Cross-Model Code Review with Codex CLI Skill

Cross-Model Code Review with Codex CLI

Cross-model validation using the codex binary directly. Claude writes code, Codex reviews it — different architecture, different training distribution, no self-approval bias.

Core insight: Single-model self-review is systematically biased. Cross-model review catches different bug classes because the reviewer has fundamentally different blind spots than the author.

Prerequisite: The codex CLI must be installed and authenticated. Verify with codex --help. Configure defaults in ~/.codex/config.toml:

model = "gpt-5.4"
review_model = "gpt-5.4"
# Note: review_model overrides model for codex review specifically
model_reasoning_effort = "high"

Two Ways to Invoke Codex

| Mode | Command | Best For | | -------------- | ----------------------------------------------------------- | ----------------------------------------------------------- | | codex review | Structured diff review with prioritized findings | Pre-PR reviews, commit reviews, WIP checks | | codex exec | Freeform non-interactive deep-dive with full prompt control | Security audits, architecture review, focused investigation |

Key flags:

| Flag | Applies To | Purpose | | ----------------------------------- | ------------- | ---------------------------------------------------- | | -c model="gpt-5.4" | both | Model selection (review has no -m flag) | | -m, --model | exec only | Model selection shorthand | | -c model_reasoning_effort="xhigh" | both | Reasoning depth: low / medium / high / xhigh | | --base <BRANCH> | review only | Diff against base branch | | --commit <SHA> | review only | Review a specific commit | | --uncommitted | review only | Review working tree changes |

Review Patterns

Pattern 1: Pre-PR Full Review (Default)

The standard review before opening a PR. Use for any non-trivial change.

Step 1 — Structured review (catches correctness + general issues):
  Run via Bash:
    codex review --base main -c model="gpt-5.4"

Step 2 — Security deep-dive (if code touches auth, input handling, or APIs):
  Run via Bash:
    codex exec -m gpt-5.4 \
      -c model_reasoning_effort="xhigh" \
      "<security prompt from references/prompts.md>"

Step 3 — Fix findings, then re-review:
  Run via Bash:
    codex review --base main -c model="gpt-5.4"

Pattern 2: Commit-Level Review

Quick check after each meaningful commit.

codex review --commit <SHA> -c model="gpt-5.4"

Pattern 3: WIP Check

Review uncommitted work mid-development. Catches issues before they're baked in.

codex review --uncommitted -c model="gpt-5.4"

Pattern 4: Focused Investigation

Surgical deep-dive on a specific concern (error handling, concurrency, data flow).

codex exec -m gpt-5.4 \
  -c model_reasoning_effort="xhigh" \
  "Analyze [specific concern] in the changes between main and HEAD.
   For each issue found: cite file and line, explain the risk,
   suggest a concrete fix. Confidence threshold: only flag issues
   you are >=70% confident about."

Pattern 5: Ralph Loop (Implement-Review-Fix)

Iterative quality enforcement — implement, review, fix, repeat. Max 3 iterations.

Iteration 1:
  Claude -> implement feature
  Bash: codex review --base main -c model="gpt-5.4" -> findings
  Claude -> fix critical/high findings

Iteration 2:
  Bash: codex review --base main -c model="gpt-5.4" -> verify fixes + catch remaining
  Claude -> fix remaining issues

Iteration 3 (final):
  Bash: codex review --base main -c model="gpt-5.4" -> clean bill of health
  (or accept known trade-offs and document them)

STOP after 3 iterations. Diminishing returns beyond this.

Multi-Pass Strategy

For thorough reviews, run multiple focused passes instead of one vague pass. Each pass gets a specific persona and concern domain.

| Pass | Focus | Mode | Reasoning | | ---------------- | ---------------------------------------- | ------------------------------------- | --------- | | Correctness | Bugs, logic, edge cases, race conditions | codex review | default | | Security | OWASP Top 10, injection, auth, secrets | codex exec with security prompt | xhigh | | Architecture | Coupling, abstractions, API consistency | codex exec with architecture prompt | xhigh | | Performance | O(n^2), N+1 queries, memory leaks | codex exec with performance prompt | high |

Run passes sequentially. Fix critical findings between passes to avoid noise compounding.

