A high-performance enhancement system that empowers AI agents with autonomous task decomposition, tool orchestration, and persistent vector memory.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-autonomy-pro
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-autonomy-pro using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Autonomy Pro?
Agent Autonomy Pro is a professional-grade system designed to transform static AI interactions into dynamic, self-executing workflows. By integrating a sophisticated Task Planner and Tool Orchestrator, it allows agents to break down complex goals into actionable Directed Acyclic Graphs (DAGs). This skill is a cornerstone for developers building advanced Openclaw Skills that require minimal human intervention and high reliability.
The system includes a built-in Vector Memory layer using a lightweight TF-IDF approach, enabling semantic search and long-term context retention without external database dependencies. Whether managing complex file operations or navigating web browsers, Agent Autonomy Pro provides the logical backbone for truly autonomous AI behavior through its integrated Execution Monitor and heartbeat protocol.
Agent Autonomy Pro Use Cases
- Automating multi-step research and content publishing pipelines across different platforms like Zhihu or GitHub.
- Building self-healing outreach systems that track and follow up with leads autonomously based on success criteria.
- Creating persistent AI assistants that maintain a hierarchical memory of user preferences and past interactions.
- Orchestrating parallel tool executions for data-intensive processing tasks to maximize efficiency.
How Agent Autonomy Pro Works
- The system receives a high-level goal in natural language from the user.
- The Task Planner decomposes this goal into a Directed Acyclic Graph (DAG) consisting of specific subtasks and actions.
- The Tool Orchestrator automatically selects and invokes necessary tools such as browser control, file system access, or API fetchers.
- The Execution Monitor tracks task progress via a heartbeat protocol, detecting stalls and initiating automatic retries or failure recovery.
- The Vector Memory Layer indexes the execution results and new information, updating the agent's context for subsequent steps or future goals.
Agent Autonomy Pro Setup
To integrate this skill into your environment, ensure you have the core engine files located in your local skills directory. You will need to initialize the autonomy engine within your JavaScript environment.
# Navigate to your project directory
cd ./skills/agent-autonomy-pro
# Ensure the configuration file is initialized
touch memory/vector-index.json
Example implementation in your project:
const autonomy = require('./skills/agent-autonomy-pro/index.js');
await autonomy.execute("Research AI news and publish a report");
Agent Autonomy Pro Data Schema & Taxonomy
The system utilizes a structured memory and monitoring hierarchy to maintain state across complex Openclaw Skills operations.
| Data Component | Format | Function |
|---|---|---|
| Vector Index | memory/vector-index.json |
Stores TF-IDF weights for semantic memory retrieval |
| Session State | SESSION-STATE.md |
Tracks active task context and immediate working variables |
| Heartbeat | tasks/execution-heartbeat.txt |
Logs timestamps to monitor agent health and detect timeouts |
| Cold Storage | MEMORY.md |
Archive of historical task results and long-term knowledge |
Agent Autonomy Pro Advanced Features
- Multi-Step Autonomous Execution: Performs complex workflows without requiring step-by-step human confirmation.
- Parallel Task Processing: Ability to run independent subtasks simultaneously to significantly reduce total execution time.
- Semantic Vector Memory: A pure JavaScript implementation of TF-IDF for similarity search, providing 'memory' without external database costs.
- Heartbeat Monitoring: Automated system that restarts or recalibrates tasks if no progress is detected within a 5-minute threshold.
- Custom Planning Strategies: Configuration options to limit parallel tasks, set per-task timeouts, and toggle vector memory usage.
SKILL.md
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