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cccskills
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deep-dive

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

中文简介正在准备中。当前显示原文说明。

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包含的文件(3)

  • SKILL.md3.5 KB
  • examples.md2.2 KB
  • README.md769 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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:

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

Workflow

  1. Entry Check — Ask one question to confirm starting rung. Skip confirmed rungs; jump to the frontier.
  2. Mechanism (Rung 2) — Walk through with a concrete worked trace. Require learner to narrate it back.
  3. Tradeoffs (Rung 3) — Present a decision context. Elicit learner's reasoning before canonical analysis. Include one case where the naive choice is wrong.
  4. Edge Cases (Rung 4) — Pose 2–3 edge case questions. Require learner to surface them first, then supplement. For each: symptom + fix.
  5. 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?"
  6. 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.

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