A professional-grade engineering loop for AI agents that ensures request clarification, minimal code changes, and validated delivery.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-engineering-harness
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 openclaw-engineering-harness 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 Openclaw Engineering Harness?
The openclaw-engineering-harness is a robust, single-skill solution designed to manage the entire engineering lifecycle for AI agents. By consolidating request clarification, codebase discovery, tool-driven execution, and artifact preparation into one unified framework, it provides a reliable path for building complex software features. This skill is a cornerstone of Openclaw Skills, emphasizing the principle of the smallest coherent change to maintain codebase stability and high-quality results.
Built for developers who need their AI agents to act as autonomous yet disciplined engineers, this harness integrates a 3-layer runtime skeleton consisting of tools, state management, and policy enforcement. It ensures that every modification is mapped, implemented, and verified against real-world constraints before being declared complete, making it an essential component for any professional project using Openclaw Skills.
Openclaw Engineering Harness Use Cases
- Implementing complex features where scope clarification and goal alignment are critical.
- Managing codebase discovery and impact analysis in large, unfamiliar repositories.
- Automating the creation of publishable artifacts while adhering to strict export policies.
- Maintaining project context and user preferences through a persistent memory system within Openclaw Skills.
How Openclaw Engineering Harness Works
- Clarify: The agent analyzes the request shape and success criteria, asking for missing details before modifying code.
- Map: It creates a minimal working map of entry points, dependencies, and impact areas to identify validation points.
- Implement: The skill executes the smallest coherent change, strictly following existing project patterns and avoiding unrelated refactoring.
- Verify: Automated checks are performed immediately after changes to record results and identify any remaining risks.
- Deliver: The agent updates the project memory system, audits the final artifacts, and generates a distribution summary.
Openclaw Engineering Harness Setup
To integrate this skill into your project environment, ensure the relevant scripts and policies are available in your directory.
# Initialize the project memory system
mkdir -p .claude && touch .claude/MEMORY.md
# Execute the engineering workflow to generate an implementation plan
python3 scripts/run_workflow.py
# Run constraint checks to validate the environment
python3 scripts/run_constraints.py
Openclaw Engineering Harness Data Schema & Taxonomy
The openclaw-engineering-harness organizes its logic and metadata through a structured set of JSON policies and markdown reference files:
| File Component | Purpose |
|---|---|
tool/tool-config.json |
Defines available tools and the boundaries for single-skill execution. |
state/state-policy.json |
Manages state sequences and mandatory inputs for phase transitions. |
policy/constraint-policy.json |
Contains the rules for deliverable validation and boundary satisfaction. |
policies/workflow-policy.json |
Outlines the master phase order, checkpoints, and default configurations. |
.claude/MEMORY.md |
Stores persistent user preferences and project-specific technical context. |
Openclaw Engineering Harness Advanced Features
- Multi-layer runtime skeleton that separates tool execution from state management and policy enforcement.
- Automated audit system via scripts/run_audit.py to prevent exposing sensitive identifiers in Openclaw Skills deliverables.
- Integrated memory system that captures lessons learned during the delivery phase for future task context.
- Strict engineering iron laws that prevent the delivery of unverified code or unauthorized refactors.
SKILL.md
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