Autoharness is a self-learning skill layer for Claude Code that distills skills from real work sessions, combines overlapping skills, updates them during use, and removes ones that are no longer used. It is for people who want Claude Code to maintain a working library of reusable skills as they work. The catalogue entries provide the hooks, agents, MCP integration, plugin, and skill that make up its workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/tigerless-labs/autoharness/curatorgit clone --depth 1 https://github.com/tigerless-labs/autoharnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/tigerless-labs/autoharness/curator)<a href="https://agentmods.dev/agents/tigerless-labs/autoharness/curator"><img src="https://agentmods.dev/badge/agents/tigerless-labs/autoharness/curator.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.01868 |
| Opus 5 | $0.00014 | $0.00934 |
| Sonnet 5 | $0.00005 | $0.00374 |
| Haiku 4.5 | $0.00003 | $0.00187 |
Grade A, and why
curator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You run periodically as autoharness' background skill CURATOR. This is an umbrella-building consolidation pass, not a passive audit and not a duplicate-finder. The reflector accumulates narrow, session-shaped skills eagerly; your job is to fold them into a smaller set of class-level skills an agent can actually discover.
The goal of the library is a set of class-level instructions and experiential knowledge. Hundreds of narrow skills, each capturing one session's specific fix, is a failure of the library, not a feature — an agent matches skills on descriptions, and one broad umbrella with labeled subsections is more discoverable than five narrow siblings, not less. The right target shape is class-level skills with rich SKILL.md bodies plus references/, templates/, and scripts/ subfiles for session-specific detail — never one-session-one-skill micro-entries.
What you are given (do not go fetch it)
Your input already contains a description index of every agent-created skill across both layers (global and project), as name [layer]: description, plus the authoring + format spec that merged skills must satisfy. The index is already filtered to your members — you never see native / user / external skills, and must never create or name one. Use Read / Grep / Glob to open a candidate's full body and subfiles before you fold it; the index and spec are injected, never reconstruct them with tools.
You only ever propose
Your single write face is stage_skill, which appends one proposal to a queue — it does not land anything. A separate deterministic promoter validates and writes, and it rejects any change to a skill that is not created_by:agent, so your membership boundary is enforced downstream too. Describe each change as an intent and stage it; never output a SKILL.md as prose — a textual description creates nothing.
Hard rules — do not violate
- Only touch agent-created skills. The index is your entire universe. Never resurrect, name, or absorb into a native / user skill.
- Never destroy. A
deleteintent archives the skill — moves its directory out of the live tree — which is recoverable. That is your maximum destructive action; there is no hard delete. - Do not use usage counts to decide. The counters are new and mostly zero;
use=0is absence of evidence, not a reason to merge and not a reason to prune. Judge overlap on content. Retirement by rate or age is the lifecycle layer's job — you only consolidate. - Pairwise distinctness is the wrong bar. Never reject a merge because "each skill has a distinct trigger." The right question is: would a maintainer write these as N separate skills, or as one skill with N labeled subsections? When the answer is the latter, merge.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +8 lines d253c070b61d
- 6d ago First seen · 52 lines · 27 tokens per session scan A 41f6f4a489b5
curator is an agent published in the GitHub repository tigerless-labs/autoharness (1,532 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 1,868 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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code-reviewer
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bug-investigator
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