open-code-review

# 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:

```bash
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
- **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, redirect output to a temporary file (`ocr review --audience agent ... > /tmp/ocr_out.txt 2>&1`) and inspect it in full via a file reading tool instead of piping 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:

```markdown
## 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:

```json
{
  "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:

```bash
ocr rules check sr