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 haddock-development · Codex Skill · ★ 379
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is claude-reflect-system safe to install? View the security audit →
Claude Code Self-Learning Skills - Reflect System 🧠 "Correct once, never again" - AI that learns from your corrections and never repeats mistakes Keywords: Claude Code, AI Learning, Self-Improving AI, LLM Skills, Machine Learning, Continuous Learning, AI Memory, Developer Tools, Code Automation, Python AI 🎯 What is this? Reflect is a revolutionary self-learning AI system for Claude Code that enables automatic skill improvement through user corrections. Unlike traditional AI coding assistants that forget everything between sessions, Reflect creates permanent learning from your feedback. Perfect for: Developers, AI engineers, automation enthusiasts, teams using Claude Code, anyone tired of repeating corrections to AI assistants. Use Cases: 🔧 Development workflows - Teach Claude your preferred tools (uv, pytest,
| Stars | 379 |
| Forks | 43 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 70.0580662541106/100 |
| Last Updated | 2026-01-17 |
| Created | 2026-01-05 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~445k |
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claude-reflect-system is Continual Learning & Self-improving skills system for Claude Code - learn from corrections, never repeat mistakes. It is categorized as a Codex Skill with 379 GitHub stars.
claude-reflect-system is primarily written in Python. It covers topics such as ai, claude-code, claude-code-skills.
You can find installation instructions and usage details in the claude-reflect-system GitHub repository at github.com/haddock-development/claude-reflect-system. The project has 379 stars and 43 forks, indicating an active community.
The top alternatives to claude-reflect-system on Agent Skills Hub include smart-ralph, getspecstory, awesome-claude-skills. 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: