harness

Scan codebases to generate structured context documents for AI agents.

2|Updated Jan 24, 2026

Search Tags:#code optimization #harness engineering #codebase transformation #AI agent collaboration #engineering knowledge extraction

Ready to run

npx skills add https://github.com/jtsang4/efficient-coding --skill harness-jtsang4

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: harness
Source: https://github.com/jtsang4/efficient-coding/tree/main/skills/harness
Command: npx skills add https://github.com/jtsang4/efficient-coding --skill harness-jtsang4

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, yaml, pyyaml, jinja2, astroid, isort, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill transforms your codebase into an environment where AI agents can work reliably and autonomously, improving coding agent productivity and providing clear guidelines for agent-friendly development.

Core Features & Use Cases

Codebase Optimization: Scan your codebase for structure, patterns, and tooling, then generate structured context documents and engineering changes.
Agent-Friendly Development: Establish constraints, guardrails, or feedback loops for AI agents, improving collaboration and efficiency.
Use Case: When you want to make your codebase agent-ready, set up AGENTS.md or agent context, or enhance AI coding agent productivity.

Quick Start

Use the harness skill to optimize your codebase for AI agents by running the command: bunx -y harness.

Frequently Asked Questions about harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize my codebase for AI agent collaboration?▼

Making a codebase agent-ready involves scanning its structure and patterns to extract engineering knowledge, then generating structured context documents like AGENTS.md to provide clear constraints and guardrails for autonomous coding agents.

What is harness engineering for AI coding agents?▼

Harness engineering is the process of transforming a codebase into an environment where AI agents can work reliably by applying constraints, guardrails, and feedback loops to improve autonomous development productivity.

Do I need Python to generate an AGENTS.md context file for my repository?▼

Yes, generating structured context documents requires Python along with YAML, pyyaml, jinja2, astroid, and isort libraries to properly parse and analyze the codebase structure and tooling.

How do I extract engineering knowledge from my codebase patterns?▼

You extract engineering knowledge by scanning the codebase to detect its underlying structure, coding patterns, and existing tooling, which is then structured into context documents for AI agent consumption.

What's the best way to set up constraints and guardrails for AI coding agents?▼

The best way to establish AI agent constraints is by transforming your codebase with structured context documents that define explicit feedback loops and operational guardrails for reliable autonomous development.

Can I use this approach to improve AI agent productivity in an existing codebase?▼

Yes, applying harness engineering to an existing codebase optimizes the development environment by generating structured context and engineering changes that significantly improve AI coding agent efficiency and reliability.