skill-doctor

# 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:

```bash
RUN="$(mktemp -d "${TMPDIR:-/tmp}/skill-doctor-XXXXXXXX")"
python scripts/collect_sessions.py --out "$RUN"          # 1 — harvest + redact
```

**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
`sessions_sampled` is 0, tell the user there is nothing recent to score (suggest
`--days 90` or `--repo`) and stop. `skills_found` 0 is fine — the report becomes a
case for creating skills.

**2 — Score.** `python scripts/score_aggregator.py --inventory "$RUN/inventory.json"
--emit-template > "$RUN/session_scores.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 `insufficient_evidence` when
a transcript shows no judgeable diff. Also write 1–5 `top_findings`: the most
impactful cross-session patterns, concrete and specific.

**3 — Draft edits.** Follow `references/skill_edit_governance.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).** `python scripts/score_aggregator.py --inventory
"$RUN/inventory.json" --scores "$RUN/session_scores.json" --suggestions
"$RUN/suggestions.json"`. It validates labels against the rubric tables, refuses
scores for unsampled sessions, requires substantive reasons, rejects suggestions
that cite no scored session, computes `overall = 0.5·efficiency +
0.35·code_quality + 0.15·skill_coverage`, and writes `report.json`. **Exit 4 is a
stop**: fix what it names and re-run; never hand-edit report.json around it.

**5 — Render + tell.** `python scripts/render_report.py --report "$RUN/report.json"`
→ one self-contained `report.html` (no JS, no CDN, dark-mode + print-to-PDF). Then
tell the user the grade and the top findings in text, link
`file://$RUN/report.html`, and ask whether to apply the proposed diffs to their
real skills — apply only on an explicit yes, skill by skill.

## Hard rules

1. **Never upload transcripts, session files, or any excerpt.** Local only.
2. **Labels only, from the rubric tables.** The aggregator owns all arithmetic.
3. **Every suggestion traces to a scored session** — or it is dropped. Generic best practice is not evidence.
4. **Zero suggestions is a success**, not a failure to report around.
5. **Exit 4 from the aggregator is a stop**, not an error to swallow or bypass.
6. **Never touch the user's real skill files** without an explicit per-skill yes; proposed edits live under `$RUN/proposed/`.
7. **A proposed skill edit follows write-a-skill discipline** — trigger phrase in the description, smallest change that expresses the rule, replace over append.

## Scripts

| Script | Role | Exit codes |
|---|---|---|
| `scripts/collect_sessions.py` | Harvest Claude Code + Codex sessions, redact secrets, sample, inventory | 0 · 3 bad input |
| `scripts/score_aggregator.py` | Validate labels/reasons/suggestions, compute grade, emit report.json | 0 · 2 warnings · 3 bad input · 4 validation failure |
| `scripts/render_report.py` | report.json → single self-contained report.html | 0 · 3 bad input |

All support `--help`, `--output json`, and `--sample` (no real history needed).

## References and assets

- [`scorers/efficiency.md`](scorers/efficiency.md) · [`scorers/code-quality.md`](scorers/code-quality.md) — the two rubrics, preserved verbatim from upstream
- [`references/transcript_scoring_canon.md`](references/transcript_scoring_canon.md) — why rubric-anchored LLM judging works and where it fails (7 sources)
- [`references/session_mining_privacy.md`](references/session_mining_privacy.md) — the local-only contract, redaction pattern canon (7 sources)
- [`references/skill_edit_governance.md`](references/skill_edit_governance.md) — the filing bar for proposing skill edits (7 sources)
- [`assets/session_scores.example.json`](assets/session_scores.example.json) · [`assets/suggestions.example.json`](assets/suggestions.example.json) · [`assets/report.example.json`](assets/report.example.json) — the three handoff shapes

[optional: --repo PATH, --days N, or a question about the setup]

