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Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurren
Passed 15 of 19 test cases with the skill, and 9 of 19 without it.
and 8 more in the full report
9 fixed8 passed either way3 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
After updating a local skill's files in ~/.codex/skills/git-helper, what standard Python validation command should be run if skill-creator tooling is installed?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies executing quick_validate.py on the target skill directory path. | Missed | Passed |
The instructions your model reads when the skill loads.
Maintain the user's personal local skill files as a lightweight self-improving memory system. The default goal is not to produce marketplace-ready generic skills; it is to preserve reusable behavior that helps this user, this team, or this workspace in future sessions. A good candidate may encode personal workflow preferences, project conventions, style contracts, tool choices, or review gates that would not be useful to other users, as long as it is reusable for the intended owner and approved.
This skill does not depend on project-specific code, servers, vector stores, databases, or storage layouts. It operates only on ordinary local skill folders:
<skill-root>/
<skill-name>/
SKILL.md
agents/openai.yaml (optional)
scripts/ (optional)
references/ (optional)
assets/ (optional)
Use this skill to decide when a session contains reusable skill material, find whether a similar skill already exists, then discard, improve, merge, or create a local skill. The model should initiate this check when signals appear; it should not wait for the user to ask. The check must stay lightweight and non-blocking unless the user explicitly asks to focus on skill maintenance.
This skill owns the lifecycle decision: when to extract, whether to discard, whether to improve or merge, and where to write. Coordinate with nearby skills when they are installed:
skill-creator for new skill structure, naming, resource placement, agents/openai.yaml, and validation.skill-improvement or equivalent improvement guidance for existing-skill iteration, test prompts, failure analysis, trigger-description tuning, and before/after comparison.skill-finder, find-skills, or equivalent discovery tools to search local or external skill ecosystems before creating a duplicate.Local skills can influence future agent behavior and may include executable scripts. Treat skill edits as durable behavior changes.
SKILL.md.personal by default; mark it team or public only when the evidence supports that broader audience.test cases passed, vs 9 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.