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Teach concept

Skill yugash007/edu-agent-skills/skills/teaching/teach-concept

Reusable educational skills for AI coding agents. Turn agents (Gemini CLI, Claude Code, Cursor, etc.) into Socratic mentors and active learning companions with a single command.

Install
npx -y skills add yugash007/edu-agent-skills --skill teach-concept

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

SKILL.md

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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.

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