Teaches any topic through a stateful learning workspace with mission-driven lessons, retrieval practice, and cited resources. Activates on 'teach me' or /teach.
How this skill is triggered — by the user, by Claude, or both
Slash command
/v1truv1us-ai-eng-system:teach What would you like to learn about?What would you like to learn about?The summary Claude sees in its skill listing — used to decide when to auto-load this skill
The user wants to learn something. This is a **stateful, multi-session** request — progress persists in the workspace directory.
The user wants to learn something. This is a stateful, multi-session request — progress persists in the workspace directory.
Related skills (do not conflate):
create-learning-path — week-by-week roadmap and milestones without a lesson workspaceknowledge-architecture — team knowledge base for a product/domain, not personal tutoringknowledge-capture — document a solved problem after the factMISSION.md, RESOURCES.md, GLOSSARY.md, NOTES.md, learning-records/, lessons/, reference/.MISSION.md is missing or empty, interview the user on why they want to learn this before teaching content. Use MISSION-FORMAT.md.RESOURCES.md is thin, search the web before teaching — do not rely on parametric knowledge alone (see Research below).State lives in the workspace directory:
| Path | Purpose |
|---|---|
MISSION.md | Why the user is learning; grounds every decision. MISSION-FORMAT.md |
RESOURCES.md | Curated high-trust sources. RESOURCES-FORMAT.md |
GLOSSARY.md | Canonical terminology. GLOSSARY-FORMAT.md |
NOTES.md | User teaching preferences and scratch notes |
learning-records/*.md | Durable insights and prior knowledge (like ADRs). LEARNING-RECORD-FORMAT.md |
lessons/* | Self-contained lessons (primary deliverable) |
reference/* | Compressed cheat sheets and quick-reference docs |
Create directories lazily when first needed.
Deep learning needs three things:
Before RESOURCES.md is well-populated, prioritize finding trustworthy resources. Never trust parametric knowledge as the primary source.
Some topics are knowledge-heavy (theoretical physics); others are skill-heavy (yoga, programming). Calibrate lesson design accordingly.
Design for storage strength via desirable difficulty:
Ground teaching in external sources. Use whichever search tools the harness exposes:
web_search, web_research, intelli_research, WebSearch, WebFetch, or MCP search toolsWorkflow:
RESOURCES.md with annotations (what it covers, when to use it).## Gaps section in RESOURCES.md.Do not present unsourced factual claims as settled when teaching a new topic.
The main deliverable. One self-contained file per lesson, numbered sequentially:
lessons/0001-<dash-case-name>.html (preferred)lessons/0001-<dash-case-name>.md (acceptable when HTML is impractical)Beautiful, readable, print-friendly layout (Tufte-inspired). Include:
MISSION.md and zone of proximal developmentreference/ docsSelf-contained HTML with embedded CSS is fine (no build step required).
Use when the harness or environment makes HTML awkward. Same content requirements; use relative links to other lessons and references.
GLOSSARY.md when the user can use a term correctly.# macOS
open "lessons/0001-example.html"
# Linux
xdg-open "lessons/0001-example.html" 2>/dev/null || true
If open fails, tell the user the path.
Every lesson traces to MISSION.md. If the mission is vague, interview before teaching.
Mission changes are normal. Update MISSION.md, add a learning record, and confirm with the user before changing direction.
Each lesson should challenge the user just enough.
If they name a specific topic, teach that (if mission-aligned). Otherwise:
learning-records/ and GLOSSARY.mdMISSION.mdLessons center on a skill the user will practice. Include only knowledge required for that skill — teach knowledge first, then practice via a tight feedback loop.
For knowledge acquisition, lower difficulty — working memory is scarce.
For skill acquisition, use difficulty — effortful retrieval builds storage strength.
Interactive patterns (pick what fits the harness):
Feedback loop: as tight as possible; immediate and specific.
Quizzes: when using multiple choice, keep answer options the same length (words and characters if possible) so formatting does not leak the answer.
When the harness has no browser, run quizzes and exercises in the conversation and score answers explicitly.
Wisdom comes from practice outside the lesson. When questions need lived experience:
RESOURCES.mdRESOURCES.mdRESOURCES.mdWhile teaching, build reference/ artifacts — compressed units reused across lessons:
Lessons are visited once; references are revisited often. Keep references scannable.
Once GLOSSARY.md exists, use its terms consistently in all lessons.
Record user preferences here: pacing, format (HTML vs markdown), depth, topics to avoid, community preferences. Read before designing new lessons.
At the start of each session:
MISSION.md, latest learning records, and NOTES.mdAt the end of a session, state what was covered and what file(s) were created or updated.
npx claudepluginhub v1truv1us/ai-eng-systemCreates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.
2plugins reuse this skill
First indexed Jul 8, 2026