open-code-review

name	open-code-review
description	Performs AI-powered code review on Git changes using the `ocr` CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply fixes when requested. With appropriate review rules, can detect various types of issues including bugs, security vulnerabilities, performance problems, and code quality concerns.
license	Apache-2.0
compatibility	Requires the `ocr` CLI installed (via `npm install -g @alibaba-group/open-code-review` or GitHub release binary). Requires a configured LLM (Anthropic or OpenAI-compatible) before first run.
metadata	{"author":"alibaba","homepage":"https://github.com/alibaba/open-code-review","version":"1.0.0"}

Open Code Review

This Codex plugin skill intentionally mirrors the canonical skill at skills/open-code-review/SKILL.md. Keep both files synchronized when updating OCR agent instructions; a symlink is avoided because plugin installs may only materialize the plugin subtree.

A skill for invoking open-code-review (ocr) — an open-source AI code review CLI that reads Git diffs and generates structured, line-level review comments.

Prerequisites check

Before starting a review, verify the environment:

# 1. Check the CLI is installed
which ocr || echo "NOT INSTALLED"

# 2. Verify LLM connectivity
ocr llm test

If ocr is not installed, install it first:

npm install -g @alibaba-group/open-code-review

If ocr llm test fails, the user must configure an LLM. Guide them with one of these options:

Option A — Environment variables (highest priority, recommended for CI):

export OCR_LLM_URL=https://api.anthropic.com/v1/messages
export OCR_LLM_TOKEN=<api-key>
export OCR_LLM_MODEL=claude-opus-4-6
export OCR_USE_ANTHROPIC=true

Option B — Persistent config:

ocr config set llm.url https://api.anthropic.com/v1/messages
ocr config set llm.auth_token <api-key>
ocr config set llm.model claude-opus-4-6
ocr config set llm.use_anthropic true

Stop here and ask the user to provide credentials — never invent or hardcode API keys.

Workflow

Step 1: Gather Business Context

Analyze the review target (commits, branch, or changes) to extract concise business context. Pass this context via --background to improve review quality.

Step 2: Run Code Review

Run the OCR command with appropriate flags. Always pass business context via --background when available:

ocr review --audience agent --background "business context here" [user-args]

Argument handling:

• Background context (RECOMMENDED): use --background "context" or -b "context" to provide business context for better review quality
• Default (no user arguments): reviews staged, unstaged, and untracked changes (workspace mode)
• Specific commit: use --commit or -c to review a single commit against its parent
• Branch comparison: use --from <ref> and --to <ref> to review diff between two refs
• Timeout: default timeout is 10 minutes per file; adjust with --timeout <minutes>
• Concurrency: default concurrency is 8 file workers; reduce with --concurrency <n> if rate limits are hit
• Preview mode: use --preview or -p to preview which files will be reviewed without running the LLM
• Installation: if ocr command is not found, install it by running npm i -g @alibaba-group/open-code-review

Common invocation patterns:

User says	Command to run
"review my changes" / "review the working copy"	ocr review --audience agent -b "context"
"review this PR" / "review feature branch"	ocr review --audience agent -b "context" --from main --to <branch>
"review commit abc123"	ocr review --audience agent -b "context" --commit abc123
"what would be reviewed?" (dry-run)	ocr review --preview

Output mode:

• Always use --audience agent to suppress progress UI and emit only the final summary

Step 3: Classify and Report

For each comment from the review output, classify by priority and report all issues to the user:

• High: Obvious bugs, security issues, clear mistakes, or well-founded suggestions with precise fix proposals
• Medium: Reasonable concerns but context-dependent, style/performance suggestions, or fixes that require manual implementation
• Low: Likely false positives, lacking sufficient context, nitpicks, or meaningless suggestions

Report all comments grouped by priority level.

Step 4: Fix

Before applying fixes, check whether the user requested automatic fixes:

• If the user explicitly requested "review and fix" or similar, proceed with automatic fixes
• If the user only requested "review" without fix intent, ask for permission before applying any changes

When fixing issues and suggestions:

• Focus on High and Medium priority items
• Apply fixes directly to the code when safe and well-defined
• For complex fixes requiring manual intervention, clearly describe what needs to be done
• Always verify fixes with the user before committing

Output Format

Each comment contains:

• path: File path
• content: Review comment text
• start_line / end_line: Line range (both 0 means positioning failed)
• suggestion_code: Optional fix suggestion
• existing_code: Optional original code snippet
• thinking: Optional LLM reasoning process

After filtering comments by priority, present results using this template:

## Code Review Results

**Files reviewed**: N
**Issues found**: X high priority / Y medium priority

### High Priority

- **`path/to/file.java:42`** — Brief description
  > Recommendation: How to fix

### Medium Priority

- **`path/to/file.ts:88`** — Brief description
  > Recommendation: How to fix (if applicable)

If the review found no issues after filtering, simply state: "Review complete — no issues found in N files."

Priority classification:

• High: Obvious bugs, security issues, clear mistakes, or well-founded suggestions with precise fix proposals
• Medium: Reasonable concerns but context-dependent, style/performance suggestions, or fixes that require manual implementation
• Low: Discarded silently (likely false positives, lacking context, nitpicks, or meaningless suggestions)

Handling mispositioned comments:

When start_line and end_line are both 0, the comment failed to locate the exact position in the file. In such cases:

1. Read the comment content to understand the issue
2. Examine the target file mentioned in the comment
3. Identify the relevant code section based on the comment's context
4. Apply the fix or suggestion to the correct location

Custom Review Rules

If the user wants project-specific rules, OCR resolves them in this priority order:

1. --rule <path> flag (highest)
2. <repo>/.opencodereview/rule.json
3. ~/.opencodereview/rule.json
4. Built-in system defaults (lowest)

By default, the first matching user rule replaces the built-in system rule. Set merge_system_rule: true on a rule entry when the matched system rule and user rule should both be included.

Rule file format:

{
  "rules": [
    {
      "path": "**/*.java",
      "rule": "All new methods must validate required parameters for null",
      "merge_system_rule": true
    },
    {
      "path": "**/*mapper*.xml",
      "rule": "Check SQL for injection risks and missing closing tags"
    }
  ]
}

To preview which rule applies to a file before reviewing:

ocr rules check src/main/java/com/example/Foo.java

Gotchas

• LLM must be configured first — ocr review will fail loudly if no LLM is reachable. Always run ocr llm test before the first review.
• Working directory matters — ocr review operates on the Git repo at the current directory. Use --repo /path/to/repo to run from elsewhere.
• Untracked files are reviewed in workspace mode — running bare ocr review includes staged, unstaged, and untracked changes. Stage selectively if you want narrower scope.
• Large diffs may hit token limits — files with very large diffs may be truncated. The default MAX_TOKENS is 58888 per request.
• Plan phase triggers at 50 lines — diffs exceeding 50 changed lines run an extra risk-analysis phase before main review. This adds latency but improves quality.
• Don't pass --audience human — it streams progress UI that pollutes output. Always use --audience agent.
• Comment language follows config — set language config to English or Chinese (default: Chinese) to control review comment language.

Validation

After the review completes, verify success by checking:

1. The command exited with code 0
2. Comments were generated (or "No comments generated" message appears)
3. Warnings (if any) are displayed in stderr

If errors occurred, check the stderr warnings for details about which files failed and why.

References

• Full docs: https://github.com/alibaba/open-code-review
• NPM package: https://www.npmjs.com/package/@alibaba-group/open-code-review
• Issue tracker: https://github.com/alibaba/open-code-review/issues