autoharness

<h1 align="center">AutoHarness</h1>
<p align="center"><strong>Building the self-learning layer next-gen agents will ship with</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" /> <a href="https://repositoryradar.dev/repo/tigerless-labs/autoharness"><img src="https://repositoryradar.dev/badge/tigerless-labs/autoharness.svg" alt="Featured on Repository Radar" /></a>
</p>

**With autoharness, we're building the self-learning layer next-gen agents will ship with, learning skills from real work.**
Today it runs on 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. It fires on its own once a session has done enough work; `/learn` distills on demand when you want a lesson kept now. |
| **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. A fold records which skill absorbed which, so a merge is never mistaken for a death. |
| **Keeps its own library in view** | Every session opens with a grouped index of the skills it wrote, so recall doesn't depend on the host happening to surface them. The host's native recall is left exactly as it was; the index is added on top. |
| **Validated in use, not on a benchmark** | A skill survives by being adhered to in later turns (loads over the requests it was available for), 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 bui