Deep dive

Skill yugash007/edu-agent-skills/skills/teaching/deep-dive

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 deep-dive

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Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.

SKILL.md

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Purpose

Take a learner from surface understanding to genuine depth on a single concept. Assumes the learner has a working mental model and pushes into mechanism, failure modes, tradeoffs, and production implications. Exit condition: learner can reason about the concept in novel, constrained contexts.

Activation

Learner asks to go deeper on a known concept. check-understanding confirms surface competence but weak mechanism knowledge. Interview prep or architectural decision needs depth.
Skip if: beginner encountering concept for the first time → teach-concept. Needs immediate practical help → build-with-me/debug-teacher. Has a misconception → misconception-detector first.
Routing: confirm current level with a quick probe before starting. Pair with challenge-generator at the end for advanced application.

Inputs

Target concept, confirmed current understanding level, motivating context (interview/architecture/debugging), known gaps or questions.

Depth Ladder

Five rungs, each confirmed before ascending:

Surface — Definition and intuition → "State this in one sentence."
Mechanism — Step-by-step how it works → "Trace a concrete execution."
Tradeoffs — When it works vs. doesn't → "What would you choose instead, and why?"
Edge Cases — Boundaries and failures → "What breaks this?"
Production — Real-world tuning, monitoring, debugging → "How would you debug this at 3am?"

Workflow

Entry Check — Ask one question to confirm starting rung. Skip confirmed rungs; jump to the frontier.
Mechanism (Rung 2) — Walk through with a concrete worked trace. Require learner to narrate it back.
Tradeoffs (Rung 3) — Present a decision context. Elicit learner's reasoning before canonical analysis. Include one case where the naive choice is wrong.
Edge Cases (Rung 4) — Pose 2–3 edge case questions. Require learner to surface them first, then supplement. For each: symptom + fix.
Production (Rung 5) — Observability, performance under load, tuning knobs, known failure patterns. Ground in a realistic system. Ask: "If this broke at 3am, what's your investigation sequence?"
Exit Synthesis — Ask learner to produce a one-paragraph explanation for someone who just learned the basics.

Rules

DO: confirm each rung before ascending — require exit question answers.
DO: require learner narration at Rung 2, learner tradeoff reasoning at Rung 3, learner-surfaced edge cases at Rung 4.
DO: use realistic scale at Rung 5 — not toy numbers.
DON'T: re-teach confirmed surface level — skip to the frontier.
DON'T: lecture all five rungs without learner participation at every rung.
DON'T: end without learner producing an exit synthesis paragraph.

Output

Responses should contain: context (concept + starting rung + motivation), depth ladder progress, current rung exploration, checkpoint question, and exit synthesis prompt. Format naturally per rung.

Checklist

Entry level confirmed; confirmed rungs skipped.
Learner participation required at every rung.
Rung 5 reached for intermediate+ learners.
Exit synthesis produced by learner.