story: e45s28
SkillAI & modelsSends your finished code to two independent AI reviewers who give unbiased feedback before you commit.
Use story: e45s28 in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add story: e45s28 and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the story: e45s28 skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
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.
| Parameter | Value |
|---|---|
| Reviewers | 2 (mandatory) |
MAX_REVIEW_ITERATIONS | 5 (hard cap — e45s28; iteration 6 forbidden) |
| Pass rule | AND-gate — both reviewers must pass independently |
| Blindness | Neither reviewer sees the other's report until both complete |
Iteration loop (max MAX_REVIEW_ITERATIONS):
- Dispatch Reviewer A and Reviewer B in parallel with identical briefs but separate contexts.
- Collect both reports. Each categorizes findings: must-fix / should-fix / consider.
- AND-gate: If either reviewer has must-fix findings → FAIL round. Run
respond-review, fix, re-dispatch both reviewers. - If both pass (zero must-fix, score ≥ 94% each) → review complete.
- 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 (activeepics/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 assecurity-sensitive: trueif 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):
| Agent | Focus |
|---|---|
| R-correctness | Logic, edge cases, verify command result |
| R-conventions | CONVENTIONS.md, test quality (F.I.R.S.T) |
| R-security | Injection, auth, secrets (when security-sensitive) |
| R-design | Simpler 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-reviewfirst. The 94% threshold also applies tonpm 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
github.com/danielvm-git/bigpowers
More in AI & models
Skill · anthropics
More in AI & modelstriage
Skill · mattpocock
More in AI & modelswayfinder
Skill · mattpocock
More in AI & modelsalgorithmic-art
Skill · anthropics
More in AI & modelscode-review-and-quality
Skill · addyosmani
More in AI & modelsmarketing-psychology
Skill · coreyhaines31
More in AI & models