by VirtusLab · AI Tool · ★ 117
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install -g @virtuslab/tulipTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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.
| Stars | 117 |
| Forks | 8 |
| Language | TypeScript |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 68/100 |
| Last Updated | 2026-10-03 |
| Created | 2026-09-04 |
| Platforms | node |
| Est. Tokens | ~15k |
Tulip Tulip explains GitHub PRs - categorizing and prioritizing the changes - and presenting them on a web page as prose, diagrams, and code. Every code change appears somewhere in the explanation, so nothing slips past you.
Looking for a tulip alternative? If you're comparing tulip with other ai tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
An AI-powered GitHub code review tool that uses LLMs to detect high-confidence, high-impact issues—such as sec
Standalone skills for Claude Code and Codex: product discovery, architecture, implementation, technical founda
Claude Code plugin for Elixir/Phoenix/LiveView — 26 specialist agents, Iron Laws enforcement, and Tidewave MCP
CLI tool to convert GitHub repositories to text files for LLMs
Claude Code plugins for reliable AI coding. Flow-Next: plan-first workflows, Ralph autonomous mode (overnight
CoexistAI is a modular, developer-friendly research assistant framework . It enables you to build, search, sum
Explore other popular ai tool tools:
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: