<h1 align="center">autoharness</h1>

<p align="center"> <img src="https://img.shields.io/badge/release-v0.2.10-brightgreen.svg" alt="release" /> <img src="https://img.shields.io/badge/python-3.11%2B-blue.svg" alt="python" /> <img src="https://img.shields.io/badge/platform-Linux%20%7C%20macOS-lightgrey.svg" alt="platform" /> <img src="https://img.shields.io/badge/license-MIT-yellow.svg" alt="license MIT" /> </p>

**autoharness is a self-learning skill layer for Claude Code.** It **learns** skills from your real sessions, **merges** same-scenario ones instead of stacking near-duplicates, **updates** them in use, and **prunes** any that stop getting used — so the layer **stays clean on its own**, **touching only the skills it wrote itself**.

Same model, different harness — 42% → 78% on CORE-Bench ([HAL](https://arxiv.org/abs/2510.11977)). The harness does much of the work (swyx's **Big Model vs Big Harness**), yet it's still rebuilt by hand every model generation. autoharness bets one slice of it — the skill layer — can maintain itself.

| | | |---|---| | **Learns from real work** | Each episode is distilled into a skill from the session you were already having — no separate data-collection or replay loop. | | **Groups, doesn't just pile up** | A new episode doesn't always add a skill — the reflector compares it against what's there and folds same-scenario skills into one, so the layer consolidates by category instead of accreting near-duplicates. | | **Validated in use, not on a benchmark** | A skill survives by being adhered to in later turns (invocation rate), not a held-out score. No oracle on the active path, and no tokens spent on a dedicated eval. | | **Only its own skills** | Touches only the skills it generated through this plugin — everything else, whether you wrote it or installed it, is left completely alone. | | **Evidence kept for later** | Every create/update logs its scenario and decision to a per-skill ledger — the raw material to build a benc