course-builder

Course Builder

Overview

This skill turns raw course materials into a structured knowledge base that downstream drilling commands (/twin, /blind, /chain, /pattern, /hwmap) can query. It is domain-general — the same pipeline works for a Linear Algebra course as for a Quantum Mechanics course.

Two-phase pipeline:

Phase 1: /ingest
  materials/**/*.pdf  →  converted/**/*.md      (via pdf skill)
  materials/**/*.md   →  (copied as-is)

Phase 2: /analyze
  converted/** + materials/*.md  →  course-index/patterns.md
                                     course-index/coverage.md
                                     course-index/summary.md

When to load

• User runs /ingest or /analyze
• User mentions adding new course materials
• User asks "what does this course cover" or "what are the key techniques"
• Downstream commands (/twin, /blind, /pattern, /hwmap) need course-index/ data that doesn't exist yet

Phase 1: Ingest

Discovery

Scan materials/ recursively. Classify each file by path and extension:

• materials/lectures/*.pdf|.md — lecture notes
• materials/textbook/*.pdf|.md — textbook chapters
• materials/homework/*.pdf|.md — HW problem sets (rename for consistency: hw1.pdf, hw2.pdf, ...)
• materials/solutions/*.pdf|.md — HW solutions (hw1_sol.pdf, etc.) or worked examples

Ambiguous location (e.g., a PDF in materials/ root)? Ask user once to categorize, then remember.

Conversion

All .pdf files in materials/ go through the vision pipeline.** pdfplumber was tried as a fast path and proved unreliable on course materials — even prose-heavy textbook pages silently word-salad when they mix equations or multi-column figures. Routing everything uniformly through vision is simpler than maintaining per-category heuristics with fallbacks. Full pipeline in skills/pdf/VISION.md; the short form:

1. Load skills/pdf/SKILL.md and skills/pdf/VISION.md.
2. Render each PDF to PNG at dpi=160 (via pdf2image) into converted/<category>/_pages/<stem>/.
3. Resize all rendered PNGs to ≤1800 px on the long edge before any agent starts reading — this is the hard 2000 px many-image limit; violating it wastes entire agent runs.
4. Spawn one parallel general-purpose agent per PDF. Each agent reads its own pages sequentially (not in parallel batches — same dimension limit) and transcribes to clean LaTeX markdown ($...$ / $$...$$). Unreadable symbols get [?].
5. Write converted/<category>/<stem>.md with provenance: <!-- SOURCE: materials/<category>/<stem>.pdf, extracted <YYYY-MM-DD>, method: vision -->.
6. After all agents finish, delete the _pages/ scratch dirs.

For each .md already in materials/: copy to converted/<category>/<stem>.md unchanged with a method: passthrough provenance comment.

Idempotence

If converted/X.md exists and is newer than source, skip unless user passes --force. Log skip count.

Output

After ingest completes, print a summary table:

| Category | Converted | Skipped (already done) | Failed | |---|---|---|---| | lectures | N | M | F | | textbook | ... | ... | ... | | homework | ... | ... | ... | | solutions | ... | ... | ... |

And (in INTERFACE_LANG from .course-meta, default en): "Next: run /analyze to generate the patterns / coverage indexes."

Phase 2: Analyze

This is the core generalization. Given converted/**/*.md, produce three index files.

course-index/summary.md

Topic tree of the course. Structure:

# Course Summary

## Scope
Inferred from lecture notes: <one paragraph>.

## Topic tree
- §1 <topic>
  - §1.1 <subtopic> — covered in: lectures/ch01.md, textbook/ch01.md
  - §1.2 ...
- §2 <topic>
  ...

## Difficulty ordering (inferred from lecture progression)
Early → foundational definitions. Middle → core theorems. Late → applications/advanced.

How to build. Parse section headers (##, ###) from lecture notes, in order. Cross-reference with textbook headers. Use section numbers if present; if not, auto-number by order of appearance.

course-index/patterns.md

Recurring solution techniques extracted from HW solutions and worked examples.

