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## The task

> Read the extracted course content in `{{nd_id}}/transcripts/` and write one markdown summary
> per course into `{{nd_id}}/`, plus a `README.md` index.
>
> Follow the recipe  detailed below. 
> Build digests first, write each course doc immediately after reading that course,
> and keep the frameworks, formulas, worked exercise
> solutions, glossaries and project requirements rather than just listing topics.

---

## Step-by-step

### 1. Build reading digests

The raw files are large (~200–300 KB per course) because video transcripts dominate. In this
curriculum **every video is paired with a `[TEXT - Summary]` or `[TEXT - Recap]` atom that restates
its content**, so stripping transcript bodies cuts the reading volume by ~65% while losing almost
nothing. Keep the video *titles* so the structure stays visible.

```python
import glob, os
DIGEST = "<SCRATCHPAD>/digest"
os.makedirs(DIGEST, exist_ok=True)
for f in sorted(glob.glob("transcripts/*.txt")):
    out = []
    for line in open(f):
        out.append(line.split("]")[0] + "]\n" if line.startswith("[VIDEO") else line)
    open(os.path.join(DIGEST, os.path.basename(f)), "w").write("".join(out))
    print(f, len(out))
```

Write digests to the **scratchpad**, not the project — they're intermediate.

> Spot-check one course against its full transcript before trusting the digest. If a course teaches
> primarily through video with no paired text recaps, read its full file instead.

### 2. Read each digest

Use the **Read tool with `offset`/`limit`**, not `cat` — Bash persists large output to a file
instead of putting it in context, which is the opposite of what you want here. Expect ~2 pages per
course at ~25k tokens each.

### 3. Write each course doc immediately

**Write course N's markdown before reading course N+1.** Context may be compacted partway through a
long extraction; writing as you go means the deliverable is already on disk if that happens.

### 4. Write the README index last

Table of courses (number, title, part key, lesson count, concept count, project), a short section on
how the courses connect, the instructors, and a description of the `transcripts/` format so the raw
files are usable on their own.

---

## What to put in a course doc

### Header block

Nanodegree and course key · instructor with their background · lesson/concept counts · a relative
link to the raw transcript · the course blurb.

### Body

- **Course learning objectives** verbatim from the course — they're the contract for what the course
  claims to teach.
- **One section per lesson**, in order, with the lesson's own summary line as the section subtitle.
- **A running-case-study callout** if the course has one. These courses each pin every exercise to a
  single scenario (a fitness tracker, a grocery chain, a party app); naming it up front makes the
  rest of the doc cohere.
- **Glossary tables per lesson**, as the courses provide them.
- **A project section** with scenario options, required deliverables, and grading criteria.

### What to keep

Keep the things someone would otherwise have to re-watch the course to recover:

- **Formulas and their worked numbers** — `TAM = ARPU × potential users`, and the actual top-down vs.
  bottoms-up calculations that produce different answers.
- **Framework tables** — HEART goals/signals/metrics, the six Thinking Hats in order, priority and
  severity scales, prototyping method trade-offs.
- **Model answers to exercises.** The instructor's solution is often the most useful content on the
  page. Include the reasoning, not just the answer.
- **Contrastive examples.** Good vs. bad, brittle vs. credible, ✅ vs. ❌ — these carry the actual
  judgment being taught and compress well into a blockquote pair.
- **Memorable direct quotes**, sparingly, as blockquotes. One or two per lesson.
- **Links** to templates, further reading, and tools — rewrite the source's inline `<a href>` HTML as
  markdown.

### What to drop

Instructor self-introductions beyond one line, prerequisite/tooling boilerplate (compress to a
line), Udacity platform mechanics, quiz atoms with no recoverable content, and image placeholders.

### Style

- Tables for anything with parallel structure — comparisons, glossaries, phase lists, priority
  scales. Most of this content is inherently tabular.
- Bold the term being defined at the start of a bullet.
- Blockquotes for direct quotes and for the one-line rules worth remembering.
- Convert source HTML tables to markdown tables; drop the `index-module--*` wrapper classes.
- Don't pad. A short course gets a short doc — the welcome/onboarding course doesn't need the same
  treatment as a 100-concept course, and two thin courses can share one file.

---

## Reference output

The nd036 run produced ~128 KB of summaries from 1.04 MB of source:

| Doc | Size | Source |
|---|---|---|
| `01-welcome-to-the-nanodegree-program.md` | 8 KB | 25 KB (+ the 8 KB congratulations course) |
| `02-product-strategy-for-product-managers.md` | 34 KB | 286 KB |
| `03-product-design.md` | 29 KB | 306 KB |
| `04-product-development.md` | 22 KB | 217 KB |
| `05-product-launch.md` | 31 KB | 194 KB |
| `README.md` | 4 KB | — |

Roughly a 9:1 compression, with formulas, worked examples and project requirements preserved.

---

## Gotchas

- **`cat`-ing a digest persists it to a file instead of loading it.** Use Read.
- **The Read tool caps a page at ~25k tokens** and tells you the offset for the next page. Don't
  answer from a partial page — a lesson's project requirements are usually at the very end of the
  file.
- **Some `[TEXT]` atoms contain raw HTML tables.** They're worth converting; they usually hold the
  densest content in the lesson (priority scales, comparison matrices, worked KPI tables).
- **Check for cross-course reuse.** Later courses re-teach earlier material (TAM appears in both
  Product Strategy and Product Launch). Summarise it fully once and reference it the second time.
- **A course's real spine is often its exercises**, not its lecture videos. If a doc reads as a list
  of topics rather than a set of methods with worked examples, it's missing the exercise solutions.

Detected placeholders 1

Placeholder	Hint shown on the form optional	Multiline
`nd_id`