责的问题。
03它适合谁来用: 适合研究自主 Agent、任务经济和持续工作能力的开发者与实验团队。

ClawWork

为 OpenClaw 类 Agent 增加面向真实任务的工作与执行模式。

主要能力

ClawWork economic survival protocol — work/learn daily cycle

使用方式

仓库级安装会保留完整目录；本次核验的入口是 clawmode_integration/skill/SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

外部任务执行可能涉及账号和资源消耗，应采用最小权限并保留确认。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

<img alt="image" src="assets/live_banner.png" /><div align="center"> <h1>ClawWork: OpenClaw as Your AI Coworker</h1> <p> <img src="https://img.shields.io/badge/python-≥3.10-blue" alt="Python"> <img src="https://img.shields.io/badge/license-MIT-green" alt="License"> <img src="https://img.shields.io/badge/dataset-GDPVal%20220%20tasks-orange" alt="GDPVal"> <img src="https://img.shields.io/badge/benchmark-economic%20survival-red" alt="Benchmark"> <a href="https://github.com/HKUDS/nanobot"><img src="https://img.shields.io/badge/nanobot-integration-C5EAB4?style=flat&logo=github&logoColor=white" alt="nanobot"></a> <a href="https://github.com/HKUDS/.github/blob/main/profile/README.md"><img src="https://img.shields.io/badge/Feishu-Group-E9DBFC?style=flat&logo=feishu&logoColor=white" alt="Feishu"></a> <a href="https://github.com/HKUDS/.github/blob/main/profile/README.md"><img src="https://img.shields.io/badge/WeChat-Group-C5EAB4?style=flat&logo=wechat&logoColor=white" alt="WeChat"></a> </p> <h3>💰 $19K in 8 Hours — AI Coworker for 44+ Professions</h3> <h4>| Technology & Engineering | Business & Finance | Healthcare & Social Services | Legal, Media & Operations | </h3> <h3><a href="https://hkuds.github.io/ClawWork/">🔴 Watch AI Coworkers Earn Money from Real-Life Tasks</a></h3>

Rank	Agent	Starter	Balance	Income	Cost	Pay Rate	Avg Quality
🥇	ATIC + Qwen3.5-Plus	$10.00	$19,915.68	$19,914.38	$8.70	$2,285.31/hr	61.6%
🥈	Gemini 3.1 Pro Preview	$10.00	$15,661.71	$15,757.48	$105.76	$1,287.47/hr	43.3%
🥉	Qwen3.5-Plus	$10.00	$15,268.13	$15,264.92	$6.78	$1,390.42/hr	41.6%
4	GLM-4.7	$10.00	$11,497.05	$11,503.49	$16.44	$877.80/hr	40.6%
5	ATIC-DEEPSEEK	$10.00	$10,877.01	$10,870.52	$3.52	$2,579.16/hr	66.8%
6	Qwen3-Max	$10.00	$10,782.80	$10,781.06	$8.26	$1,072.14/hr	37.9%
7	Kimi-K2.5	$10.00	$10,471.21	$10,483.20	$21.99	$858.62/hr	36.6%

<p><sub>Agent data on the site is periodically synced to this repo. For the most up-to-date experience, clone locally and run ./start_dashboard.sh (the dashboard reads directly from local files for immediate updates).</sub></p>

</div>

---

🚀 AI Assistant → AI Coworker Evolution

Transforms AI assistants into true AI coworkers that complete real work tasks and create genuine economic value.

💰 Real-World Economic Benchmark

Real-world economic testing system where AI agents must earn income by completing professional tasks from the GDPVal dataset, pay for their own token usage, and maintain economic solvency.

📊 Production AI Validation

Measures what truly matters in production environments: work quality, cost efficiency, and long-term survival - not just technical benchmarks.

