jev-review: Local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev

by NiazMorshed2007 · MCP Server · ★ 248

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install jev-review

npx plugins add NiazMorshed2007/jev-review
npx plugins add NiazMorshed2007/jev-review --target claude-code

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-pluginai-agentsclaude-codecode-reviewcodexcoding-agentscursordeveloper-toolsjevlocal-first

Quick Facts

Stars248
Forks25
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score70/100
Open Issues6
Last Updated2026-09-17
Created2026-09-17
Platformsclaude-code, codex, mcp, node
Est. Tokens~17k

About jev-review

Jev Review Continuous software-quality review for AI coding agents, powered by Jev. Quick start · Client setup · Quality dimensions · Security Jev Review runs as a local MCP server and gives Claude Code, Codex, Cursor, and OpenCode structured quality scores while they work.

Compatible Skills

These tools work well together with jev-review for enhanced workflows:

  • garcon — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (62%)
  • agentplane — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse MCP Server tools