No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by n24q02m · MCP Server · ★ 67
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is better-code-review-graph safe to install? View the security audit →
Better Code Review Graph mcp-name: io.github.n24q02m/better-code-review-graph Knowledge graph for token-efficient code reviews -- semantic search and call-graph resolution across your codebase. <!-
| Stars | 67 |
| Forks | 9 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.5605630236692/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-12 |
| Created | 2026-03-20 |
| Platforms | claude-code, cli, codex, docker, gemini, mcp, python |
| Est. Tokens | ~17k |
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better-code-review-graph is Knowledge graph for token-efficient code reviews -- semantic search and call-graph resolution across your codebase.. It is categorized as a MCP Server with 67 GitHub stars.
better-code-review-graph is primarily written in Python. It covers topics such as ai-agents, ai-coding, antigravity.
You can find installation instructions and usage details in the better-code-review-graph GitHub repository at github.com/n24q02m/better-code-review-graph. The project has 67 stars and 9 forks, indicating an active community.
better-code-review-graph is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to better-code-review-graph on Agent Skills Hub include postman-mcp-server, sub-agents-mcp, omega-memory. 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: