open-code-review v1.0.0A

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---
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

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.

## Prerequisites check

Before starting a review, verify the environment:

```bash
# 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:

```bash
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):**

```bash
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:**

```bash
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

Before running the review, inspect the review target (commits, branch, working copy) and synthesise a short `--background` string that captures the intent of the changes. This improves review quality by giving the LLM context about what the code is trying to achieve.

### Step 2: Run Code Review

- **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

Group the JSON comments into **High** / **Medium** / **Low** using the rubric below, then render a Markdown summary.

| Severity | Criteria |
|----------|----------|
| **High** | Bugs, security vulnerabilities, data loss risks, correctness issues |
| **Medium** | Performance problems, maintainability concerns, test gaps, unclear logic |
| **Low** | Style nits, minor naming suggestions, documentation improvements |

Nitpicks and likely false positives are silently dropped.

### Step 4: Fix

If the user said "review **and** fix" (or similar), apply safe fixes to High/Medium items inline. Otherwise ask before touching the code.

## Output Format

Each comment follows this structure:

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| path | string | yes | Relative file path |
| content | string | yes | Review comment describing the issue |
| start_line | integer | no | Start line in the new file |
| end_line | integer | no | End line in the new file |
| category | enum | no | bug, security, performance, maintainability, test, style, documentation, other |
| severity | enum | no | critical, high, medium, low |

## Custom Review Rules

OCR supports custom review rules via `.ocr-rules.yaml` in the repo root. Rules can target specific file patterns and define review checklists. See the [Review Rules docs](https://open-codereview.ai/docs/review-rules) for the full schema.

## 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`.

## 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