agentleFS
eai-orgeai-org/agent-toolkit/skills/self-improve/SKILL.md

self-improve

Skill85 starsChanged 21 days agoMITView on GitHub

Capture durable user feedback into the governing skill, doc or check, or propose creating a new skill when no suitable one exists, so future sessions don't repeat the mistake. Use when the user rejects, reverts, or overrides the agent's output or approach on something a skill, doc or check covers or should cover, and when manually invoked to improve or create guidance.

36 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use ecosystem-reference, fractal-delegation and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • naman10parikhSkill

    ecosystem-reference

    Quick-access directory of Claude Code ecosystem repos, skills, plugins, MCPs, and patterns. Use when improving the harness.

    Also in 5 projects that use this

  • naman10parikhSkill

    fractal-delegation

    Also in 5 projects that use this

  • aaronjmarsSkill

    aeon-update

    Pull framework updates from the upstream Aeon repo into this instance - 3-way merges canon's new commits into a PR, never clobbering operator config.

    Also in 4 projects that use this

  • aaronjmarsSkill

    auto-workflow

    Two-mode aeon.yml workflow builder - analyze inspects URLs and emits a tiered, signal-verified skill-enablement plan plus an aeon.yml diff; enable flips slugs to enabled:true and opens a PR.

    Also in 4 projects that use this

  • +6 more from naman10parikh
  • +2 more from aaronjmars

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 31 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Never edit a skill, doc or check without the user's explicit go-ahead on the concrete change — present it as a diff and apply only on …

    line 36
  2. Distinctive:

    Place by context cost: Always-loaded docs (AGENTS.md/CLAUDE.md) hold mostly pointers.

    line 80
  3. Distinctive:

    Draft the edit: Write the rule into the target as the least text that fully captures it: agent-agnostic ("the agent", never a vendor name), …

    line 82
  4. Distinctive:

    If the lesson reverses an existing rule, surface that explicitly — show the old rule, the feedback, and the proposed replacement — and …

    line 85

“Common” is measured: kept by at least 6 in 10 of the 31 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Self-improve

Suggest durable improvements to the skill, doc or check that should have steered the agent, so the next session gets it right without being told again — and apply them only after the user approves. The skill proposes; the user stays in control of every change. Three ways in:

  • Manual — the user invokes /self-improve to deliberately improve a skill or doc.
  • Self-triggered — the agent notices it was corrected on something a skill, doc or check governs (or should). Don't silently correct and move on, but don't derail the task either: note the lesson, finish what the user asked for, and offer to persist it at the next natural breakpoint.
  • Driven by another skill — a caller (or its launch command, pasted by the user) hands over lessons already phrased as durable guidance, sometimes with all or part of a target it settled with the user (scope, form, path — possibly a new file or a merge into an existing one). Skip step 1; skip step 2 for a lesson whose target the caller settled fully, run it for the rest keeping what the caller settled — its Mechanical lesson bullet and new-doc indexing requirement always apply. Several lessons at once → first propose their placement as one table, each lesson with its doc, existing or new; then draft + apply, lessons sharing a target as one diff.

"Skill/doc" means any standing instruction: a SKILL.md, AGENTS.md/CLAUDE.md, a coding-standards or convention doc, a rules file — anything that guides future agents. A "check" (lint rule, hook, CI job) enforces one mechanically and is not a skill/doc.

Hard rules

  • Confirm before applying. The skill's job is to suggest, never to change text on its own. Never edit a skill, doc or check without the user's explicit go-ahead on the concrete change — present it as a diff and apply only on approval, whether the user invoked the skill or the agent self-triggered. State plainly whether a change is not yet applied (awaiting approval) or already applied (and where), so the user never has to ask.
  • Editing any skill/doc → always route the write through one of two skills to keep it compact; never edit it directly. A SKILL.md goes through compact-skill-creator; any other doc (rule, AGENTS.md/CLAUDE.md, convention doc) goes through compact-docs-writer. Actually invoke the skill and follow its workflow before drafting or applying; reading it, applying its principles by hand, or naming it after a direct edit does not count. A check is edited directly.

Recognize a persistable correction (self-trigger)

Signals the agent was corrected in a way worth persisting: the user rejects or reverts a choice ("no, do X instead"), states a standing preference ("we always…", "never…"), or redirects an action the agent took under a skill or doc.

Only persist a durable lesson — one that generalizes and will recur. Skip one-off, task-specific tweaks that won't apply next time; persisting those pollutes the docs. Whenever unsure whether it generalizes, ask the user.

Workflow

  1. Capture the lesson. State, in one line, the general rule the feedback implies — not the surface incident ("Mock external HTTP in unit tests," not "the agent mocked the wrong call").
  2. Locate the target. Settle scope first: would the lesson hold in another project? No → a project doc, rule or wrapper, never a shared generic skill. Yes → the governing skill or doc, shared ones included, edited at its canonical source when the installed copy is a symlink or sync — the proposal says so. Find which skill/doc governs this action (search skills, AGENTS.md/CLAUDE.md, convention docs); when the repo defines a doc-placement guide (e.g. .agents/docs/README.md), follow it for locating and creating targets. If one exists, it's the target — and if that rule already existed yet failed to steer the agent, the discoverability gap is the lesson, not a no-op: don't stop at "the rule exists." Diagnose why it didn't fire (buried, in a doc the agent wouldn't open for this action, scoped or worded too narrowly, or unenforced) and fix that root cause: surface it where the agent looks, tighten its scope, cross-reference it, or enforce it mechanically. If none fits, propose a new target and ask before drafting: a check for a mechanical lesson (below), a new skill for a recurring workflow, a new rule for a standing constraint, or the most fitting doc otherwise; a new doc comes with its entry-file index line and read-when hook, or nothing ever loads it. Ask whenever unsure.
    • Mechanical lesson (a banned API, an import shape, a file location: any fixed pattern) → propose a deterministic check over prose; the check is declined, or no tooling can host it (the proposal says so) → write the rule. Read the repo's check scripts and CI first: wire or repair an existing check rather than add one.
    • Place by context cost. Always-loaded docs (AGENTS.md/CLAUDE.md) hold mostly pointers; a rule that matters only when reviewing goes in a review-time doc, not implementation context.
  3. Draft the edit. Write the rule into the target as the least text that fully captures it: agent-agnostic ("the agent", never a vendor name), no process narration, no restating — a real durable instruction. Prefer tightening or extending an existing rule over appending a new one. If the lesson reverses an existing rule, surface that explicitly — show the old rule, the feedback, and the proposed replacement — and never overwrite it silently; the contradiction may mean the feedback is context-specific, not a true reversal. A check is drafted outside the repo, with its proof: run it on a reproduction of the mistake (must fail) and on the corrected case (must pass), both results in the proposal; one that cannot run here is labelled untested.
  4. Apply the edit. Route the write per the Hard rules — SKILL.md → compact-skill-creator, any other doc → compact-docs-writer, a check → edited directly — presenting a diff and applying only on approval. Done when every handed-over or captured lesson is applied, declined by the user, or reported as already covered, each named in the closing message.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.