tigerless-labs/autoharness
768 stars · Last commit 2026-07-28
Autoharness — a self-learning skill layer for Claude Code — distills skills from your real sessions, updates them as you work, and prunes the ones that stop getting used. No daemon, no benchmark.
README preview
<h1 align="center">AutoHarness</h1> <p align="center"><strong>Self-Learning Skills for Claude Code</strong></p> <p align="center"> <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fraw.githubusercontent.com%2Ftigerless-labs%2Fautoharness%2Fmain%2F.claude-plugin%2Fplugin.json&query=%24.version&label=release&prefix=v&color=brightgreen" 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. |