How to extract. For each solution (converted/solutions/*.md and examples in lecture notes):

1. Identify the "key move" — the step where a reusable technique is applied (e.g., "integration by parts", "change of variable", "Cauchy's integral formula", "Lagrange multipliers", "separation of variables", "Green's function", "diagonalization").
2. Check whether the same move appears in 2+ other problems. If yes, it's a pattern.
3. Number patterns P1, P2, ... in order of first appearance.

Format each pattern card:

### Pk. <short name>

**Recognition signal.** <1-2 lines: what triggers this pattern>

**Move.** <1-3 lines: the operation>

**Appears in.** <HW problem IDs, textbook example numbers>

**Topic.** <§ numbers from summary.md>

Target pattern count: 15–30 (too few misses important ones; too many becomes noise). If you find <10, the course is too small or you missed patterns — re-scan. If you find >40, merge similar patterns.

course-index/coverage.md

Bidirectional map between HW/example problems and course sections.

Core premise (do not break). HW coverage is a signal of exam probability, not a completeness metric. The professor has already told you, via HW, where the exam will be drawn from: sections with heavy HW emphasis are where the exam points live. Sections with no HW are unlikely to produce problems worth drilling — they become reference-only.

Structure:

## Forward map: problem → sections

| Problem | Primary § | Secondary § | Patterns |
|---|---|---|---|
| HW1-P1 | §2.3 | §2.1 | P1, P3 |
| ...

## Reverse map: section → exam-probability (from HW density)

| § | Title | HW coverage | Exam tier |
|---|---|---|---|
| §2 | ... | HW1-P1, HW2-P3, HW3-P1 | 🔥🔥 Exam-primary |
| §1 | ... | HW1-P2, HW2-P1           | 🔥 Exam-likely |
| §4 | ... | HW3-P5                    | 🟡 Exam-possible |
| §5 | ... | —                         | ⚪ Low-risk (reference only) |

Exam tiers (based on HW problem count targeting the section):

• 🔥🔥 Exam-primary — 3+ HW instances. Highest exam probability. Drill hardest.
• 🔥 Exam-likely — 2 HW instances. High exam probability.
• 🟡 Exam-possible — 1 HW instance. Moderate probability; warm-pass review.
• ⚪ Low-risk — no HW coverage. Treat as reference; do not spend drill time here unless the user explicitly asks.

A section in the user's declared weak zones gets a trailing ⚠weak flag after its tier (e.g. ⚪ Low-risk ⚠weak). The flag never upgrades the tier — it is a drill-priority tie-breaker only.

This 🔥/⚪ + ⚠weak vocabulary is the only one. Earlier drafts used ✅✅/✅/🟡/🔴/🔴🔴 "coverage strength" markers in coverage.md; that scheme is retired — hwmap, weakmap, chain, and alt regex on the 🔥 tiers and would not see 🔴 rows.

Do not invert this. Sections with no HW are NOT "blind spots that the exam will bite" — they are sections the professor chose not to test, by omission. Drilling them steals time from exam-primary sections.

Summary of analysis output

At end of analyze, print to chat:

• Number of patterns extracted
• Number of sections in summary
• Count of 🔥🔥 / 🔥 / 🟡 / ⚪ sections
• Top 3 exam-primary sections and their recommended drills (most HW-dense first)

Domain-general hints

When analyzing, watch for common mathematical patterns (applicable broadly):

• Integration techniques (substitution, parts, partial fractions, contour)
• Linear algebra moves (diagonalization, Gram-Schmidt, rank-nullity)
• Series manipulations (telescoping, generating functions, asymptotics)
• Induction structures (strong, transfinite, well-ordering)
• Function-space methods (orthogonality, completeness, eigenexpansions)

And common physics patterns:

• Conservation laws invocation (energy, momentum, cha