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 VILA-Lab · Agent Tool · ★ 2.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Dive-into-Claude-Code safe to install? View the security audit →
Dive into Claude Code A comprehensive source-level architectural analysis of Claude Code (v2.1.88, 1,900 TypeScript files, 512K lines of code), combined with a curated collection of community analyses, a design-space guide for agent builders, and cross-system comparisons. [!TIP] TL;DR -- Only 1.6% of Claude Code's codebase is AI decision logic. The other 98.4% is deterministic infrastructure -- permission gates, context management, tool routing, and recovery logic. The agent loop is a simple while-loop; the real engineering complexity lives in the systems around it. This repo dissects that architecture and distills it into actionable design guidance for anyone building AI agent systems. Table of Contents From
| Stars | 2,111 |
| Forks | 320 |
| Category | Agent Tool |
| Quality Score | 55.3778616208845/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-15 |
| Created | 2026-04-11 |
| Platforms | claude-code |
| Est. Tokens | ~23k |
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Dive-into-Claude-Code is A Systematic Analysis and Discussion of Claude Code for Designing Today's and Future AI Agent Systems. It is categorized as a Agent Tool with 2.1k GitHub stars.
You can find installation instructions and usage details in the Dive-into-Claude-Code GitHub repository at github.com/VILA-Lab/Dive-into-Claude-Code. The project has 2.1k stars and 320 forks, indicating an active community.
The top alternatives to Dive-into-Claude-Code on Agent Skills Hub include claude-code-guide, Unity-MCP, linkedin-mcp-server. 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: