improve-skills
Skill10 starsChanged 19 days agoMITView on GitHub
Fold one lesson into one existing skill, behind a confirmation gate.
4 other projects use a version of this.
Adopted by
Most recently taken up by
- jmusick/skillstheir own version, first seen Nov 8, 2026
- lautioliver/barrilete-kittheir own version, first seen Nov 1, 2026
- adhyayanrathi01/product-manager-ostheir own version, first seen Sep 6, 2026
- zrosenfield/sharepoint-ai-skillstheir own version, first seen Aug 4, 2026
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- zrosenfield/sharepoint-ai-skills★ 96same name
- lautioliver/barrilete-kit★ 6same name
- adhyayanrathi01/product-manager-os★ 2same name
- jmusick/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository25 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agents-md-improverSkill
- ai-tutorialSkill
- deassembleSkill
- excalidraw-diagramSkill
- find-overlapping-skillsSkill
- first-principlesSkill
- humanizerSkill
- local-artifactSkill
- loom-transcriptSkill
- manage-contextSkill
- organize-skill-mcpSkill
- pair-consultSkill
- pair-optimizeSkill
- pair-ratchetSkill
- pr-classifySkill
- pr-reportSkill
- scan-sessions-and-learnSkill
- simplify-pythonSkill
- skill-flowSkill
- slack-commSkill
- socratesSkill
- step-by-stepSkill
- sub-consultSkill
- to-dbmlSkill
The file
Files
improve-skills
A skill should absorb what you learn while using it. Mid-session you hit friction, get corrected, or find a better way — each is a lesson the skill should have known and didn't. This turns one lesson into one skill edit, behind a gate: nothing is written until you approve the proposal.
One skill per run. The single highest-value change, made well and confirmed, beats a batch of half-considered edits — and it keeps the confirmation honest. Targets are the skills under ~/personal/skills; this skill edits existing skills, it does not create new ones.
Step 1 — Collect lessons
Gather candidate improvements from two sources:
- Session — scan what happened this session for the moments a skill fell short: a correction you gave, friction the agent pushed through, a workaround it discovered, a mistake a skill should have prevented. Each is a candidate lesson.
- Direct — if the invocation named a skill, a specific change, or a handoff file and finding (
<file> F3, written by skill-flow or scan-sessions-and-learn), that is the lesson; take it as given (still confirm the target in Step 2).
For each lesson capture three things: (a) what was learned, (b) which repo skill it belongs in, (c) why that skill is currently silent or wrong about it. A lesson with no home skill is out of scope — note it as "no target, maybe a new skill" and set it aside.
Completion: a list of candidate lessons, each tied to exactly one existing skill in the repo with a one-line rationale — or an explicit "no actionable lesson found" when the session surfaced none.
Step 2 — Choose the one skill
If the lessons cluster on a single skill, that is the target. If they span several, present the shortlist (skill · the lesson · why it matters) and ask the user to pick one, recommending the highest-value. Then read the whole target SKILL.md, plus any reference file the change would touch, so the edit fits its structure and voice.
Completion: exactly one target skill chosen and its SKILL.md (and any affected reference file) fully read.
Step 3 — Draft the proposal
Load the writing-for-agents skill (mattpocock-skills), then design the concrete edit. In particular:
- Place it right on the information hierarchy — inline step/reference vs. a disclosed file — so the top doesn't bloat.
- Single source of truth — change the meaning in one place; if it shifts what the skill triggers on, update the description too.
- No duplication, no no-ops — prefer strengthening or introducing a leading word over restating something the skill already says or the model already does.
- Minimal and in-voice — the smallest change that lands the lesson, in the skill's existing tone. Don't pad the skill: every added line costs attention on every run, so prefer rewording or replacing existing lines, and add new content only when the lesson can't land otherwise.
Completion: a written proposal a reader could approve or amend without seeing the file — naming the target skill, the exact change (before→after or added/removed lines) and where it goes, and the lesson driving it.
Step 4 — The gate
Show the proposal. Ask the user to approve, amend, or reject. Fold feedback back into the Step 3 proposal and re-present until they approve. Write nothing before approval. On outright rejection, stop and report.
Completion: explicit user approval of a specific proposal, or a clean stop on rejection.
Step 5 — Apply
Make exactly the approved edit — no scope creep to other files or to other improvements you happen to notice. Bump the skill's version if it carries one and the change is substantive.
Completion: the approved change is written to the one target skill; nothing else is modified.
Step 6 — Verify & report
Re-read the edited region: confirm it reads cleanly, added no duplication or sprawl, and left the skill internally consistent — description matches body, links resolve. Report which skill changed, what changed, the lesson behind it, and any leftover lessons parked for a future run.
Completion: edited region verified consistent; report delivered, listing the change and any deferred lessons.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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.