{
  "format": "aiskill/v1",
  "skill": {
    "name": "skill-doctor",
    "slug": "skill-doctor",
    "version": "1.0.0",
    "description": "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.",
    "category": "Development",
    "tags": [
      "ai-agents",
      "agents"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "# skill-doctor — grade the agent setup from real sessions\r\n\r\n> **Privacy is the contract.** Everything runs locally. Transcripts are condensed,\r\n> secret-redacted, chmod-0600, and never uploaded — the only shareable artifact is\r\n> the report the user chooses to share.\r\n\r\nRun from the repo being graded. Every artifact goes to one fresh scratch dir, never\r\ninto the repo:\r\n\r\n```bash\r\nRUN=\"$(mktemp -d \"${TMPDIR:-/tmp}/skill-doctor-XXXXXXXX\")\"\r\npython scripts/collect_sessions.py --out \"$RUN\"          # 1 — harvest + redact\r\n```\r\n\r\n**1 — Collect.** Scans Claude Code project-history JSONL and Codex rollouts,\r\ndiscovers repo skills (`.claude/skills`, `.agents/skills`, `.codex/skills`, plugin\r\nlayouts), detects skill usage (`Skill` invocations, slash commands, SKILL.md paths), samples\r\nnewest-first, and writes redacted transcripts. Read `$RUN/inventory.json`: if\r\n`sessions_sampled` is 0, tell the user there is nothing recent to score (suggest\r\n`--days 90` or `--repo`) and stop. `skills_found` 0 is fine — the report becomes a\r\ncase for creating skills.\r\n\r\n**2 — Score.** `python scripts/score_aggregator.py --inventory \"$RUN/inventory.json\"\r\n--emit-template > \"$RUN/session_scores.json\"`. Read each transcript in\r\n`$RUN/transcripts/` and judge it against **both** rubrics — `scorers/efficiency.md`\r\nand `scorers/code-quality.md`. Fill the template with a **label from the rubric's\r\ntable** and a 1–3 sentence reason citing transcript specifics. Never invent numeric\r\nscores — the aggregator derives them from labels. Use `insufficient_evidence` when\r\na transcript shows no judgeable diff. Also write 1–5 `top_findings`: the most\r\nimpactful cross-session patterns, concrete and specific.\r\n\r\n**3 — Draft edits.** Follow `references/skill_edit_governance.md` (the filing bar:\r\nwould a competent agent with the current instructions still fail this way?). For\r\neach suggestion that clears it, write the full improved SKILL.md to\r\n`$RUN/proposed/<skill>/SKILL.md`, produce `diff -u <current> <proposed>`, and record\r\nit in `$RUN/suggestions.json` citing the sampled session id(s) that motivated it.\r\nZero suggestions is a valid success — say why per finding. Never modify the user's\r\nreal skill files in this step.\r\n\r\n**4 — Aggregate (the gate).** `python scripts/score_aggregator.py --inventory\r\n\"$RUN/inventory.json\" --scores \"$RUN/session_scores.json\" --suggestions\r\n\"$RUN/suggestions.json\"`. It validates labels against the rubric tables, refuses\r\nscores for unsampled sessions, requires substantive reasons, rejects suggestions\r\nthat cite no scored session, computes `overall = 0.5·efficiency +\r\n0.35·code_quality + 0.15·skill_coverage`, and writes `report.json`. **Exit 4 is a\r\nstop**: fix what it names and re-run; never hand-edit report.json around it.