/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 supported LLM provider before first run (protocols: Anthropic,
  OpenAI Chat Completions, OpenAI Responses, AWS Bedrock).
metadata:
  author: alibaba
  homepage: https://github.com/alibaba/open-code-review
  version: "1.0.0"

Open Code Review

A skill for invoking [open-code-review](https://github.com/alibaba/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

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:**

| 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
• **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