teach-concept

Use when explaining a technical concept and adapting depth, pacing, and examples to the learner's readiness.

SKILL.md

GitHub

Purpose

Explain technical concepts correctly, incrementally, and adaptively. Build from definition to application in one teaching arc.

Activation

  • User asks for explanation of a concept, pattern, algorithm, or system behavior.
  • User asks "why" or "how" questions requiring conceptual grounding.
  • User is struggling to connect theory to code.
  • Skip if: user wants only a command/answer with no teaching, or explicitly declines teaching.
  • Routing: run repo-understand first if repo context exists but isn't mapped. Hand off to check-understanding after major explanations.

Inputs

  • Target concept, learner level signals, prior conversation context, relevant repo/project context.

Workflow

  1. Calibrate — Infer learner level from prompt and prior turns. State assumptions; keep first explanation conservative.
  2. Anchor — Give a short definition and one core intuition sentence.
  3. Concrete Example — One practical code or system example before any abstraction. Prefer the learner's current project/repo.
  4. Deepen — Add one layer of complexity at a time. Introduce terminology only when needed. Explain tradeoffs and failure cases, not just happy path.
  5. Recall Check — Ask a reasoning question requiring explanation, not repetition. If confusion appears, simplify and reframe with a new analogy.
  6. Transfer — Give one small implementation or debugging task to apply the concept.

Rules

  • DO: one core idea per step; example before abstraction; verify understanding before escalating.
  • DO: ground examples in the learner's project/repo when possible.
  • DO: explain tradeoffs and failure cases alongside happy-path behavior.
  • DON'T: dump multiple concepts at once — cap each response to one primary concept plus one extension.
  • DON'T: end without an active-recall question and a transfer task.
  • DON'T: skip the concrete example — no abstract-only teaching.
  • DON'T: keep teaching at the same level if learner asks repeated clarifications — run a calibration checkpoint and reduce depth.

Output

Responses should contain: context (concept + assumed level), explanation (definition + intuition), example (concrete code/system), checkpoint (reasoning question), and next step (application exercise). Format naturally — don't force rigid templates.

Checklist

  • Learner level assumption is explicit.
  • At least one concrete example included before abstraction.
  • Active-recall checkpoint present.
  • Transfer task assigned.