AutoHarness plugin
A self-learning skill layer for Claude Code that distills skills from your sessions, updates them in use and prunes the unused
- 11,041 ★ on the repository
- Last commit
- MIT
Extended access
Why this level
- Reads the content of your sessions to distill skills from them
- Runs Python hooks, spawns child sessions and writes skill files
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add plugins/autoharnessDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- Reads the content of your sessions to distill skills from them
- Runs Python hooks, spawns child sessions and writes skill files
This is third-party code. Review the repository files before installing.
What it does
The plugin learns from real Claude Code sessions and turns each episode into a skill without a separate data-collection loop. It folds same-scenario skills into one instead of piling up near-duplicates, updates them as you work and prunes the ones that stop firing. Every session opens with a grouped index of the skills it wrote itself. It touches only its own skills and leaves everything else, yours or installed, untouched. Learning fires automatically after enough tool calls, and the /learn command distills a lesson from the current session on demand. Each create and update is written to a per-skill ledger with its scenario and decision.
Who it is for. For Claude Code users who want their skill layer to build and maintain itself.
Good fit when
- You want good approaches from sessions saved as skills automatically
- You want to lock in a specific lesson with /learn right now
- You want unused skills pruned without manual cleanup
Not a fit when
- You do not work in Claude Code
- There is no python3 on PATH, or the OS is not Linux or macOS
Example request
/learn save the lesson from this session as a skillLimitations
It works only in Claude Code on Linux and macOS and needs python3 on PATH; without it the hooks and the internal MCP server do not run. Third-party marketplaces have auto-update off by default, so updates are manual with a restart. Background reflection spawns child sessions, which spends tokens; the pace is tunable via environment variables. After uninstall the plugin stops running, but the skills it wrote and its state stay on disk and are cleaned up separately.
How to disable. Remove the plugin with claude plugin uninstall autoharness@autoharness and claude plugin marketplace remove autoharness. To clear its data, delete the state directory ~/.claude/autoharness/ or its per-project copy and the plugin's self-authored skills, marked as self-authored, in .claude/skills/.
FAQ
Will it touch my skills?
No. The plugin touches only the skills it created itself, marked as self-authored. Your own and externally installed skills stay untouched.
Do I need to invoke anything manually?
No, learning runs in the background after enough tool calls. The /learn command is only for locking in a lesson from the current session right away.
Discussion
No questions or reviews yet.
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Comparison with similar tools
Cards that share the main search query, and how they differ.
| Tool | Official | Repository stars | Main difference |
|---|---|---|---|
| AutoHarness (this card) | no | 11,041 | |
| Better Harness | no | 2,455 | the /better-harness command reviews how an AI agent's work is set up |
| Autocontext | no | 1,327 | autocontext runs an agent task through evaluation and keeps traces |
README in short
The README presents autoharness as a self-learning skill layer for Claude Code that distills skills from real sessions, merges same-scenario ones, updates them in use and prunes the unused. The plugin runs entirely on Python with no third-party dependencies, installs through the marketplace with /plugin commands and needs python3 on PATH. Learning fires by tool-call count, the /learn command distills a lesson on demand, and each session opens with an index of its own skills. Configuration is via AUTOHARNESS_* variables, and every decision is logged to a ledger. MIT license.