story: e45s28

SkillAI & models

Sends your finished code to two independent AI reviewers who give unbiased feedback before you commit.

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 story: e45s28 skill

About this skill

Dispatch a fresh reviewer agent with a clean context to critique the code after audit-code passes. The reviewer has no shared state with the coding agent and gives a genuine second opinion. Use after audit-code passes, before committing, or when user wants an independent code review.

What this skill tells your AI

The instructions your AI receives, as published by danielvm-git/bigpowers in skills/request-review/SKILL.md and read by ahel’s review.

Request Review

Dispatch fresh reviewer agents with clean contexts. Reviewers have no shared state — they find what the coding agent missed.

Distinct from audit-code: audit-code is self-review (internal). This skill dispatches external agents.

Solo developer note: Reviewer agents replace the human reviewer.

Run audit-code first. Don't waste reviewer attention on hygiene issues you could have caught yourself.

Santa Method — Dual-Blind AND Gate (e45s07)

Use two independent reviewers (Reviewer A and Reviewer B) with no shared context between them or the coding agent.

ParameterValue
Reviewers2 (mandatory)
MAX_REVIEW_ITERATIONS5 (hard cap — e45s28; iteration 6 forbidden)
Pass ruleAND-gate — both reviewers must pass independently
BlindnessNeither reviewer sees the other's report until both complete

Iteration loop (max MAX_REVIEW_ITERATIONS):

  1. Dispatch Reviewer A and Reviewer B in parallel with identical briefs but separate contexts.
  2. Collect both reports. Each categorizes findings: must-fix / should-fix / consider.
  3. AND-gate: If either reviewer has must-fix findings → FAIL round. Run respond-review, fix, re-dispatch both reviewers.
  4. If both pass (zero must-fix, score ≥ 94% each) → review complete.
  5. After 5 iterations without dual pass → stop; report "Review cap exhausted (5/5). Human decision required." Do not merge.

HARD GATE — Single-reviewer pass is insufficient. Partial agreement does not satisfy the AND-gate.

Process

1. Prepare the review brief

Write a self-contained brief for each reviewer. Include:

  • What was built (feature description, not implementation)
  • Which files changed (the diff context)
  • What specs/ artifacts are relevant (active epics/eNN-*.yaml, requirements/SCOPE_LATEST.yaml, bugs/BUG-*.md)
  • What CONVENTIONS.md requires
  • What the verify command is
  • What you're most uncertain about (where you want fresh eyes)
  • Security focus — If the epic has a specs/security/epics/<id>/THREAT_MODEL.md, include the relevant vulnerability categories as reviewer focal points. Also include the false-positive exclusion rules so the reviewer avoids known-safe patterns. Tag the review as security-sensitive: true if THREAT_MODEL risk is HIGH+.

2. Fan-out parallel reviewers (e45s17)

Beyond the mandatory dual-blind pair (e45s07), optionally dispatch N dimension-specific subagents in one message — one check per agent for broader coverage (OpenAI Codex code-review-* pattern):

AgentFocus
R-correctnessLogic, edge cases, verify command result
R-conventionsCONVENTIONS.md, test quality (F.I.R.S.T)
R-securityInjection, auth, secrets (when security-sensitive)
R-designSimpler alternatives, API shape

Santa Method still applies: each agent is blind; AND-gate uses Reviewer A + B scores. Fan-out agents feed findings into respond-review but do not replace the dual-blind pair.

2b. Dispatch both reviewer agents (parallel)

Use the Agent tool twice with completely fresh contexts. Each prompt must be self-contained — no references to "our conversation" or "what we discussed."

You are code reviewer [A|B]. Review the following code changes independently.

Context: [feature description]
CONVENTIONS.md rules: [paste relevant sections]
Active epic shard: [paste or summarize from specs/epics/]

Diff: [paste git diff or describe changed files]

Verify command: [runnable command]

Review for:
1. Correctness — does the code do what was intended?
2. CONVENTIONS.md compliance — are all rules followed?
3. Test quality — do tests verify behavior (not implementation)?
4. Design — are there simpler or more robust approaches?
5. Edge cases — what inputs or states could cause failures?
6. Security — any injection, auth, or data exposure risks?
7. Refactoring smells — explicitly name any detected Fowler smells: Mysterious Name, Duplicated Code, Feature Envy, Data Clumps, Primitive Obsession, Message Chains, Middle Man

For each finding, categorize as: must-fix / should-fix / consider.
Run the verify command and report the result.

3. Collect both reports

When reviewers return:

  • Read every finding from both reports before acting on any
  • Note each verify command result
  • Compute quality score per reviewer: 100 × (total_items − must_fix − should_fix) / total_items
  • AND-gate check: both scores ≥ 94% and zero must-fix from both?

HARD GATE — If either score < 94% or either has must-fix → FAIL round. Run respond-review first. The 94% threshold also applies to npm run compliance (scripts/audit-compliance.sh).

4. Hand off to respond-review

Pass combined findings to respond-review to categorize and apply fixes. Increment iteration counter. Re-dispatch both reviewers until AND-gate passes or iteration 3 exhausted.

Report to user: "Review round [N/3]. Reviewer A: [score], Reviewer B: [score]. AND-gate: [PASS|FAIL]."

Verify

→ verify: test -f scripts/lib/parallel-review-worktrees.sh && test -f skills/request-review/SKILL.md

Signals

GitHub stars
248
Forks
19
Last commit
Sep 2026
Advanced
Item type
skill
Key
request-review
Source
github.com/danielvm-git/bigpowers