Open Code Review CLI

Open Code Review

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.

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

Do not pre-check whether ocr is installed — skip probes like command -v ocr or ocr --version. Assume the CLI is available and run the review directly; that saves a tool call on the common path. Only if the review fails with command not found should you install it per Troubleshooting.

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: effective timeout per review group = --timeout × review rounds. Default --timeout 15 with default effort medium (2 rounds) gives 30 minutes; low/high give 15/45 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
• Output file: use --output <path> to write the full result to a file instead of stdout. If the command fails with unknown flag: --output, do not continue the review with plain stdout. Ask the user whether to upgrade (npm i -g @alibaba-group/open-code-review@latest) and wait for the answer before proceeding. After the user confirms and the upgrade succeeds, rerun with --output.
• Installation: if ocr command is not found, install it by running npm i -g @alibaba-group/open-code-review

Common invocation patterns:

Output mode:

• Always use --audience agent to suppress progress UI and emit only the final summary
• Prevent output truncation: For large reviews or restricted tool environments, pass --output /tmp/ocr_out.txt and inspect the file in full via a file reading tool instead of piping stdout through tail or head, which drops earlier review comments.

On failure: If ocr review exits non-zero (e.g. an LLM connection error), do not retry blindly — consult the Troubleshooting section below for the matching fix before re-running.

Step 3: Report

OCR output includes structured severity (critical / high / medium / low) and category (bug / security / performance / maintainability / test / style / documentation / other) on each comment. Present results grouped by severity, discarding low severity items that are likely false positives or nitpicks.

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 critical, high, and medium severity 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 in OCR's output contains:

• path: File path
• content: Review comment text
• start_line / end_line: Line range (both 0 means positioning failed)
• category: Issue category (bug, security, performance, maintainability, test, style, documentation, other)
• severity: Issue severity (critical, high, medium, low)
• suggestion_code: Optional fix suggestion
• existing_code: Optional original code snippet
• thinking: Optional LLM reasoning process

Present results grouped by severity using this template:

## Code Review Results

**Files reviewed**: N
**Issues found**: X critical, Y high, Z medium

### Critical

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

### High

- **`path/to/file.java:26`** [bug] — Brief description
  > Recommendation: How to fix

### Medium

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

If no critical, high, or medium severity issues remain after filtering, state: "Review complete — no critical, high, or medium issues found in N files."

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