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name: total-review description: 'Run Fable and GPT reviews, then combine their findings for the user''s approval. Use only when the user explicitly invokes /total-review.' disable-model-invocation: true

Total Review

Workflow

1. Launch both reviewers in parallel as two review threads. Follow the relevant harness guidance and each reviewer skill:

   • One fable-review worker (Fable 5 Max 1M).
   • One gpt-review worker (GPT 5.6 Sol Max).
   • Follow each reviewer skill exactly: neutral prompt, broad review scope, detailed findings on critical/serious issues, concise plain-English final report.
   • Use the same files and context for both reviewers.
   • If the user names another harness (Cursor Task, cmux, Codex CLI, etc.), use that for both instead.

2. Wait for both to finish. Do not start triage until both reports are back.

3. Merge and triage. Read both reports in full. Then:

   • Combine and deduplicate findings; an issue reported by both counts once.
   • Assess each finding: is it a real bug or risk, or a style preference, theoretical edge case, or non-issue? Agreement alone does not prove an issue is real.
   • Keep only issues that matter.

4. Output to the user — clear and very concise:

   • A numbered list of real issues, one line each: what it is + where.
   • Mark which reviewer(s) found each: [both], [fable], or [gpt]. Issues found by both go first.
   • One short line at the end: how many findings were dropped as overthinking.
   • Then ask the user: approve fixing these, or adjust the list.

5. On approval, fix and ship. Fix only approved issues. Then follow the repository's contribution process.

Rules

• Keep every step's output short and in plain English.
• Show the merged shortlist by default; provide full reviewer reports only if the user asks.
• Never fix an issue before the user approves the shortlist.