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cross-review

Cross review code using a subagent with a specified model. Use when the user asks to review code changes AND specifies a model to use (e.g., 'review with opus', 'use sonnet to review', 'review changes with gemini'). The key differentiator from the regular zen-review skill is that the user explicitly specifies which model should perform the review. The root agent reconstructs what changed from its own conversation history — no git commands are used.

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ProjectZeroDays/FreeAI_AI_Inference_Workstation스타 5(GitHub 5 + cccskills 0)

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Cross Review

Reconstruct what you changed during this conversation, then delegate the actual review to a single subagent running the zen-review skill with a user-specified model.

IMPORTANT: Steps 1–2 run in the current agent (the master). Only Step 3 spawns a subagent. Do NOT spawn a subagent to execute this skill's workflow — that creates unnecessary nesting.

Expected Prompt Format

Use review skill with <model-id> model to review the changes. Review instructions: <instructions>

Workflow

Step 1: Parse the user request

Extract from the user's prompt:

  • Model: The model ID to use for the subagent. Validate against the available models.
  • Review instructions: Any text after "Review instructions:" — pass these verbatim to the subagent.
  • Change scope: Any indication of what should be reviewed. If not provided, default to reviewing all changes made by you during this conversation.

Step 2: Gather context from your own changes

Collect all information the review subagent will need. Do this in the current agent — do NOT delegate this step.

You already know what you changed — reconstruct the diff from your own conversation history:

  1. Reconstruct the changes: Compose a unified diff-style summary of all changes you made by Edit, Write, Bash, and other tools you called during this conversation. Group changes by file.

    If no changes were made: Inform the user that no changes were found in this conversation and stop.

  2. Create a unique temp directory and save the diff there: Create a unique temporary directory for this review session and use it for generated files.

    # macOS / Linux / Git Bash on Windows
    TEMP_DIR=$(mktemp -d "${TMPDIR:-/tmp}/cross-review-XXXXXXXX")
    

    If mktemp is not available (for example, native Windows without Git Bash), create a temporary directory with PowerShell and capture its full path before continuing.

    Write the reconstructed unified diff to $TEMP_DIR/cross-review-diff.patch using the Bash tool (for example, cat << 'DIFF_EOF' > "$TEMP_DIR/cross-review-diff.patch"). The zen-review skill reads the diff from a file path.

  3. Read the final state of changed files to provide full surrounding context. Use the Read tool on each changed file.

  4. Check for related context:

    • Read test files related to the changed files, prioritizing nearby paths and excluding dependency/vendor directories (e.g., node_modules, .git, dist, build, coverage)
    • Check for configuration changes that may affect behavior
    • Note any related type definitions or interfaces

Do NOT show gathered context or its process to the user. Use it only for the subagent.

Step 3: Spawn the review subagent

Use spawn_subagent with:

  • skill: "zen-review"
  • model: The model extracted from the user's request.
  • prompt: use following template
IMPORTANT: Follow the skill instructions STRICTLY and IN ORDER. The diff is saved at {temp_dir}/cross-review-diff.patch. Substitute `{temp_dir}` with the real temp directory path from Step 2 before spawning the subagent. Your FIRST action MUST be to read that file — do NOT run git diff, do NOT read any other files before you have the diff. Review the changes.

## Review Instructions

{user's review instructions, verbatim}

## Additional Context

{links to any related files, test files, type definitions, or user-provided requirements}

Step 4: Format and relay the result — READ-ONLY, NO ACTIONS

The zen-review subagent returns findings as a JSON array:

[{"path": "...", "line": ..., "body": "...", "severity": "P0|P1|P2|P3"}]

Format the JSON into a human-readable review before relaying to the user:

## Code Review

**Verdict**: [APPROVE if no P0/P1 findings | REQUEST CHANGES if P0/P1 exist]

### Summary
[1-2 sentences: what the changes do and overall assessment based on findings]

### Findings

| Priority | Issue | Location |
|----------|-------|----------|
| P0 | {body summary} | {path}:{line} |
| ... | ... | ... |

### Details

#### [severity] Issue
**File:** `{path}:{line}`

{body}

(Repeat for each P0/P1 finding. P2/P3 items only need the table entry.)

### Recommendation
[Concise actionable recommendation for the author]

If the subagent returns [] (no findings), output:

## Code Review

**Verdict**: APPROVE

No issues found.

CRITICAL constraints:

  • Do NOT act on the review: Do NOT fix, improve, or modify any code based on findings.
  • Do NOT add your own commentary on the findings beyond formatting.
  • A one-line model attribution (e.g., "Review produced by opus-4-8-think") is acceptable.
  • You can ONLY suggest or offer to implement recommendations. Let the user decide.

Error Handling

  • spawn_subagent fails or times out: Inform the user of the failure. Suggest retrying or using a different model.
  • No changes in conversation: Inform the user no changes were found and stop.
  • Subagent returns an error or incomplete review: Relay whatever was returned and note that the review may be incomplete.

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