A neuroscience-backed learning partner skill for AI coding agents. Retrieval, spacing, reflection, interleaving, and more — while you build software.
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<img src="assets/banner.png" alt="agentic-learning" width="720" />
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<p><strong>A learning partner skill for AI coding agents, grounded in neuroscience and philosophy.</strong></p>
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<a href="https://github.com/FavioVazquez/agentic-learninging/releases"><img src="https://img.shields.io/badge/version-1.4-1b3a57?style=flat-square" alt="version"></a>
<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-00bcd4?style=flat-square" alt="license"></a>
<a href="https://agentskills.io/specification"><img src="https://img.shields.io/badge/AgentSkills-compatible-1b3a57?style=flat-square" alt="AgentSkills compatible"></a>
<a href="https://skills.sh/FavioVazquez/agentic-learning"><img src="https://img.shields.io/badge/skills.sh-installable-00bcd4?style=flat-square" alt="skills.sh"></a>
<a href="CODE_OF_CONDUCT.md"><img src="https://img.shields.io/badge/Contributor%20Covenant-2.1-1b3a57?style=flat-square" alt="code of conduct"></a>
</p>
<p>
<a href="#installation">Install</a> •
<a href="#actions">Actions</a> •
<a href="EXAMPLES.md">Examples</a> •
<a href="CONTRIBUTING.md">Contributing</a> •
<a href="CHANGELOG.md">Changelog</a>
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## Why agentic-learning?
When AI gives you an answer, it *feels* like you've learned. It doesn't mean you have.
Researchers call this the **illusion of competence**: the fluency of reading a well-structured answer is mistaken for actual understanding. Students who re-read notes feel more confident than students who test themselves — but perform significantly worse.

`agentic-learning` makes your AI assistant resist this pattern. Instead of handing over answers, it guides you through four neuroscience-backed techniques that build **real, durable understanding** — while you build real software.
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## Actions

| Action | Trigger | What it does |
Agent that generates comprehensive documentation, API references, architecture diagrams, and developer onboarding guides from existing code.
Agent configuration for systematic bug investigation that traces issues from error logs through the codebase to root cause with suggested fixes.
Agent for integrating third-party APIs including SDK setup, type generation, error handling, retry logic, and rate limit management.
Cursor's built-in autonomous coding agent that can make multi-file edits, run terminal commands, search the codebase, and iteratively build features with minimal human intervention.
Cloud-based autonomous coding agent that runs in the background on remote sandboxed environments, handling complex multi-step tasks while you continue working.
Cursor's multi-file editing agent within Composer mode that can create, edit, and delete files across your entire project in a single conversation.