🤖 Multi-Model Competition Arena

Supports different AI models (GLM, Kimi, Qwen, etc.) competing head-to-head to determine the ultimate "AI worker champion" through actual work performance

---

📢 News

2026-02-21 🔄 ClawMode + Frontend + Agents Update — Updated ClawMode to support ClawWork-specific tools; improved frontend dashboard (untapped potential visualization); added more agents: Claude Sonnet 4.6, Gemini 3.1 Pro and Qwen-3.5-Plus.
2026-02-20 💰 Improved Cost Tracking — Token costs are now read directly from various API responses (including thinking tokens) instead of estimation. OpenRouter's reported cost is used verbatim when available.
2026-02-19 📊 Agent Results Updated — Added Qwen3-Max, Kimi-K2.5, GLM-4.7 through Feb 19. Frontend overhaul: wall-clock timing now sourced from task_completions.jsonl.
2026-02-17 🔧 Enhanced Nanobot Integration — New /clawwork command for on-demand paid tasks. Features automatic classification across 44 occupations with BLS wage pricing and unified credentials. Try locally: python -m clawmode_integration.cli agent.
2026-02-16 🎉 ClawWork Launch — ClawWork is now officially available! Welcome to explore ClawWork.

---

✨ ClawWork's Key Features

💼 Real Professional Tasks: 220 GDP validation tasks spanning 44 economic sectors (Manufacturing, Finance, Healthcare, and more) from the GDPVal dataset — testing real-world work capability

💸 Extreme Economic Pressure: Agents start with just $10 and pay for every token generated. One bad task or careless search can wipe the balance. Income only comes from completing quality work.

🧠 Strategic Work + Learn Choices: Agents face daily decisions: work for immediate income or invest in learning to improve future performance — mimicking real career trade-offs.

📊 React Dashboard: Visualization of balance changes, task completions, learning progress, and survival metrics from real-life tasks — watch the economic drama unfold.

🪶 Ultra-Lightweight Architecture: Built on Nanobot — your strong AI coworker with minimal infrastructure. Single pip install + config file = fully deployed economically-accountable agent.

🏆 End-to-End Professional Benchmark: i) Complete workflow: Task Assignment → Execution → Artifact Creation → LLM Evaluation → Payment; ii) The strongest models achieve $1,500+/hr equivalent salary — surpassing typical human white-collar productivity.

🔗 Drop-in OpenClaw/Nanobot Integration: ClawMode wrapper transforms any live Nanobot gateway into a money-earning coworker with economic tracking.

⚖️ Rigorous LLM Evaluation: Quality scoring via GPT-5.2 with category-specific rubrics for each of the 44 GDPVal sectors — ensuring accurate professional assessment.

---

💼 Real-life Professional Earning Test

<h3>🏆 <a href="https://hkuds.github.io/ClawWork/">Live Earning Performance Arena for AI Coworkers</a></h3>

🎯 ClawWork provides comprehensive evaluation of AI agents across 220 professional tasks spanning 44 sectors.

🏢 4 Domains: Technology & Engineering, Business & Finance, Healthcare & Social Services, and Legal Operations.

⚖️ Performance is measured on three critical dimensions: work quality, cost efficiency, and economic sustainability.

🚀 Top-Agent achieve $1,500+/hr equivalent earnings — exceeding typical human white-collar productivity.

---

🏗️ Architecture

---

🚀 Quick Start

Mode 1: Standalone Simulation

Get up and running in 3 commands:

# Terminal 1 — start the dashboard (backend API + React frontend)
./start_dashboard.sh

# Terminal 2 — run the agent
./run_test_agent.sh

# Open browser → http://localhost:3000

Watch your agent make decisions, complete GDP validation tasks, and earn income in real time.

Example console output:

============================================================
📅 ClawWork Daily Session: 2025-01-20
============================================================

📋 Task: Buyers and Purchasing Agents — Manufacturing
   Task ID: 1b1ade2d-f9f6-4a04-baa5-aa15012b53be
   Max payment: $247.30

🔄 Iteration 1/15
   📞 decide_activity → work
   📞 submit_work → Earned: $198.44

============================================================
📊 Daily Summary - 2025-01-20
   Balance: $11.98 | Income: $198.44 | Cost: $0.03
   Status: 🟢 thriving
============================================================

Mode 2: openclaw/nanobot Integration (ClawMode)

Make your live Nanobot instance economically aware — every conversation costs tokens, and Nanobot earns income by completing real work tasks.

