ai-engineering-harness: Instructions file for Codex

AGENTS.md

ai-engineering-harness AGENTS.md is an instructions file for Codex, OpenCode from adrielp/ai-engineering-harness. It costs 1,784 tokens per session, scanned A, original, Apache-2.0.

A configuration guide for an AI coding-agent toolkit with reusable prompts, helper agents, commands, and workflows. It supports OpenCode, Claude Code, Gemini CLI, and Pi.

In plain words
What is it for?
Use it to install the harness for one or more supported coding tools and to find its agents, commands, skills, and configuration files.
Why use it?
It gives coding agents a shared repository layout and setup instructions, so you do not have to configure each supported tool from scratch.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: reads .claude/ paths; mentions CLAUDE.md; mentions subagents.

This is adrielp/ai-engineering-harness's own configuration. It tells Codex and OpenCode how to work on ai-engineering-harness itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-engineering-harness configures →

Reuse

Borrowing it

Nothing to install: this file belongs to adrielp/ai-engineering-harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/adrielp/ai-engineering-harness/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/adrielp/ai-engineering-harness

Made for: Codex, OpenCode.

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Per session 1,784 This file is loaded in full into every session.
When invoked 1,784 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.01784 $0.01784
Opus 5.5 $0.00714 $0.00714
Sonnet 5.5 $0.00357 $0.00357
Haiku 4.5 $0.00178 $0.00178

Measured 1mo ago against content hash f51684598ebe, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-engineering-harness AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Engineering Harness

For AI Agents: This document provides structured reference data for AI coding assistants. For human-readable documentation, see README.md.

Quick Reference

Purpose: Configuration harness for AI coding agents with reusable prompts, agents, and workflows.

Supported Tools: OpenCode, Claude Code, Gemini CLI, Pi

Installation: ./setup.sh <tool> where tool is opencode, claude, gemini, pi, or all

Repository Structure

ai-engineering-harness/
├── opencode/           → ~/.config/opencode/
│   ├── agents/         # 6 agents (snake_case)
│   ├── commands/       # 11 slash commands
│   ├── skills/         # 15 skills (auto-triggered)
│   └── opencode.json   # MCP configuration
├── claude/             → ~/.claude/
│   ├── agents/         # 6 agents (snake_case)
│   ├── skills/         # 26 skills (13 manual + 13 auto)
│   ├── .mcp.json       # MCP configuration
│   └── settings.json   # Settings schema
├── gemini/             → ~/.gemini/
│   ├── agents/         # 6 agents (snake_case)
│   ├── commands/       # 14 commands (TOML format)
│   └── skills/         # 23 skills (auto-triggered)
├── pi/                 → ~/.pi/agent/
│   ├── agents/         # 6 agents (kebab-case)
│   ├── prompts/        # 11 prompt templates (Pi's commands)
│   ├── skills/         # 21 skills (auto-triggered)
│   └── extensions/     # subagent extension (multi-agent workflows)
└── thoughts/           # Context engineering artifacts
    ├── shared/tickets/ # Work items
    ├── shared/plans/   # Implementation plans
    ├── shared/research/# Research documents
    └── global/         # Cross-repo concerns

Commands & Skills

Command OpenCode Claude Gemini Pi Type Description
/init_harness ✓ ✓ ✓ ✓ Manual Initialize harness (creates AGENTS.md/CLAUDE.md/GEMINI.md + thoughts/)
/create_plan ✓ ✓ ✓ ✓ Manual Generate implementation plan from ticket
/implement_plan ✓ ✓ ✓ ✓ Manual Execute approved plan phase-by-phase
/validate_plan ✓ ✓ ✓ ✓ Manual Verify implementation against plan
/commit ✓ ✓ ✓ ✓ Manual Create well-structured git commits
/debug ✓ ✓ ✓ ✓ Manual Investigate issues during testing
/debug_k8s ✓ ✓ ✓ ✓ Manual Debug Kubernetes (prefers MCP, falls back to kubectl)
/research_codebase ✓ ✓ ✓ ✓ Manual Comprehensive codebase research
/validate_telemetry ✓ ✓ ✓ ✓ Manual Validate local telemetry against a narrative spec
observability_driven_development ✓ ✓ ✓ ✓ Auto Design the trace before the feature; local OTel feedback loop
git_commit_helper ✓ ✓ ✓ ✓ Auto Triggers on "commit" keywords
pr_description_generator ✓ ✓ ✓ ✓ Auto Triggers when creating PRs
experimental_pr_workflow ✓ ✓ ✓ ✓ Auto Formalizes experimental work
interview ✓ ✓ ✓ ✓ Auto Stress-test plans via relentless user interview
improve_codebase_architecture ✓ ✓ ✓ ✓ Auto Find architectural friction, propose deep-module refactors
prd_to_issues ✓ ✓ ✓ ✓ Auto Break a PRD into vertical-slice issue files
tdd ✓ ✓ ✓ ✓ Auto Red-green-refactor TDD discipline
write_a_prd ✓ ✓ ✓ ✓ Auto Generate a PRD from a client brief

Read the full file on GitHub · 134 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 134 lines · 1,784 tokens per session scan A f51684598ebe

Subscribe to this mod's changes

ai-engineering-harness AGENTS.md is an instructions file published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 1,784 tokens to every session, about $0.0071 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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