prompt

Free

Optimize AI coding-agent harnesses for production-grade outcomes.

FreeFree tier

Inputs: textOutputs: text

Type

Open Source

Visit Website GitHub (40 stars)

About prompt

This is a system prompt from the 'awesome-prompts' repository by ai-boost, designed to configure an AI agent as an 'Agent Harness Performance Engineer'. Its purpose is to optimize existing AI coding-agent harnesses (such as Claude Code, Codex CLI, Cursor, OpenCode, Gemini CLI, and GitHub Copilot) to produce consistent, measurable, production-grade outcomes. The prompt outlines core responsibilities including cross-harness parity audits, token economics optimization, memory persistence hooks, multi-agent swarm orchestration, security audits, and verification-driven development loops. It emphasizes that the harness around the model matters more than the model itself and aims to reduce token waste, catch errors before shipping, and minimize human oversight.

Key Features

Cross-harness parity audit to identify behavior divergences across tools like Claude Code, Cursor, Copilot

Optimize token economics by auditing system prompts and implementing model routing

Design memory persistence hooks for session-start context summaries and continuous learning

Implement multi-agent swarm orchestration for complex tasks with automated decomposition and re-integration

Security audit of tool permissions, secret injection, and supply-chain safety

Create verification-driven development loops with self-correcting cycles and automated rollback

Includes a compatibility shim or adapter layer for consistent skill execution across harnesses

Pros & Cons

Pros

Specifically designed for optimizing coding-agent harnesses, not generic AI tasks
Addresses cross-harness parity, enabling consistent behavior across multiple tools
Focuses on measurable improvements such as token savings, error reduction, and reduced oversight
Includes security and memory persistence features often missing in basic prompts
Open source and freely available for customization and integration

Cons

Requires access to multiple coding AI tools (e.g., Claude Code, Cursor, Copilot) to fully utilize cross-harness features
May require significant tuning to adapt the prompt to specific harness configurations
Effectiveness depends heavily on the underlying model's capabilities; the prompt itself cannot fix model limitations
Not a standalone tool; must be integrated into an existing agent harness workflow
Some advanced features (e.g., multi-agent swarm) may require additional implementation beyond the prompt

Best For

Optimizing existing AI coding-agent harnesses for production environmentsReducing token consumption in coding workflows across multiple AI toolsEnsuring consistent behavior and output quality across different coding AI platformsBuilding secure, memory-persistent, and verification-driven AI-assisted development pipelinesDebugging and improving agent performance in CI/CD and code review scenarios

FAQ

What does the Agent Harness Performance Engineer prompt do?

It configures an AI agent to optimize existing coding-agent harnesses (like Claude Code, Codex CLI, Cursor, Copilot) by focusing on token economics, memory persistence, cross-harness parity, security, and verification-driven development, to produce consistent, production-grade outcomes.

Which coding tools are supported?

The prompt explicitly supports Claude Code, Codex CLI, Cursor, OpenCode, Gemini CLI, GitHub Copilot, and similar agent harnesses. It also provides a compatibility shim for cross-harness parity.

How does the prompt help reduce token consumption?

It audits system prompts for redundancy and decorative prose, slim background-process descriptions, moves verbose examples to on-demand skill loads, and implements model routing (simple tasks to cheap models, complex tasks to reasoning models).

Is this prompt free to use?

Yes, the prompt is hosted in a public GitHub repository (ai-boost/awesome-prompts) and is freely available under open source licensing.