Teach Concept

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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: yugash007
• Source: yugash007/edu-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.