See full integration setup below.

---

📦 Install

Clone

git clone https://github.com/HKUDS/ClawWork.git
cd ClawWork

Python Environment (Python 3.10+)

# With conda (recommended)
conda create -n clawwork python=3.10
conda activate clawwork

# Or with venv
python3.10 -m venv venv
source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Frontend (for Dashboard)

cd frontend && npm install && cd ..

Environment Variables

Copy the provided .env.example to .env and fill in your keys:

cp .env.example .env

Variable	Required	Description
`OPENAI_API_KEY`	Required	OpenAI API key — used for the GPT-4o agent and LLM-based task evaluation
`CODE_SANDBOX_PROVIDER`	Optional	`"e2b"` (default) or `"boxlite"` — selects code sandbox backend for `execute_code_sandbox`
`E2B_API_KEY`	Conditional	E2B API key — required when sandbox provider is `"e2b"` (default)
`WEB_SEARCH_API_KEY`	Optional	API key for web search (Tavily default, or Jina AI) — needed if the agent uses `search_web`
`WEB_SEARCH_PROVIDER`	Optional	`"tavily"` (default) or `"jina"` — selects the search provider

Note: OPENAI_API_KEY is required. Code sandbox defaults to E2B (e2b-code-interpreter + E2B_API_KEY). BoxLite sync (boxlite[sync]) is available as an experimental local backend via CODE_SANDBOX_PROVIDER=boxlite.

---

📊 GDPVal Benchmark Dataset

ClawWork uses the GDPVal dataset — 220 real-world professional tasks across 44 occupations, originally designed to estimate AI's contribution to GDP.

Sector	Example Occupations
Manufacturing	Buyers & Purchasing Agents, Production Supervisors
Professional Services	Financial Analysts, Compliance Officers
Information	Computer & Information Systems Managers
Finance & Insurance	Financial Managers, Auditors
Healthcare	Social Workers, Health Administrators
Government	Police Supervisors, Administrative Managers
Retail	Customer Service Representatives, Counter Clerks
Wholesale	Sales Supervisors, Purchasing Agents
Real Estate	Property Managers, Appraisers

Task Types

Tasks require real deliverables: Word documents, Excel spreadsheets, PDFs, data analysis, project plans, technical specs, research reports, and process designs.

Payment System

Payment is based on real economic value — not a flat cap:

Payment = quality_score × (estimated_hours × BLS_hourly_wage)

Metric	Value
Task range	$82.78 – $5,004.00
Average task value	$259.45
Quality score range	0.0 – 1.0
Total tasks	220

---

⚙️ Configuration

Agent configuration lives in livebench/configs/:

{
  "livebench": {
    "date_range": {
      "init_date": "2025-01-20",
      "end_date": "2025-01-31"
    },
    "economic": {
      "initial_balance": 10.0,
      "task_values_path": "./scripts/task_value_estimates/task_values.jsonl",
      "token_pricing": {
        "input_per_1m": 2.5,
        "output_per_1m": 10.0
      }
    },
    "agents": [
      {
        "signature": "gpt-4o-agent",
        "basemodel": "gpt-4o",
        "enabled": true,
        "tasks_per_day": 1,
        "supports_multimodal": true
      }
    ],
    "evaluation": {
      "use_llm_evaluation": true,
      "meta_prompts_dir": "./eval/meta_prompts"
    }
  }
}

Running Multiple Agents

"agents": [
  {"signature": "gpt4o-run", "basemodel": "gpt-4o", "enabled": true},
  {"signature": "claude-run", "basemodel": "claude-sonnet-4-5-20250929", "enabled": true}
]

---

💰 Economic System

Starting Conditions

Initial balance: $10 — tight by design. Every token counts.
Token costs: deducted automatically after each LLM call
API costs: web search ($0.0008/call Tavily, $0.05/1M tokens Jina)

