AutoHarness
Self-Learning Skills for Claude Code
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. |
| 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. |
| Validated in use, not on a benchmark | A skill survives by being adhered to in later turns (usage rate), 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. |
Install
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.
Update
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.
Uninstall
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.
Configuration
Every knob is an AUTOHARNESS_* environment variable with a built-in default — nothing to
configure unless you want to change the pace.
| Variable | Default | What it does |
|---|---|---|
AUTOHARNESS_REFLECT_EVERY_N |
10 |
Reflection cadence: a background reflection run fires every N host turns, and each run receives that full N-turn window. Lower = learns faster, spawns more child sessions. |
AUTOHARNESS_DIGEST_EXCHANGES |
20 |
How many exchanges before the episode window are compressed into the reflector's prior-context digest (text + tool names only). |
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 — never used across the whole probation → archived; used at least once → graduates into the mature pool. 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. 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. |
Set them in the environment Claude Code launches with — either the shell
(export AUTOHARNESS_REFLECT_EVERY_N=3) or the env map in .claude/settings.json:
{ "env": { "AUTOHARNESS_REFLECT_EVERY_N": "3" } }
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/); size caps
on captured windows and staged skill bodies are fixed constants, not env knobs.
How it works
A learning pipeline runs beside the host and stays off its recall path — symbols are plain native skills, recalled by the host's own name-and-description mechanism as if a human had written them.
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. |
| REF · reflect | At an episode boundary, receives the current episode window in full detail (the last N turns, tool I/O included) plus a compressed digest of the exchanges before it (text and tool names only), reads the existing skill index, and decides add / merge / patch / drop a support file / delete — emits an intent (body, delta, or path, plus reason and evidence). Proposes only; no write tools. |
| 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. |
| MNG · lifecycle | Daemon-free: recomputed lazily at session start, once per session. Ranks symbols by usage rate — uses over the requests that arrived since the symbol was created, so the measure is opportunity-relative and a closed laptop doesn't age anyone out (the wall-clock replacement). A use is counted whenever the host consumes the skill: a Skill-tool invocation or a read of any file in the skill's directory (measured to be the dominant path). New symbols sit in probation until they've had a fair sample of requests: recalled as usual, but neither counted against the cap nor evictable. At maturity, graduation review: zero use across the whole probation → archived, never enters the pool. For graduates, capacity contention is the only death — nothing is archived until a layer's mature pool exceeds its cap, then the lowest rates go first. Archives, never deletes: an archived symbol is a directory moved out of recall, and moving it back revives it. |
| LED · ledger | Per-symbol append-only sidecar: why each symbol was born or changed, with evidence and a reflection watermark. Kept out of the skill body so recall stays clean. |
Walkthrough: watching it learn
Everything autoharness does lands on disk as plain files — a demo is just opening them in the right order. For a fast-paced run, speed up the loop first (see Configuration):
{ "env": { "AUTOHARNESS_REFLECT_EVERY_N": "1",
"AUTOHARNESS_MATURITY_PROJECT": "5",
"AUTOHARNESS_CAPACITY_PROJECT": "2" } }
1 · The pipeline running. Work a few normal turns on anything non-trivial (debug something, figure out a workflow). Every Nth turn a background reflection fires — nothing blocks your session. Its bookkeeping is visible in the state dir:
ls .claude/autoharness/ # per project — ~/.claude/autoharness/ for the global layer
requests # layer request counter (MNG's denominator)
session-<id> # per-session turn count toward the next reflection
offset-<id> # byte watermark: where the last captured window ended
intents/ # queued skill proposals awaiting the promoter
2 · A skill is born. After a reflection lands, a new folder appears under .claude/skills/
(project) or ~/.claude/skills/ (global — for techniques that aren't repo-specific). Use ls -la:
the interesting files are hidden.
.claude/skills/<name>/
SKILL.md # the skill itself — plain native format, nothing proprietary
.ledger.jsonl # LED: why it was born / changed (append-only)
.sidecar.json # lifecycle counters MNG reads
references/evidence-*.md # the transcript slice that justified each ledger entry
scripts/ templates/ ... # optional support files the reflector attached
3 · LED — the paper trail. cat .ledger.jsonl — one JSON line per lifecycle event:
{"action": "create", "reason": "User asked about the correct command to update a plugin ...", "evidence": "references/evidence-21cd22cc.md"}
{"action": "patch", "reason": "User discovered /reload-plugins is required in-session ...", "evidence": "references/evidence-1a4ec51d.md"}
action + reason + evidence — and the evidence file is a real, redacted slice of the session
that taught it, materialized by the promoter (content-addressed, so the model never names files).
This is the "evidence kept for later" from the table above.
4 · An update, not a duplicate. Hit the same scenario again with a correction ("that's missing
a step") and let the next reflection run. The skill layer does not grow a near-duplicate:
the existing skill's SKILL.md changes and its ledger appends a patch/update line — the
two-line ledger above is a real example. git diff on a project-layer skill shows the edit.
5 · Recall is the host's, untouched. Landed skills load like hand-written ones — same
name-and-description recall, no autoharness code on that path. When one is used — invoked as a
skill or read from its directory — calls in its .sidecar.json ticks up: that adherence count
is the validation signal.
6 · Retirement is an archive, not a delete. Two paths out, both a folder move to
.claude/skills/.archive/<name>/ — ledger, evidence and all, out of recall. A skill never used
across its whole probation is archived at graduation review; after graduation, once a layer's
mature pool exceeds capacity the lowest-usage-rate skills go. Moving the folder back revives it,
history intact. With the shrunk knobs above this fires within one session; at defaults it takes
hundreds of turns.
7 · Yours are never touched. Every autoharness-authored skill carries the ledger marker; anything without it — skills you wrote or installed — is invisible to the promoter and MNG.
How it compares
A self-learning skill layer can be validated against a held-out benchmark, or against its own use. autoharness takes the second — cheaper, and it works on a live host doing open-ended work where no benchmark exists.
| Grow unbounded | Offline-gated self-edit (Self-Harness) |
Timer + daemon (hermes-agent) |
autoharness | |
|---|---|---|---|---|
| Bounds the skill layer | No | Yes | Yes | Yes |
| Validation signal | None | Held-out benchmark score | Wall-clock inactivity | Adherence in use |
| Needs a benchmark / oracle | No | Yes | No | No |
| Needs a resident daemon | No | No | Yes | No |
Acknowledgements
NousResearch/hermes-agent — studying its auto-skill-creation and memory-consolidation design helped sharpen autoharness's adherence-based, daemon-free take.
Built by Tigerless Labs.