course-builder（课程建设者）

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task: - Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. - Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. - Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. - Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. - When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. - Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. - Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. - Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. - Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. - Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Quick Summary

Goal: Build structured course material with learning objectives, modules, lessons, exercises, and assessments.

Workflow:

1. Define scope — Target audience, prerequisites, duration, objectives
2. Map objectives — Align to Bloom's taxonomy
3. Structure curriculum — Modules → Lessons → Exercises → Assessments
4. Develop content — Per lesson: concept, explanation, examples, exercises
5. Create assessments — Knowledge checks + final assessment
6. Review pedagogy — Progressive difficulty, prerequisite chains

Key Rules:

• Every objective mapped to Bloom's taxonomy level
• Progressive complexity (each module builds on previous)
• Use enforced template from .claude/templates/course-outline-template.md

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Course Builder

Bloom's Taxonomy Reference

Step 1: Define Learning Scope

Gather from user or research:

• Target audience — Who? What's their background?
• Prerequisites — What must they know already?
• Duration — Total hours/weeks available
• Desired outcomes — What can they DO after the course?

Step 2: Map Objectives to Bloom's

For each desired outcome, assign a Bloom's level:

• Start with lower levels (Remember, Understand)
• Progress to higher levels (Apply, Analyze, Evaluate, Create)
• Ensure at least one objective at Apply level or above

Step 3: Structure Curriculum

Organize into modules (3-8 per course):

• Each module has 2-5 lessons
• Each module has its own learning objectives
• Modules build on each other (prerequisite chain)

Step 4: Develop Lesson Content

For each lesson, provide:

1. Duration — Estimated time
2. Concept — Core idea in 1-2 sentences
3. Explanation — Theory, context, why it matters
4. Examples — 2-3 real-world illustrations
5. Exercise — Hands-on practice activity
6. Assessment — How to verify learning

Step 5: Create Assessments

Per module:

• Knowledge check — 3-5 questions covering key concepts
• Questions aligned to module's Bloom's level

Final:

• Comprehensive assessment — Covers all modules
• Mix of Bloom's levels — At least 1 question per level taught

Output

Write to docs/knowledge/courses/{descriptive-slug}.md using enforced template from .claude/templates/course-outline-template.md.

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.