When to use multi-pass vs single-pass:

| Change Size | Strategy | | ------------------------------------------- | ------------------------------ | | < 50 lines, single concern | Single codex review | | 50-300 lines, feature work | codex review + security pass | | 300+ lines or architecture change | Full 4-pass | | Security-sensitive (auth, payments, crypto) | Always include security pass |

Decision Tree: Which Pattern?

digraph review_decision {
    rankdir=TB;
    node [shape=diamond];

    "What stage?" -> "Pre-commit" [label="writing code"];
    "What stage?" -> "Pre-PR" [label="ready to submit"];
    "What stage?" -> "Post-commit" [label="just committed"];
    "What stage?" -> "Investigating" [label="specific concern"];

    node [shape=box];
    "Pre-commit" -> "Pattern 3: WIP Check";
    "Pre-PR" -> "How big?";
    "Post-commit" -> "Pattern 2: Commit Review";
    "Investigating" -> "Pattern 4: Focused Investigation";

    "How big?" [shape=diamond];
    "How big?" -> "Pattern 1: Pre-PR Review" [label="< 300 lines"];
    "How big?" -> "Full Multi-Pass" [label=">= 300 lines"];
}

Prompt Engineering Rules

Assign a persona — "senior security engineer" beats "review for security"
Specify what to skip — "Skip formatting, naming style, minor docs gaps" prevents bikeshedding
Require confidence scores — Only act on findings with confidence >= 0.7
Demand file:line citations — Vague findings without location are not actionable
Ask for concrete fixes — "Suggest a specific fix" not just "this is a problem"
One domain per pass — Security-only, architecture-only. Mixing dilutes depth.

Ready-to-use prompt templates are in references/prompts.md.

Anti-Patterns

| Anti-Pattern | Why It Fails | Fix | | ---------------------------------------------------- | ------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | | "Review this code" | Too vague — produces surface-level bikeshedding | Use specific domain prompts with persona | | Single pass for everything | Context dilution — every dimension gets shallow treatment | Multi-pass with one concern per pass | | Self-review (Claude reviews Claude's code) | Systematic bias — models approve their own patterns | Cross-model: Claude writes, Codex reviews | | No confidence threshold | Noise floods signal — 0.3 confidence findings waste time | Only act on >= 0.7 confidence | | Style comments in review | LLMs default to bikeshedding without explicit skip directives | "Skip: formatting, naming, minor docs" | | > 3 review iterations | Diminishing returns, increasing noise, overbaking | Stop at 3. Accept trade-offs. | | Review without project context | Generic advice disconnected from codebase conventions | Codex reads CLAUDE.md/AGENTS.md automatically | | Using an MCP wrapper | Unnecessary indirection over a CLI binary | Call codex directly via Bash | | Specifying legacy/deprecated models (o1, o3, gpt-4o) | These models are ancient history and may not be available on the user's account | Use the defaults from ~/.codex/config.toml or the model shown in codex --help. Never guess model names | | Overcomplicating the invocation | Adding unnecessary flags, custom reasoning efforts, or exotic configs | Use codex review with simple flags (--uncommitted, --base main). The defaults are good |

What This Skill is NOT

Not a replacement for human review. Cross-model review catches bugs but can't evaluate product direction or user experience.
Not a linter. Don't use Codex review for formatting or style — that's what linters are for.
Not infallible. 5-15% false positive rate is normal. Triage findings, don't blindly fix everything.
Not for self-approval. The whole point is cross-model validation. Don't use Claude to review Claude's code.

References

For ready-to-use prompt templates, see references/prompts.md.

Agent Skills: Cross-Model Code Review with Codex CLI

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