self-improve
Capture durable user feedback into the governing skill/doc, 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 covers or
Install
npx skills add https://github.com/eai-org/agent-toolkit/tree/main/skills/self-improve
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install eai-org-agent-toolkit@llmmart
git clone https://github.com/eai-org/agent-toolkit.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole eai-org/agent-toolkit collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Self-improve
Suggest durable improvements to the skill or governing doc 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-improveto deliberately improve a skill or doc. - Self-triggered — the agent notices it was corrected on something a skill/doc 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 hands over content that is already durable guidance plus an already-chosen target (scope, form, and path — possibly a new file). It resolved both with the user, so skip steps 1-2 and run only draft + apply.
"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.
Hard rules
- Confirm before applying. The skill's job is to suggest, never to change text on its own. Never edit a skill or doc 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.mdgoes 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.
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
- 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").
- Locate the target. 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 propose mechanical enforcement (e.g. a lint rule). If none fits, propose a new target and ask before drafting: a new skill for a recurring workflow, a new rule for a standing constraint, or the most fitting doc otherwise. Ask whenever unsure. - 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.
- Apply the edit. Route the write per the Hard rules —
SKILL.md→ compact-skill-creator, any other doc → compact-docs-writer — presenting a diff and applying only on approval.
Files (agent-toolkit)
-
SKILL.md 4.9 KB
--- name: self-improve description: Capture durable user feedback into the governing skill/doc, 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 covers or should cover, and when manually invoked to improve or create guidance. allowed-tools: Read, Write, Edit, Glob, Grep license: MIT metadata: version: "1.10" --- # Self-improve **Suggest** durable improvements to the skill or governing doc 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 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 hands over content that is already durable guidance plus an already-chosen target (scope, form, and path — possibly a new file). It resolved both with the user, so skip steps 1-2 and run only draft + apply. "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. ## Hard rules - **Confirm before applying.** The skill's job is to **suggest**, never to change text on its own. Never edit a skill or doc 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](../compact-skill-creator/SKILL.md); any other doc (rule, `AGENTS.md`/`CLAUDE.md`, convention doc) goes through [compact-docs-writer](../compact-docs-writer/SKILL.md). 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. ## 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.** 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 propose mechanical enforcement (e.g. a lint rule). If none fits, propose a new target and ask before drafting: a new skill for a recurring workflow, a new rule for a standing constraint, or the most fitting doc otherwise. Ask whenever unsure. 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. 4. **Apply the edit.** Route the write per the Hard rules — `SKILL.md` → compact-skill-creator, any other doc → compact-docs-writer — presenting a diff and applying only on approval.
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