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A Claude or Codex skill for deliberate skill development during AI-assisted coding
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
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.
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 skilllearning-opportunities-auto — optional post-commit prompting hookorient — repo orientation generatorThis 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.
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.
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
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:
The techniques in SKILL.md are designed to counteract these risks by reintroducing:
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.
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).
Two suppression conditions are currently suggested which can be adapted to your workflow needs. Claude will not prompt learning opportunities when:
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.
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:
The measures are free and open access under a CC-BY-SA 4.0 license. For the full set of measures and design notes, see the AI Skill Threat open access measures supplement and the Developer Thriving open access measures supplement.
This skill can be significantly refined and adapted. You might want to:
This skill was developed based on learning science and informed by multiple qualitative interviews with software development professionals about their concerns around agentic coding, as part of my open science empirical evidence about developer thriving and skill development in AI-assisted workflows. In my research with thousands of developers, I've also found that a strong value and commitment to learning predicts that developers feel less threat, worry and anxiety when imagining needing to adjust to agentic coding. Learning culture also associates with increases in team effectiveness overall, not just individual productivity.
I'd love to know if you enjoy this and what you learn! Sharing open science resources helps researchers like me create more things to help software teams. I always appreciate a shout-out or a share in public, which helps more people learn about the psychology of software teams. Get updates and access to more of the psychology of software teams at my newsletter: Fight for the Human
Learning-Opportunities:
Dr. Cat Hicks
I'm a psychological scientist studying software teams and technology work, an author, a public speaker, a research architect, and an empirical interventionist who builds radical research teams that put answers behind questions everyone is asking but few people are gathering real evidence about.
Orient:
Dr. Michael Mullarkey
I'm a machine learning engineer who used to be a therapist + social science researcher. I'm thinking a lot about how to leverage agentic AI to help people learn skills, see blendtutor for an
Selected from shared topics, language and repository description—not editorial ratings.
Graphify-Labs /
AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph.
100/100 healthAntigmaLabs /
Ghost in your shell. Ante is a self-contained agent harness with a highly optimized core. It works like Claude Code or Codex, with none of their dependencies or model constraints.
74/100 healthzebbern /
A Discord bot that brings Claude Code to your channels so you can chat, run shell/git, and manage branches. Access from any local, VM, or Docker instance with full capebilty of claude code!
84/100 healthAryia-Behroziuan /
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Archived (PDF) from the original on 4 September 2013. Retrieved 4 June 2013 – via msu.edu. "Applications of AI". www-formal.stanford.edu. Archived from the original on 28 August 2016. Retrieved 25 September 2016. Further reading DH Author, 'Why Are There Still So Many Jobs? The History and Future of Workplace Automation' (2015) 29(3) Journal of Economic Perspectives 3. Boden, Margaret, Mind As Machine, Oxford University Press, 2006. Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.) Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65. Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press. Koch, Christof, "Proust among the Machines", Scientific American, vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because "[e]ven the most sophisticated brain simulations are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient [is important] for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)
59/100 healthhenrikekblad /
Multi-agent remote dashboard for Claude Code, Copilot, and Codex. Approve permissions, answer questions, and monitor token usage in real-time from a GeekMagic screen, Android widget, GNOME Shell, or VS Code.
76/100 healthsaadnvd1 /
Agentic terminal workspace for AI-assisted coding. Run Claude Code, Aider, or OpenCode alongside your shell in project-based layouts.
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