\r\n\r\n**5 — Render + tell.** `python scripts/render_report.py --report \"$RUN/report.json\"`\r\n→ one self-contained `report.html` (no JS, no CDN, dark-mode + print-to-PDF). Then\r\ntell the user the grade and the top findings in text, link\r\n`file://$RUN/report.html`, and ask whether to apply the proposed diffs to their\r\nreal skills — apply only on an explicit yes, skill by skill.\r\n\r\n## Hard rules\r\n\r\n1. **Never upload transcripts, session files, or any excerpt.** Local only.\r\n2. **Labels only, from the rubric tables.** The aggregator owns all arithmetic.\r\n3. **Every suggestion traces to a scored session** — or it is dropped. Generic best practice is not evidence.\r\n4. **Zero suggestions is a success**, not a failure to report around.\r\n5. **Exit 4 from the aggregator is a stop**, not an error to swallow or bypass.\r\n6. **Never touch the user's real skill files** without an explicit per-skill yes; proposed edits live under `$RUN/proposed/`.\r\n7. **A proposed skill edit follows write-a-skill discipline** — trigger phrase in the description, smallest change that expresses the rule, replace over append.\r\n\r\n## Scripts\r\n\r\n| Script | Role | Exit codes |\r\n|---|---|---|\r\n| `scripts/collect_sessions.py` | Harvest Claude Code + Codex sessions, redact secrets, sample, inventory | 0 · 3 bad input |\r\n| `scripts/score_aggregator.py` | Validate labels/reasons/suggestions, compute grade, emit report.json | 0 · 2 warnings · 3 bad input · 4 validation failure |\r\n| `scripts/render_report.py` | report.json → single self-contained report.html | 0 · 3 bad input |\r\n\r\nAll support `--help`, `--output json`, and `--sample` (no real history needed).\r\n\r\n## References and assets\r\n\r\n- [`scorers/efficiency.md`](scorers/efficiency.md) · [`scorers/code-quality.md`](scorers/code-quality.md) — the two rubrics, preserved verbatim from upstream\r\n- [`references/transcript_scoring_canon.md`](references/transcript_scoring_canon.md) — why rubric-anchored LLM judging works and where it fails (7 sources)\r\n- [`references/session_mining_privacy.md`](references/session_mining_privacy.md) — the local-only contract, redaction pattern canon (7 sources)\r\n- [`references/skill_edit_governance.md`](references/skill_edit_governance.md) — the filing bar for proposing skill edits (7 sources)\r\n- [`assets/session_scores.example.json`](assets/session_scores.example.json) · [`assets/suggestions.example.json`](assets/suggestions.example.json) · [`assets/report.example.json`](assets/report.example.json) — the three handoff shapes",
    "user_template": "[optional: --repo PATH, --days N, or a question about the setup]"
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The skill-doctor/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/skill-doctor",
    "exported_at": "2026-11-03T07:44:33.980Z"
  }
}

