Agent skill · alirezarezvani
skill-doctor
Use when the user wants their agent setup graded from real conversation history, asks which installed skills are actually working, or wants evidence-backed skill edits — scores recent local Claude Code / Codex sessions against efficiency and code-quality rubrics, then drafts skill changes gated by a deterministic aggregator and renders one local shareable report.
What it needs
About 3k tokens when loaded.
What this skill does
skill-doctor — grade the agent setup from real sessions Privacy is the contract. Everything runs locally. Transcripts are condensed, secret-redacted, chmod-0600, and never uploaded — the only shareable artifact is the report the user chooses to share. Run from the repo being graded. Every artifact goes to one fresh scratch dir, never into the repo: 1 — Collect. Scans Claude Code project-history JSONL and Codex rollouts, discovers repo skills (.claude/skills, .agents/skills, .codex/skills, plugin layouts), detects skill usage (Skill invocations, slash commands, SKILL.md paths), samples newest-first, and writes redacted transcripts. Read $RUN/inventory.json: if sessionssampled is 0, tell the user there is nothing recent to score (suggest --days 90 or --repo) and stop. skillsfound 0 is fine — the report becomes a case for creating skills. 2 — Score. python scripts/scoreaggregator.py --inventory "$RUN/inventory.json" --emit-template > "$RUN/sessionscores.json". Read each transcript in $RUN/transcripts/ and judge it against both rubrics — scorers/efficiency.md and scorers/code-quality.md. Fill the template with a label from the rubric's table and a 1–3 sentence reason citing transcript specifics. Never invent numeric scores — the aggregator derives them from labels. Use insufficientevidence when a transcript shows no judgeable diff. Also write 1–5 topfindings: the most impactful cross-session patterns, concrete and specific. 3 — Draft edits. Follow references/skilleditgovernance.md (the filing bar: would a competent agent with the current instructions still fail this way?). For each suggestion that clears it, write the full improved SKILL.md to $RUN/proposed/<skill>/SKILL.md, produce diff -u <current> <proposed>, and record it in $RUN/suggestions.json citing the sampled session id(s) that motivated it. Zero suggestions is a valid success — say why per finding. Never modify the user's real skill files in this step. 4 — Aggregate (the gate). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/skill-doctor--f8e554