Cross Modal Review

Cross-Modal Review

I act as a second, independent reviewer — catching errors, blind spots, and assumptions the primary pass missed. Inspired by gbrain's cross-modal quality gate pattern.

Token Optimization

Expected range: 400–1,500 tokens (second-pass review), 50 tokens (trivial change, skip)

Patterns used: Git diff scope default (staged only), early exit (no reviewable content), progressive disclosure (verdict first → issues → details)

Early exit: If the target is a single-line change or a comment-only edit, report "Trivial change — cross-modal review skipped" and stop.

When to Use

Invoke after:

• /review — to get a second opinion on the primary review output
• Generating a new skill draft — to validate it passes conformance before writing
• Writing complex business logic — to catch logic errors the primary pass may rationalize
• Security-sensitive changes — to add a second set of eyes before commit

Can also be invoked directly: /cross-modal-review with staged changes as the default target.

Step 1 — Identify Target

# Default: staged changes
git diff --cached --stat
git diff --cached --name-only

# If no staged changes, check for an argument (file path or prior output)
[[ -z "$ARGUMENTS" ]] && git diff --cached --quiet && echo "Nothing to review" && exit 0

Step 2 — Frame the Second-Pass Review

Review the target with deliberate adversarial framing — assume the primary pass was optimistic. Check specifically:

Correctness

• Does the logic match the stated intent?
• Are there off-by-one errors, missed null checks, or incorrect assumptions?
• Do variable names match what they actually contain?

Completeness

• Are edge cases handled?
• Are error paths covered?
• Does the change handle the empty/zero/nil case?

Safety

• Are there command injection, path traversal, or injection vulnerabilities?
• Are credentials, tokens, or PII handled correctly?
• Are destructive operations guarded?

Consistency

• Does the change follow the patterns in the surrounding code?
• Are naming conventions consistent?
• Are existing tests updated to cover the change?

Skill-Specific (when reviewing a SKILL.md)

• Does the frontmatter have all required fields?
• Is the Token Optimization section present with concrete estimates?
• Does the description trigger correctly?
• Are there early exit conditions?
• Is it under 100 lines of instructions?

Step 3 — Report

Cross-modal review — 

Verdict: PASS / PASS WITH NOTES / FAIL

Issues found:
  [CRITICAL]  — 
  [WARN]      — 
  [NOTE]      — 

Passed:
  ✓ Logic matches intent
  ✓ No injection vulnerabilities
  ...

Recommendation: 

Verdict definitions:

• PASS — No issues; safe to proceed
• PASS WITH NOTES — Minor issues that don't block; worth fixing before merge
• FAIL — One or more CRITICAL issues; do not proceed until resolved

Step 4 — On FAIL

List each CRITICAL issue with:

• Where it is (file:line or section name)
• Why it is critical
• The minimal fix

Do not apply fixes automatically — this is a review, not a fixer. The user decides whether to fix and re-run.

Edge Cases

• Reviewing a review: If the input is the output of /review, compare both reviews and flag any disagreements between them as areas needing human judgment
• Large diff (50+ files): Focus on the highest-risk files (auth, deploy, schema changes) and sample the rest; note what was sampled
• No prior review: Runs as a standalone first-pass review if no prior review output is available

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: manastalukdar
• Source: manastalukdar/ai-devstudio
• License: MIT

Install and usage instructions live in the source repository linked above.