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 melagiri · Codex Skill · ★ 73
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is code-insights safe to install? View the security audit →
Code Insights Turn your AI coding sessions into knowledge. Extract decisions, learnings, and prompt quality scores. Detect patterns. Get better at working with AI. Analyzes your sessions from Claude Code, Cursor, Codex CLI, Copilot CLI, and VS Code Copilot Chat to extract structured insights — decisions with trade-offs, learnings with root causes
| Stars | 73 |
| Forks | 17 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.7179232663789/100 |
| Open Issues | 8 |
| Last Updated | 2026-06-01 |
| Created | 2026-01-21 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~5503k |
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code-insights is Turn your AI coding sessions into knowledge.. It is categorized as a Codex Skill with 73 GitHub stars.
code-insights is primarily written in TypeScript. It covers topics such as ai-coding-analytics, claude-code, codex.
You can find installation instructions and usage details in the code-insights GitHub repository at github.com/melagiri/code-insights. The project has 73 stars and 17 forks, indicating an active community.
code-insights is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to code-insights on Agent Skills Hub include omega-memory, mcp-fusion, deterministic-agent-control-protocol. 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: