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.
# Add to your Claude Code skills
git clone https://github.com/tigerless-labs/autoharnessLast scanned: 7/3/2026
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autoharness is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by tigerless-labs. 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. It has 5,797 GitHub stars.
Yes. autoharness passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/tigerless-labs/autoharness" and add it to your Claude Code skills directory (see the Installation section above).
autoharness is primarily written in Python. It is open-source under tigerless-labs on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh autoharness against similar tools.
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This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
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). 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 build a benchmark from real usage if you ever want one. |
Requires Python 3.11+ as the python3 on your PATH — autoharness runs entirely as Python
(zero third-party dependencies); its hooks and MCP server won't fire without it. The hooks resolve
bare python3, so an older interpreter earlier on your PATH (Xcode ships 3.9.6 at
/usr/bin/python3) turns every hook off for the session; autoharness says so on stderr.
Type these in the Claude Code input box.
/plugin marketplace add tigerless-labs/autoharness
/plugin install autoharness@autoharness
Then run /reload-plugins (or restart Claude Code).
Zero config. It now watches your sessions and lands learned skills into .claude/skills/ in the
background. Cadence and lifecycle thresholds are tunable — see Configuration.
Nothing to invoke, but one entry point exists when you want it: /learn distills the session
you're in right now — say it after working something out and the lesson goes through the same
proposal-and-validation chain the background pass uses.
MCP server naming. The .mcp.json registers the server as stage_skill, but agent
definitions reference the fully-qualified name mcp__plugin_autoharness_stage_skill__stage_skill.
This translation is automatic: the plugin runtime constructs the qualified name from the plugin
name in .claude-plugin/plugin.json (autoharness) and the server key in .mcp.json.
Outside the plugin context (e.g., testing with claude directly), the tool would be available
as mcp__stage_skill__stage_skill — but the agent allowlists still reference the plugin-namespaced
form. Always install as a plugin to match both names.
Update from a terminal — refresh the catalog, then update with the full plugin@marketplace
id, then restart:
claude plugin marketplace update autoharness
claude plugin update autoharness@autoharness
Then restart Claude Code to apply — a version bump is a fresh cached copy, not a hot reload.
The refresh is first on purpose: without it, update checks a stale local catalog and may report
already at the latest version when a newer release actually shipped.
Third-party marketplaces have auto-update off by default. To make future releases hands-off,
enable it once: /plugin → Marketplaces → autoharness → Enable auto-update. The
installed copy is cached by the version in plugin.json; a release reaches users only when that
field is bumped.
claude plugin uninstall autoharness@autoharness
claude plugin marketplace remove autoharness
Uninstalling only stops it from running — the skills it landed and its own state live outside the
plugin and stay on disk. To clear those too, delete its state dir (~/.claude/autoharness/ global,
<repo>/.claude/autoharness/ per project) and the self-authored skills under .claude/skills/ (each
carries a self-authored ledger marker, so they're easy to tell from yours). Your own skills are
never touched.
Every knob is an AUTOHARNESS_* environment variable with a built-in default — nothing to
configure unless you want to change the pace.
Cadence — when it learns
| Variable | Default | What it does |
|---|---|---|
AUTOHARNESS_REFLECT_EVERY_N |
50 |
Reflection cadence, counted in tool calls, not turns: every main-session tool call advances a counter, and the turn that pushes it past N ends with a background reflection. A working stretch triggers; a conversation that only talks never does. Lower = learns faster and spawns more child sessions. |
AUTOHARNESS_CONSOLIDATE_EVERY_N |
250 |
Same quantum for the curator, the periodic pass that merges the library as a whole. Held well above the reflection cadence — consolidation is rarer than distillation. |
AUTOHARNESS_DIGEST_EXCHANGES |
20 |
How many exchanges before the episode window are compressed into the reflector's prior-context digest (text + tool names only). Unused by the fork carrier, which replays the real conversation instead. |
AUTOHARNESS_CARRIER |
bundle |
What carries the reflection. bundle hands a redacted window + digest to a fresh subagent. fork resumes and forks the session that just ended, so the reflector reads the real conversation on the parent's warm cache. Stays bundle until the cache-hit measurement is in. |
Recall — what the model sees
| Variable | Default | What it does |
|---|---|---|
AUTOHARNESS_INDEX_SUSPENDED |
0 |
Set to 1 to stop injecting the index entirely. Everything else keeps running — the lifecycle pass, the use/view counters, the last-run summary — so this is the switch for measuring what the index is actually worth, and for anyone unwilling to spend the context on it. |
AUTOHARNESS_INDEX_DESC_MAX_CHARS |
60 |
Per-line description budget in the session-start index. The index is a scan surface, not the full trigger text — raise it for longer lines, at the cost of context on every session. |
AUTOHARNESS_SKILL_DESC_MAX_CHARS |
1024 |
Hard cap on a skill's own description, matching the host's documented limit; a longer one is rejected rather than silently truncated at load. |
AUTOHARNESS_SKILL_BODY_MAX_LINES |
25 |
Altitude cap: a SKILL.md body over this many non-blank lines is rejected as a transcript rather than a rule. Backing detail belongs in the skill's references/. |
Lifecycle — what survives
| Variable | Default | What it does |
|---|---|---|
AUTOHARNESS_MATURITY_PROJECT |
100 |
Probation gate, project layer: after this many requests have arrived in its layer since a skill landed, it faces graduation review. Until then it's recalled as usual but can't be archived. |
AUTOHARNESS_MATURITY_GLOBAL |
300 |
Same gate for the global layer — higher because a global skill loads in every project. |
AUTOHARNESS_CAPACITY_PROJECT |
50 |
Cap on mature skills in the project layer. It is also what bounds the session-start index: one line per live skill, so the index can never exceed the two caps combined. For graduates, capacity contention is the only death: nothing is archived until the mature pool exceeds this, then the lowest usage rates go first. |
AUTOHARNESS_CAPACITY_GLOBAL |
20 |
Same cap for the global layer — smaller because its blast radius is every project. |
AUTOHARNESS_GRADUATION_SUSPENDED |
0 |
Set to 1 to park graduation review entirely, so nothing is archived for going unused. Meant for when you have reason to doubt the recall surface: archiving on zero use would then be punishing skills for never having been offered. Capacity contention still applies. |
AUTOHARNESS_SNAPSHOT_KEEP |
5 |
How many pre-run snapshots of each skill tree the curator keeps before merging. A merge is the one operation a single atomic rename can't undo. |
Set them in the environment Claude Code launches with — either the shell
(export AUTOHARNESS_REFLECT_EVERY_N=10) or the env map in .claude/settings.json:
{ "env": { "AUTOHARNESS_REFLECT_EVERY_N": "10" } }
Hooks read the environment on every event, so a change applies from the next session. The defaults
are deliberate placeholders pending empirical calibration (tracked under experiments/); byte caps
on ca