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4r code review

Skill GustavoGutierrez/engineering-skills/skills/4r-code-review

Reusable AI agent skills for engineering, product, architecture, planning, and delivery workflows.

Install
npx -y skills add GustavoGutierrez/engineering-skills --skill 4r-code-review

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One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Trigger: 4R code review, review this change, review agent code, pre-commit review, is this done, quality gate before commit. Run a Risk/Readability/Reliability/Resilience review on a completed change before declaring it done.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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4R Code Review

When to Use

Use this skill when a coding task is reported complete and BEFORE declaring it done, committing, pushing, or opening a PR — especially when the change was produced wholly or partly by a coding agent.

Use it to run a systematic, stack-agnostic review across four dimensions — Risk, Readability, Reliability, Resilience — that classifies findings by severity, surfaces fixable defects, and returns an explicit accept/block decision.

Do NOT use this skill to write the feature, design architecture from scratch, or audit non-code artifacts (use reviewer for PRDs, specs, and API contracts). This skill reviews a code diff, not prose.

Operating Workflow

  1. Scope the diff. Run git diff / git diff --staged (and git status) to get exactly what changed. Review the diff and its blast radius, not the whole repo.
  2. Tier the review. Match effort to risk (see Decision Rules). Do not burn a deep 4R pass on a one-line doc typo; do not shortcut a change that touches auth, data, or money.
  3. Confirm intent match. Verify the change does what the task asked AND ONLY that. Flag edits outside the intended scope, stray files, and unrequested behavior changes.
  4. Run the 4R passes. Evaluate each dimension against references/4r-dimensions.md. Prioritize the AI failure patterns listed there — agent code passes the eye test and fails differently than human code.
  5. Gather evidence. Run the test suite / linter / build when available. Treat "looks correct" as unproven until executed. Record commands and results.
  6. Classify and decide. Tag every finding with severity, score each R 0–2, and return one verdict: Approve / Approve with follow-up / Request changes / Escalate.

Core Rules

  • Evidence over intention: tests run, errors handled, limits set, and observability must be VISIBLE in the change, not assumed.
  • Code that looks correct is not evidence that it is correct. Trace the unhappy paths explicitly.
  • Complexity is a budget. A change that adds branches, state, or abstraction must justify the cost; reject or simplify accidental or AI-inflated complexity.
  • Be specific and actionable. Never say "security is missing" — name the file, line, failure mode, and the fix direction.
  • Never approve a change with a Blocker in Risk or Resilience. A residual risk must be explicit, bounded, and proportional.

The 4R Dimensions

RCentral questionCovers
RiskDoes this add disproportionate risk to security or production?Auth, secrets, trust boundaries, input validation, injection, blast radius, rollback.
ReadabilityCan another engineer maintain this without rebuilding the author's intent?Naming, structure, duplication, complexity budget, AI slop.
ReliabilityIs there real evidence it works on normal AND edge inputs?Tests that catch bugs, edge cases, error handling, timeouts, correctness.
ResilienceWhen it fails, does the system recover or cascade?Retries with backoff, fallback, graceful degradation, observability, isolation.

Full per-dimension checklists, AI-specific failure patterns, and acceptance criteria are in references/4r-dimensions.md — read it before the 4R passes.

Decision Rules

SituationAction
Diff is trivial (docs, comments, formatting)Quick pass: intent match + Risk scan only; skip deep 4R.
Diff touches auth, secrets, payments, data, or infraFull 4R + mandatory Escalate consideration; no Blockers allowed.
Agent-generated logic, new integration, or 2+ filesFull 4R; prioritize AI failure patterns and test quality.
Tests absent where change warrants themReliability ≤ 1; Request changes unless risk is low AND justified in writing.
Blocker in Risk or ResilienceBlock. Do not approve regardless of other scores.
Change exceeds reviewer's authority or domainEscalate to specialized/human review.

Severity

SeverityMeaning
🔴 BlockerProbable vulnerability, data loss, critical regression, plausible cascade, unbounded risk in a sensitive zone.
🟠 HighMissing tests on a critical path, absent timeout, wrong retry, excessive complexity in core logic, weak observability at a critical point.
🟡 MediumImprovable readability, bounded uncovered edge case, documentable debt without immediate impact.
🔵 LowNaming, minor structure, non-critical simplification.

Scorecard

Score each R 0–2 (0 = insufficient, 1 = acceptable with reservations, 2 = solid). Merge rule: minimum 1 per dimension, no 0 in Risk or Resilience, target average ≥ 1.5. Any security Blocker or plausible cascade invalidates the aggregate until fixed.

Output Contract

Produce the report in assets/4r-review-report.md. Required sections: Context, one block per R (verdict Pass/Concern/Block + findings table), a severity-tagged findings list, the 0–2 scorecard, and a Final Decision (status + residual risk + required actions before merge + follow-ups). End with one explicit sentence: whether the change may be committed/pushed or must return to iteration.

Quality Checklist

Before returning the review, verify:

  • Review is based on the actual diff, with tests/linter/build run when available.
  • Intent match confirmed: change does what was asked and nothing extra.
  • All four R dimensions evaluated; AI failure patterns checked on agent code.
  • Every finding cites a file/line, the failure mode, and an actionable fix.
  • Severity assigned by impact; scorecard filled; merge rule applied.
  • No Blocker left in Risk or Resilience before any Approve.
  • Final decision is explicit and unambiguous.

References

  • references/4r-dimensions.md — per-dimension checklists, AI failure patterns, acceptance criteria. Read before the 4R passes.
  • assets/4r-review-report.md — report template. Fill for every review.

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