Total Review

SkillAI & models

Runs two AI model reviews of your code in parallel, merges their findings into one shortlist, and fixes them after your approval.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Total Review skill

About this skill

Run Fable and GPT reviews, then combine their findings for the user''s approval. Use only when the user explicitly invokes /total-review.

What this skill tells your AI

The instructions your AI receives, as published by davidondrej/skills in skills/agent-orchestration/total-review/SKILL.md and read by ahel’s review.

Workflow

  1. Launch both reviewers in parallel as two bb threads (default). Follow /nagent, /bb-cli, 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.
    • Reuse this thread's environment so both see the same files.
    • If the user names another harness (Cursor Task, cmux, Codex CLI, etc.), use that for both instead.
  2. Wait for both to finish (bb thread wait on each, unless another harness was requested). 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 stage, commit with a clear message, and push to GitHub using the standard ship workflow.

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.

Signals

GitHub stars
4k
Forks
598
Last commit
Sep 2026
Advanced
Item type
skill
Key
total-review
Source
github.com/davidondrej/skills