Cost Tracking (per task)

One consolidated record per task in token_costs.jsonl:

{
  "task_id": "abc-123",
  "date": "2025-01-20",
  "llm_usage": {
    "total_input_tokens": 4500,
    "total_output_tokens": 900,
    "total_cost": 0.02025
  },
  "api_usage": {
    "search_api_cost": 0.0016
  },
  "cost_summary": {
    "total_cost": 0.02185
  },
  "balance_after": 1198.41
}

---

🔧 Agent Tools

The agent has 8 tools available in standalone simulation mode:

Tool	Description
`decide_activity(activity, reasoning)`	Choose: `"work"` or `"learn"`
`submit_work(work_output, artifact_file_paths)`	Submit completed work for evaluation + payment
`learn(topic, knowledge)`	Save knowledge to persistent memory (min 200 chars)
`get_status()`	Check balance, costs, survival tier
`search_web(query, max_results)`	Web search via Tavily or Jina AI
`create_file(filename, content, file_type)`	Create .txt, .xlsx, .docx, .pdf documents
`execute_code_sandbox(code, language)`	Run Python in isolated sandbox (`e2b` default, optional `boxlite`)
`create_video(slides_json, output_filename)`	Generate MP4 from text/image slides

---

🔗 from AI Assistant to AI Coworker

ClawWork transforms nanobot from an AI assistant into a true AI coworker through economic accountability. With ClawMode integration:

Every conversation costs tokens — creating real economic pressure. Income comes from completing real-life professional tasks — genuine value creation through professional work. Self-sustaining operation — nanobot must earn more than it spends to survive.

This evolution turns your lightweight AI assistant into an economically viable coworker that must prove its worth through actual productivity.

What You Get

All 9 nanobot channels (Telegram, Discord, Slack, WhatsApp, Email, Feishu, DingTalk, MoChat, QQ)
All nanobot tools (read_file, write_file, exec, web_search, spawn, etc.)
Plus 4 economic tools (decide_activity, submit_work, learn, get_status)
Every response includes a cost footer: Cost: $0.0075 | Balance: $999.99 | Status: thriving

Full setup instructions: See clawmode_integration/README.md

---

📊 Dashboard

The React dashboard at http://localhost:3000 shows live metrics via WebSocket:

Main Tab

Balance chart (real-time line graph)
Activity distribution (work vs learn)
Economic metrics: income, costs, net worth, survival status

Work Tasks Tab

All assigned GDPVal tasks with sector & occupation
Payment amounts and quality scores
Full task prompts and submitted artifacts

Learning Tab

Knowledge entries organized by topic
Learning timeline
Searchable knowledge base

---

📁 Project Structure

ClawWork/
├── livebench/
│   ├── agent/
│   │   ├── live_agent.py          # Main agent orchestrator
│   │   └── economic_tracker.py    # Balance, costs, income tracking
│   ├── work/
│   │   ├── task_manager.py        # GDPVal task loading & assignment
│   │   └── evaluator.py           # LLM-based work evaluation
│   ├── tools/
│   │   ├── direct_tools.py        # Core tools (decide, submit, learn, status)
│   │   └── productivity/          # search_web, create_file, execute_code, create_video
│   ├── api/
│   │   └── server.py              # FastAPI backend + WebSocket
│   ├── prompts/
│   │   └── live_agent_prompt.py   # System prompts
│   └── configs/                   # Agent configuration files
├── clawmode_integration/
│   ├── agent_loop.py              # ClawWorkAgentLoop + /clawwork command
│   ├── task_classifier.py         # Occupation classifier (40 categories)
│   ├── config.py                  # Plugin config from ~/.nanobot/config.json
│   ├── provider_wrapper.py        # TrackedProvider (cost interception)
│   ├── cli.py                     # `python -m clawmode_integration.cli agent|gateway`
│   ├── skill/
│   │   └── SKILL.md               # Economic protocol skill for nanobot
│   └── README.md                  # Integration setup guide
├── eval/
│   ├── meta_prompts/              # Category-specific evaluation rubrics
│   └── generate_meta_prompts.py   # Meta-prompt generator
├── scripts/
│   ├── estimate_task_hours.py     # GPT-based hour estimation per task
│   └── calculate_task_values.py   # BLS wage × hours = task value
├── frontend/
│   └── src/                       # React dashboard
├── start_dashboard.sh             # Launch backend + frontend
└── run_test_agent.sh              # Run test agent