format: aiskill/v1
skill:
  name: skill-doctor
  slug: skill-doctor
  version: 1.0.0
  description: >-
    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.
  category: Development
  tags:
    - ai-agents
    - agents
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# skill-doctor — grade the agent setup from real sessions\r\n\r\n> **Privacy is the contract.** Everything runs locally. Transcripts are condensed,\r\n> secret-redacted, chmod-0600, and never uploaded — the only shareable artifact is\r\n> the report the user chooses to share.\r\n\r\nRun from the repo being graded. Every artifact goes to one fresh scratch dir, never\r\ninto the repo:\r\n\r\n```bash\r\nRUN=\"$(mktemp -d \"${TMPDIR:-/tmp}/skill-doctor-XXXXXXXX\")\"\r\npython scripts/collect_sessions.py --out \"$RUN\"          # 1 — harvest + redact\r\n```\r\n\r\n**1 — Collect.** Scans Claude Code project-history JSONL and Codex rollouts,\r\ndiscovers repo skills (`.claude/skills`, `.agents/skills`, `.codex/skills`, plugin\r\nlayouts), detects skill usage (`Skill` invocations, slash commands, SKILL.md paths), samples\r\nnewest-first, and writes redacted transcripts. Read `$RUN/inventory.json`: if\r\n`sessions_sampled` is 0, tell the user there is nothing recent to score (suggest\r\n`--days 90` or `--repo`) and stop. `skills_found` 0 is fine — the report becomes a\r\ncase for creating skills.\r\n\r\n**2 — Score.** `python scripts/score_aggregator.py --inventory \"$RUN/inventory.json\"\r\n--emit-template > \"$RUN/session_scores.json\"`. Read each transcript in\r\n`$RUN/transcripts/` and judge it against **both** rubrics — `scorers/efficiency.md`\r\nand `scorers/code-quality.md`. Fill the template with a **label from the rubric's\r\ntable** and a 1–3 sentence reason citing transcript specifics. Never invent numeric\r\nscores — the aggregator derives them from labels. Use `insufficient_evidence` when\r\na transcript shows no judgeable diff. Also write 1–5 `top_findings`: the most\r\nimpactful cross-session patterns, concrete and specific.\r\n\r\n**3 — Draft edits.** Follow `references/skill_edit_governance.md` (the filing bar:\r\nwould a competent agent with the current instructions still fail this way?). For\r\neach suggestion that clears it, write the full improved SKILL.md to\r\n`$RUN/proposed/<skill>/SKILL.md`, produce `diff -u <current> <proposed>`, and record\r\nit in `$RUN/suggestions.json` citing the sampled session id(s) that motivated it.\r\nZero suggestions is a valid success — say why per finding. Never modify the user's\r\nreal skill files in this step.\r\n\r\n**4 — Aggregate (the gate).** `python scripts/score_aggregator.py --inventory\r\n\"$RUN/inventory.json\" --scores \"$RUN/session_scores.json\" --suggestions\r\n\"$RUN/suggestions.json\"`. It validates labels against the rubric tables, refuses\r\nscores for unsampled sessions, requires substantive reasons, rejects suggestions\r\nthat cite no scored session, computes `overall = 0.5·efficiency +\r\n0.35·code_quality + 0.15·skill_coverage`, and writes `report.json`. **Exit 4 is a\r\nstop**: fix what it names and re-run; never hand-edit report.json around it.\r\n\r\n**5 — Render + tell.** `python scripts/render_report.py --report \"$RUN/report.json\"`\r\n→ one self-contained `report.html` (no JS, no CDN, dark-mode + print-to-PDF). Then\r\ntell the user the grade and the top findings in text, link\r\n`file://$RUN/report.html`, and ask whether to apply the proposed diffs to their\r\nreal skills — apply only on an explicit yes, skill by skill.\r\n\r\n## Hard rules\r\n\r\n1. **Never upload transcripts, session files, or any excerpt.** Local only.\r\n2. **Labels only, from the rubric tables.** The aggregator owns all arithmetic.\r\n3. **Every suggestion traces to a scored session** — or it is dropped. Generic best practice is not evidence.\r\n4. **Zero suggestions is a success**, not a failure to report around.\r\n5. **Exit 4 from the aggregator is a stop**, not an error to swallow or bypass.\r\n6. **Never touch the user's real skill files** without an explicit per-skill yes; proposed edits live under `$RUN/proposed/`.\r\n7. **A proposed skill edit follows write-a-skill discipline** — trigger phrase in the description, smallest change that expresses the rule, replace over append.\r\n\r\n## Scripts\r\n\r\n| Script | Role | Exit codes |\r\n|---|---|---|\r\n| `scripts/collect_sessions.py` | Harvest Claude Code + Codex sessions, redact secrets, sample, inventory | 0 · 3 bad input |\r\n| `scripts/score_aggregator.py` | Validate labels/reasons/suggestions, compute grade, emit report.json | 0 · 2 warnings · 3 bad input · 4 validation failure |\r\n| `scripts/render_report.py` | report.json → single self-contained report.html | 0 · 3 bad input |\r\n\r\nAll support `--help`, `--output json`, and `--sample` (no real history needed).\r\n\r\n## References and assets\r\n\r\n- [`scorers/efficiency.md`](scorers/efficiency.md) · [`scorers/code-quality.md`](scorers/code-quality.md) — the two rubrics, preserved verbatim from upstream\r\n- [`references/transcript_scoring_canon.md`](references/transcript_scoring_canon.md) — why rubric-anchored LLM judging works and where it fails (7 sources)\r\n- [`references/session_mining_privacy.md`](references/session_mining_privacy.md) — the local-only contract, redaction pattern canon (7 sources)\r\n- [`references/skill_edit_governance.md`](references/skill_edit_governance.md) — the filing bar for proposing skill edits (7 sources)\r\n- [`assets/session_scores.example.json`](assets/session_scores.example.json) · [`assets/suggestions.example.json`](assets/suggestions.example.json) · [`assets/report.example.json`](assets/report.example.json) — the three handoff shapes"
  user_template: "[optional: --repo PATH, --days N, or a question about the setup]"
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The skill-doctor/ folder contains
        SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/skill-doctor
  exported_at: "2026-11-03T07:44:33.980Z"