Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by alibaba · Agent Tool · ★ 43.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-code-review safe to install? View the security audit →
The open source AI code review agent. English | 简体中文 AI-powered code review CLI that reads Git diffs, sends changed files to a configurable LLM via an agent with tool-use capabilities, and generates structured review comments with line-level precision. The agent can read full file contents, search the codebase, inspect other changed files for context, and produce deep reviews — not just surface-level diff feedback. Install Via NPM (Recommended) After installation, the command is available globally
| Stars | 43,158 |
| Forks | 3,106 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 76.8896616610312/100 |
| Open Issues | 257 |
| Last Updated | 2026-10-01 |
| Created | 2026-05-18 |
| Platforms | go |
| Est. Tokens | ~18k |
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open-code-review is 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,. It is categorized as a Agent Tool with 43.2k GitHub stars.
open-code-review is primarily written in Go. It covers topics such as agent, agent-skills, code-review.
You can find installation instructions and usage details in the open-code-review GitHub repository at github.com/alibaba/open-code-review. The project has 43.2k stars and 3106 forks, indicating an active community.
open-code-review is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to open-code-review on Agent Skills Hub include hive, ragflow, awesome-claude-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: