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Teach the user a new skill or concept over multiple sessions, using this workspace as a structured learning space.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | teach |
| description | Teach the user a new skill or concept over multiple sessions, using this workspace as a structured learning space. |
| disable-model-invocation | true |
| argument-hint | What would you like to learn about? |
The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.
Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:
MISSION.md: A document capturing the reason the user is interested in the topic. This should be used to ground all teaching. Use the format in MISSION-FORMAT.md../reference/*.html: A directory of reference materials. These are the compressed learnings
from the lessons - cheat sheets, reference algorithms, syntax, yoga poses, glossaries. They are
the raw units of learning. They should be beautiful documents which print out well, and are
designed for quick reference.RESOURCES.md: A list of resources which can be explored to ground your teaching in
contextual knowledge, or to acquire knowledge and wisdom. Use the format in
RESOURCES-FORMAT.md../learning-records/*.md: A directory of learning records, which capture what the user has
learned. These are loosely equivalent to architectural decision records in software
development - they capture non-obvious lessons and key insights that may need to be revised
later, or drive future sessions. These should be used to calculate the zone of proximal
development. They are titled 0001-<dash-case-name>.md, where the number increments each time.
Use the format in LEARNING-RECORD-FORMAT.md../lessons/*.html: A directory of lessons. A lesson is a single, self-contained HTML
output that teaches one tightly-scoped thing tied to the mission. This is the primary unit of
teaching in this workspace../assets/*: Reusable components shared across lessons. See Assets.NOTES.md: A scratchpad for you to jot down user preferences, or working notes.See REFERENCE.md for the pedagogical model — Knowledge, Skills, Wisdom, and the distinction between fluency and storage strength.
A lesson is the main thing you produce — the unit in which knowledge and skills reach the user.
Each lesson is one self-contained HTML file, saved to ./lessons/ and titled
0001-<dash-case-name>.html where the number increments each time.
A lesson should be beautiful — clean, readable typography and layout — since the user will return to these later to review. Think Tufte.
The lesson should be short, and completable very quickly. Learners' working memory is very small, and we need to stay within it. But each lesson should give the user a single tangible win that they can build on. It should be directly tied to the mission, and should be in the user's zone of proximal development.
If possible, open the lesson file for the user by running a CLI command.
Each lesson should link via HTML anchors to other lessons and reference documents.
Each lesson should recommend a primary source for the user to read or watch. This should be the most high-quality, high-trust resource you found on the topic.
Each lesson should contain a reminder to ask followup questions to the agent. The agent is their teacher, and can assist with anything that's unclear.
Lessons are built from reusable components, stored in ./assets/: stylesheets, quiz widgets, simulators, diagram helpers — anything a second lesson could reuse.
Reuse is the default, not the exception. Before authoring a lesson, read ./assets/ and build
from the components already there. When a lesson needs something new and reusable, write it as a
component in ./assets/ and link to it — never inline code a future lesson would duplicate.
A shared stylesheet is the first component every workspace earns: every lesson links it, so the lessons look like one consistent course rather than a pile of one-offs. As the workspace grows, so should the component library.
Every lesson should be tied into the mission - the reason that the user is interested in learning about the topic.
If the user is unclear about the mission, or the MISSION.md is not populated, your first job
should be to question the user on why they want to learn this.
Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Lessons will feel too abstract. You will have no way of judging what the user should do next.
Missions may change as the user develops more skills and knowledge. This is normal - make sure to
update the MISSION.md and add a learning record to capture the change. Confirm with the user
before changing the mission.
Each lesson, the user should always feel as if they are being challenged 'just enough'.
The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:
learning-recordsLessons should be designed around a skill the user is going to learn. The knowledge in the lesson should be only what's required to acquire that skill. You teach the knowledge first, then get the user to practice the skills via an interactive feedback loop.
Knowledge should first be gathered from trusted resources. Use RESOURCES.md to keep track of
them. Lessons should be littered with citations - links to external resources to back up any
claim made. This increases the trustworthiness of the lesson.
For acquiring knowledge, difficulty is the enemy. It eats working memory you need for understanding.
If knowledge is all about acquisition, skills are about durability and flexibility. Make the knowledge stick.
For skill acquisition, difficulty is the tool. Effortful retrieval is what builds storage strength. Skills should be taught through interactive lessons. There are several tools at your disposal:
Each of these should be based on a feedback loop, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately - and ideally automatically.
For quizzes, each answer should be exactly the same number of words (and characters, if possible). Don't give the user any clues about the answer through formatting.
Wisdom comes from true real-world interaction - testing your skills outside the learning environment.
When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a community.
A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.
You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.
While creating lessons, you should also create reference documents. Lessons can reference these documents - they are useful for tracking raw units of knowledge useful across lessons.
Lessons will rarely be revisited later - reference documents will be. They should be the compressed essence of the lesson, in a format designed for quick reference.
Some learning topics lend themselves to reference:
Glossaries, in particular, are an essential reference. Once one is created, it should be adhered to in every lesson.
NOTES.mdThe user will sometimes express preferences of how they want to be taught, or things you should keep in mind. This is the place to record those preferences, so you can refer back to them when designing lessons or working with the user.
The teaching session is complete when:
Run a long-form goal with judge-verified completion.
Creates or updates repository specs, acceptance criteria, durable decisions, and documentation for new work. Use when adding a feature or defining an ambiguous slice, when making architectural decisions or changing contracts (game state, leaderboard, audio, settings), or when recording durable context (decisions, glossary, conventions) for future engineers and agents.
Audits the MEMORYBLOX codebase for dead TypeScript code, orphan files, and unused symbols, triages findings into live dead code or false positives, and optionally removes provably dead code with focused validation. Use when the user asks for a dead-code scan, unused-code audit, orphan-file scan, unused-symbol triage, or cleanup from analyzer findings.
Remove AI-generated code slop from the current branch diff. Use when the user asks to deslop, clean up AI slop, remove unnecessary comments, strip defensive cruft, or flatten needless nesting.
Runs a disciplined diagnosis loop for hard bugs and performance regressions. Reproduce, minimise, hypothesise, instrument, fix, and regression-test. Use when the user says "diagnose this", the bug is flaky or not yet reproducible, root cause is unknown after triage, or a performance regression needs measurement before fixing.
Extremely strict maintainability review hunting code-judo restructurings — abstraction quality, the 1k-line rule, and spaghetti growth. Use when asked to review code, do a code review, audit changes, check for issues, or review uncommitted/staged changes. Covers diff-first review of pending changes and broader codebase audit.