Prototype · Preview: every server in the official registry, synced every 30 minutes. Sign in with Google to write reviews.
PopNx MCPMCP servers for people who use AI · explained in plain words

open-code-review Skill

by alibaba★ 49,8212 skills

Install

2 skills (instruction sets) by alibaba that an AI reads and follows · not an MCP server

Maker’s description (original English)

Secure, fast, efficient, battle-tested at Alibaba's scale. Hybrid architecture code review tool: deterministic pipelines + LLM Agent, precise line-level comments, built-in multi-language ruleset (NPE, thread-safety, XSS, SQL injection), OpenAI & Anthropic compatible.

How a Skill differs from MCP

MCP connects an AI to an outside service or data. A Skill is a how-to guide the AI reads and follows; it connects to nothing.

Works with

Claude CodeCodexCursorOpenCode

From the maker’s README and the folders in the repo

Install

A Skill is instructions the AI will follow. Read the files before installing, and install only from makers you trust.

Which app do you use?

  1. Open Terminal

    Mac: press Command+Space and type Terminal · Windows: open PowerShell (the command below is for Mac/Linux)

  2. Copy the skill folder to ~/.claude/skills

    This downloads the repo to a temporary folder and copies only the folders that contain a SKILL.md

    rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/alibaba/open-code-review.git /tmp/popnx-skill && mkdir -p ~/.claude/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.claude/skills && rm -rf /tmp/popnx-skill
  3. Restart Claude Code, then type /open-code-review or ask for a matching task and Claude picks it up

  1. Open Terminal

    Mac: press Command+Space and type Terminal · Windows: open PowerShell (the command below is for Mac/Linux)

  2. Copy the skill folder to ~/.agents/skills

    This downloads the repo to a temporary folder and copies only the folders that contain a SKILL.md

    rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/alibaba/open-code-review.git /tmp/popnx-skill && mkdir -p ~/.agents/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.agents/skills && rm -rf /tmp/popnx-skill
  3. Restart Codex, then ask for a matching task; Codex lists the skill

  1. Open Terminal

    Mac: press Command+Space and type Terminal · Windows: open PowerShell (the command below is for Mac/Linux)

  2. Copy the skill folder to ~/.cursor/skills

    This downloads the repo to a temporary folder and copies only the folders that contain a SKILL.md

    rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/alibaba/open-code-review.git /tmp/popnx-skill && mkdir -p ~/.cursor/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.cursor/skills && rm -rf /tmp/popnx-skill
  3. In Cursor’s Agent chat, type / and pick open-code-review

Other apps that support Agent Skills (such as Gemini CLI or GitHub Copilot) use different folders. Check that app’s guide, then copy the folder with the SKILL.md there.

View on GitHub ↗

Skill folders checked against the official Claude Code, Codex and Cursor guides on Nov 11, 2026

Read SKILL.md before installing

The text below comes from the maker’s repo, not from PopNx MCP

---
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>` — 

Discussion

Ask, share a tip or report that it does not work. Sorted by what people marked helpful.

No posts yet. Be the first to ask or share.

Posts here are written by users, not the PopNx team.

Sign in with Google to post

From GitHub

GitHub stars
49,821
Maker
alibaba
License
Apache-2.0
Last code change
Nov 11, 2026
SKILL.md files in repo
4
Topics
agent, agent-skills, code-review, code-review-assistant, harness, repository-level-context