의도적 실력 향상을 위한 코딩 에이전트 학습 스킬

Learning Opportunities: A Claude Code and Codex Skill for Deliberate Skill Development Build your expertise, not just your projects. This skill uses an adaptive "dynamic textbook" approach to help you integrate science-based expertise building exercises while doing agentic coding. When you complete architectural work (new files, schema changes, refactors), Claude offers optional 10-15 minute learning exercises grounded in evidence-based learning science. The exercises use techniques like prediction, generation, retrieval practice, and spaced repetition to provide you with semi-worked examples from across your own project work. Pairs well with Learning-Goal , a skill that guides you through semi-structured, interactive learning goal-setting using the technique of Mental Contrasting with Implementation Intentions (MCII), an evidence-based exercise. Installation Codex This repository is also a Codex plugin marketplace. To add it from GitHub: codex plugin marketplace add https://github.com/DrCatHicks/learning-opportunities.git For local development from a checkout: codex plugin marketplace add /path/to/learning-opportunities The Codex marketplace includes: learning-opportunities — the core learning exercise skill learning-opportunities-auto — optional post-commit prompting hook orient — repo orientation generator Claude Code This repository is a Claude Code plugin marketplace . To install: Add the marketplace: /plugin marketplace add https://github.com/DrCatHicks/learning-opportunities.git Install the plugin: /plugin install learning-opportunities@learning-opportunities Restart Claude Code to activate For more on Claude Code plugins, see the plugin documentation . Automatic Prompting (Optional) Linux and macOS users can install learning-opportunities-auto alongside learning-opportunities to have Claude automatically consider offering an exercise after each git commit. Windows users can use it too — a little setup is required . Get Repo Orientation Lessons (Optional) If you're learning a new repo you can create an orientation.md file with suggested lessons using the orient skill. The orientation approach applies strategies from empirical research on program comprehension and codebase navigation — including how expert developers sample codebases strategically rather than reading exhaustively. See the orient bibliography for the full source list. Install the orient plugin: /plugin install orient@learning-opportunities Navigate to the repo you want to orient yourself to, and call the orient skill either as default /orient Or using Simon Willison's showboat tool /orient showboat Then call learning-opportunities with the orient argument to get offered two lessons that will orient you to core features of the repo /learning-opportunities orient Why You Might Want to Experiment with This Skill AI coding tools can create specific risks for decreasing users' engagement in learning by introducing inefficient learning habits. These effects can be anticipated based on several foundational science-backed learning principles: Generation effect: Accepting generated code and decreasing generating one's own code can skip the active processing that builds understanding. Fluency illusion: Clean generated code can be perceived as more understood than it truly is; likewise, easily accessible knowledge from search can promote the illusion of knowledge and the illusion of more complete mental models. Spacing effect: Machine velocity can push users toward constant cramming and long production sessions without the cadence, reflection and spacing of learning that leads to longer-term retention. Metacognition: Fast workflows often don't leave room to monitor learning and develop schema representation as well as a user's sense of their own level of relative expertise and knowledge when working with novel technology. Testing and retrieval: Agentic models push toward giving complete answers, which could result in users taking fewer opportunities to benefit from self-testing and retrieving specific components of new knowledge, which strengthens retention. The techniques in SKILL.md are designed to counteract these risks by reintroducing: Active generation (predictions, explanations, sketches) Retrieval practice (check-ins, teach-it-back, self-testing) Deliberate pauses (spacing, reflection) Explicit metacognition (self-assessment, gap identification) This skill interrupts that pattern by reminding you to consider investing in reflection and learning. It introduces a different "mode" of interacting with Claude, which will intentionally feel different than highly fluent and fast agentic coding in the service of helping you reflect and explore your generated work. This skill may be particularly useful for users who are experimenting with developing discrete projects with agentic coding that involve multiple unfamiliar languages, techniques, or architectural patterns. How It Works After you complete significant work (which you can self-define, but I've suggested: creating new files or modules, database schema changes, architectural decisions or refactors, implementing unfamiliar patterns, any work where the user asked "why" questions during development. The key idea is to find a moment in your personal flow where a learning opportunity is most beneficial) Claude will ask: "Would you like to do a quick learning exercise on [topic]? About 10-15 minutes." If you accept, Claude runs you through an interactive exercise. A key design principle: Claude pauses and waits for your input rather than answering its own questions. This can feel frustrating, but this pushes against Claude's default to always provide the full answer and encourages your own mental effort and learning. You may encounter and need to design against Claude's defaults to provide the complete answer; please feel free let me know if you find gotchas or conflicts in your own workflow that you think will generalize to others so that I can incorporate in the Skill to improve this (e.g., I learned we needed to suppress prompt suggestions). Exercise Types Prediction → Observation → Reflection : What do you expect to happen? Now let's see. What surprised you? Generation → Comparison : Sketch how you'd approach this before seeing the implementation Trace the path : Walk through execution step by step, predicting each transition Debug this : What would go wrong here, and why? Teach it back : Explain this component as if onboarding a new developer Retrieval check-in : At the start of a session, what do you remember from last time? Will Not Suggest Learning Opportunities When... Two suppression conditions are currently suggested which can be adapted to your workflow needs. Claude will not prompt learning opportunities when: You've already declined an exercise this session You've completed 2 exercises this session The Science Behind It & Resources The exercises draw from well-established findings in learning science, along with substantive research on typical learner misconceptions. Design choices also draw from multiple qualitative interviews with developers about what aspects of rapid agentic coding they find most frustrating, worrisome, or difficult when it comes to their own learning and development. See PRINCIPLES.md for detailed explanations which can help you develop new exercise types or simply learn more about strategies to help your own learning. Measure This, A Lightweight Playbook for Making A Team Experiment Visible If you're trying this skill with your team, you can layer on a lightweight pre/post measurement to make the experiment more visible and valued in your organization. MEASURE-THIS.md is a companion playbook that includes: A curated set of validated survey items from our peer-reviewed research on developer thriving and AI skill threat, ready to copy into a Google Form or team channel Guidance on what to do (and not do) with your results including why variance matters as much as averages, and some guardrails on how I t