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.
> /plugin marketplace add tigerless-labs/autoharness> /plugin install autoharness@autoharness
What's inside
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 python3 on your PATH — autoharness runs entirely as Python (zero third-party
dependencies); its hooks and MCP server won't fire without it.
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.
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 captured windows and staged files are fixed constants, not env knobs.
A learning pipeline runs beside the host. Skills are plain native files, recalled by the host's own name-and-description mechanism as if a human had written them — that path is left untouched. On top of it, each session opens with an index of the skills autoharness wrote, so whether its own library gets offered is a property of this plugin rather than a hope about host behavior.
Diagram source: docs/assets/pipeline.mmd — re-render to pipeline.svg after editing.
| Component | Role |
|---|---|
| CAP · capture | Hook-driven dumb pipe: grabs each turn (user input, agent output, tool I/O), redacts at egress, points back at the host log instead of copying it. It also holds the trigger, which is deterministic and counts one thing — tool calls. The turn that crosses the threshold ends with a reflection; nothing about the content is judged here. |
| REF · reflect | Reads the episode, compares it against the existing skill index, and decides add / merge / patch / drop a support file / delete — emitting an intent (body, delta, or path, plus reason and evidence). Where a new lesson contradicts an older skill, it must rewrite the stale one in the same run rather than leave the library arguing with itself. Proposes only; it has no write tools, and a fork carrier's inherited ones are denied at the hook. |
| promoter · validate·store | The only writer. Lints the intent in memory (safety, structure, ledger, completeness, self-authored-only) and on pass does an atomic rename into the live skill directory. A new skill's description has to carry its trigger early enough to survive the index's truncation — a cue that lands past the cut leaves the skill as half a sentence on the very surface meant to recall it. A fold must name the skill that absorbed the deleted one, and the umbrella has to be a live skill autoharness manages — an invented name fails the whole intent rather than losing the content. Each run leaves an account of what landed and what was rejected. |
| IDX · surface | Builds the session-start index: the skills autoharness wrote, grouped by category, one truncated description per line, tagged by layer. Archived and hand-written skills are excluded, an empty library injects nothing, and the previous run's landed/rejected line rides along — so a rejected proposal is visible instead of silent. |
View @tigerless-labs’s profileFeed your agent papers and half-formed ideas — it links them into a system design you can defend. Markdown keeps the record; a visual canvas makes it readable. An Agent Skill for Claude Code & any SKILL.md-compatible agent.
@tigerless-labsFAQ
autoharness is a Claude Code plugin with 1 hand-picked skill for development work, indexed on Flowy. Install it with the command on its page. It includes learn. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
Is this plugin yours?
Claim it with GitHubSubmit a pluginPromote it