open-code-review

Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.

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

## 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)

{
  "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"
    }
  ]
}

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

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

ocr config pro

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"

---
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

**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:

```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
- **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:

```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 src/main/java/com/example/Foo.java
```

## Advanced Review Options

Beyond the common flags above, `ocr review` exposes a few groups of controls. Run `ocr review --help` for the complete list.

**Scoping**

- `--exclude '<patterns>'` — comma-separated gitignore-style patterns (for example `--exclude '**/generated/*,**/testdata/*'`), merged with `rule.json` excludes.
- `--background-file <path>` — read review context from a Markdown file. Takes precedence over `--background`.

**Output**

- `--format text|json|sarif` — `text` (default) for humans; `json` for machine-readable findings; `sarif` for code-scanning integrations such as GitHub Code Scanning.

**Model**

- `--provider <name>` / `--model <name>` — override the configured provider/model for this run only (for example, to recheck a diff with a different model; the user names the model, `ocr llm providers` lists the built-ins).

**Budget**

- `--max-tokens <n>` — per-group prompt ceiling; defaults to the configured value or the template default (`200000`).
- `--max-tokens-budget <n>` — cap total input + output tokens for the run. Checked before every LLM round: a group already over budget gets one final round to submit findings, no further groups are dispatched, partial results are still published, and skipped files are reported as `failed(budget)`.
- `--no-filter` — keep all review comments and skip the LLM post-filtering call.

## Gotchas

- **LLM must be configured first** — `ocr review` will fail loudly if no LLM is reachable. See the Troubleshooting section below if this happens.
- **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** — `MAX_TOKENS` sets the prompt budget (`200000` in the review template; `ocr scan` uses `58888`); conversation context is compressed to stay within this prompt budget. Model output is capped separately by `MAX_COMPLETION_TOKENS` (`16384`). A file whose diff alone exceeds ~80% of `MAX_TOKENS` is skipped before the LLM is called.
- **Plan phase triggers on either of two thresholds** — a group runs an extra risk-analysis phase before main review when its largest changed file reaches `PLAN_MODE_LINE_THRESHOLD` (default `50`) **or** it holds 2+ files whose combined changed lines reach `PLAN_MODE_GROUP_LINE_THRESHOLD` (default `100`). 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** — the `language` config controls review comment language, defaults to `English`, and accepts any language name (for example `English` or `中文`).
- **Avoid output truncation** — Large review runs produce verbose output. Never pipe command output to `tail` or `head` as it drops review comments from earlier sections. Use `--output <path>` and read it in full; on older CLIs, follow the **Output file** guidance above.
- **Resume an interrupted review** — a failed or interrupted range/commit review can be continued with `ocr review --resume <id>` using the same `--from`/`--to` or `--commit` target (the id is printed as `retry with: --resume <id>` on failure, or find it with `ocr session list`). Workspace resume is not supported.

## 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.

## Troubleshooting

**`ocr: command not found`**

Install the CLI:

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

**`unknown flag: --output`**

The CLI is older than v1.10.0. 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`.

**`ocr review` fails with LLM connection error**

Prompt the user to configure an LLM provider.

Interactive setup (recommended):

```bash
ocr config pro