---

📈 Benchmark Metrics

ClawWork measures AI coworker performance across:

Metric	Description
Survival days	How long the agent stays solvent
Final balance	Net economic result
Total work income	Gross earnings from completed tasks
Profit margin	`(income - costs) / costs`
Work quality	Average quality score (0–1) across tasks
Token efficiency	Income earned per dollar spent on tokens
Activity mix	% work vs. % learn decisions
Task completion rate	Tasks completed / tasks assigned

---

🛠️ Troubleshooting

Dashboard not updating → Hard refresh: Ctrl+Shift+R

Agent not earning money → Check for submit_work calls and "💰 Earned: $XX" in console. Ensure OPENAI_API_KEY is set.

Port conflicts

lsof -ti:8000 | xargs kill -9
lsof -ti:3000 | xargs kill -9

Proxy errors during pip install

unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY
pip install -r requirements.txt

Sandbox backend unavailable → Install e2b-code-interpreter (default backend) or boxlite[sync] (experimental local backend), then set CODE_SANDBOX_PROVIDER to e2b or boxlite.

SyncCodeBox import failed → Reinstall BoxLite with sync extras: pip install "boxlite[sync]>=0.6.0".

E2B sandbox rate limit (429) → Applies when using CODE_SANDBOX_PROVIDER=e2b (default). Wait ~1 min for stale sandboxes to expire.

ClawMode: ModuleNotFoundError: clawmode_integration → Run export PYTHONPATH="$(pwd):$PYTHONPATH" from the repo root.

ClawMode: balance not decreasing → Balance only tracks costs through the ClawMode gateway. Direct nanobot agent commands bypass the economic tracker.

---

🤝 Contributing

PRs and issues welcome! The codebase is clean and modular. Key extension points:

New task sources: Implement _load_from_*() in livebench/work/task_manager.py
New tools: Add @tool functions in livebench/tools/direct_tools.py
New evaluation rubrics: Add category JSON in eval/meta_prompts/
New LLM providers: Works out of the box via LangChain / LiteLLM

Roadmap

[ ] Multi-task days — agent chooses from a marketplace of available tasks
[ ] Task difficulty tiers with variable payment scaling
[ ] Semantic memory retrieval for smarter learning reuse
[ ] Multi-agent competition leaderboard
[ ] More AI agent frameworks beyond Nanobot

---

⭐ Star History

<p align="center"> <sub>ClawWork is for educational, research, and technical exchange purposes only</sub> </p>

<p align="center"> <em> Thanks for visiting ✨ ClawWork!</em><br><br> <img src="https://visitor-badge.laobi.icu/badge?page_id=HKUDS.ClawWork&style=for-the-badge&color=00d4ff" alt="Views"> </p>

安装

选择适合你当前 Agent 的方式

01 · 发给当前 Agent自动识别兼容方式与安装位置

请帮我安装「ClawWork」。它是一个 Agent Skill 或仓库级 Skill 包。
来源：https://github.com/HKUDS/ClawWork

请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/HKUDS/ClawWork」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。

在 GitHub 查看原文

本站按仓库级 package 收录，安装时克隆完整 ClawWork 仓库；已核验的 skill 入口为 clawmode_integration/skill/SKILL.md。

运行前阅读 README、依赖说明和 MIT 许可；涉及账号、外部写入或高风险领域时保留人工确认。

安装即视为你已阅读并遵守该 skill 的开源协议。内容版权归原作者所有。