Audit existing skills through a formal Tessl review or recent-task experiential feedback. Report recommendations for audit-only requests; make the smallest skill changes only when improvement or revision is explicitly requested.
95
98%
Does it follow best practices?
Impact
86%
1.01xAverage score across 3 eval scenarios
Passed
No known issues
Audit a skill before calling it ready. Favor Tessl output, repo conventions, and the skill's actual file shape over taste.
name and description before polishing the bodySKILL.md lean; move depth into references/ or scripts only when they earn their keepAGENTS.md, CLAUDE.md, or repo rules, when presentSKILL.md first, then nearby references/, scripts/, and agents/openai.yaml only as neededFor formal audit commands, batch behavior, and the explicitly requested optimizer path, follow references/tessl.md. Never let a fallback silently resolve the latest Tessl CLI.
For a formal audit, capture the Tessl score, summary, and concrete suggestions before proposing edits. Prefer per-skill --json for a narrow or structured audit loop. If Tessl is missing, install the repo's audited version or follow the official docs before continuing.
For experiential feedback:
name or descriptionUse references/scorecard.md to check:
name is specific and memorabledescription states what the skill does, when to use it, and its main boundaryQuick example:
helper — "Helps with skills"skill-audit — "Audits existing skills with Tessl scoring, metadata checks, and repo conventions"Check that the skill tells the agent how to start, what evidence to gather, what not to change, and what "done" looks like.
Use the scorecard's major findings as the detailed rubric. Treat unrunnable commands or paths, missing output gates, and vague or fragile workflows as blockers.
Check whether detail belongs in SKILL.md, references/, or executable scripts:
SKILL.mdreferences/Use references/best-practices.md when the skill feels bloated, under-specified, or hard to trigger.
Check for repo-relative links, stale paths, duplicated guidance, and conflicts with the source repo's conventions.
Treat agents/openai.yaml as picker-facing metadata: keep interface.default_prompt to one scope-aligned sentence. Do not invent undocumented Codex limits; use the real loader or a shared versioned linter when deterministic enforcement is necessary.
Require package independence across frontmatter, picker metadata, bodies, references, scripts, and evals. A skill may state prerequisites and boundaries, but it must explain them locally instead of naming or requiring sibling skills.
Separate blockers from polish. If edits are requested, fix the highest-leverage issues first, rerun Tessl, and report what improved.
After an audit, report a compact audit footer:
noneKeep details compact: