Mark’s Pi Harness

This is the real, daily-driven config for the pi coding agent (@earendil-works/pi-coding-agent) running 100% local against LM Studio on a MacBook. No cloud LLM. No API bill. No internet required. The workhorse model is Qwen3.5 122B (MLX) on Apple Silicon, and this harness is the difference between a local model that flails and a local model that one-shots complete, verified websites in headless mode with zero tool errors.

Everything in this repo lives in ~/.pi/agent/ on the source machine. Clone it, steal the pieces you want, or drop the whole thing into your own pi config.

The receipts. With this exact setup, a locally-hosted 122B has built full production websites end to end through pi -p (headless, no human in the loop): design system, copy, GSAP animation, video heroes, its own browser-based visual verification, and a passing production build. A session audit across four consecutive headless builds found zero tool-call errors and zero edit-retry loops.

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Table of contents

1. Why local, and why a harness
2. How it all fits together
3. The extensions
4. The verification loop that changes everything
5. The skills
6. Headless mode, where it all pays off
7. Hard-won lessons
8. Design memory
9. Repo layout
10. Setup

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Why local, and why a harness

A frontier cloud model tolerates a sloppy setup. A local 122B does not. It will write CSS for HTML it never created, declare victory from log output, retry a failing command forever, and hang for 35 minutes on brew install when the wifi drops on a plane. None of that is fixed by a bigger prompt. It is fixed by infrastructure: tools that force the model to look at its work, gates it cannot talk its way past, guards that turn environmental chaos (offline, port conflicts, giant tool outputs) into structured signals it can act on.

That is what this harness is. Three layers, all included here:

• AGENTS.md, the standing rules. Working style, verification requirements, server discipline, code discipline, and the care rules for destructive actions.
• extensions/, 15 TypeScript modules pi loads at startup. They add tools (browser_check, bash_background, task, web_search, facts_recall), enforce discipline (gates, freshness, output caps, budget awareness), and adapt to the machine (offline detection, per-model prompt tuning).