https-deeplearning-ai Feature Spec
Workflow
1. Find the next phase
Read specs/roadmap.md. The next phase is the first section whose items are all [ ]. Note its name to derive the branch and directory name.
2. Create the branch
git checkout -b phase-N-<kebab-name>
3. Interview the user — BEFORE writing any files
Use AskUserQuestion with exactly 3 questions in one call:
| Header | Question focus |
|---|---|
| Scope | What the feature collects, exposes, or does — fields, page behaviour, data shape |
| Decisions | Key implementation choices — storage, visibility, validation, UX pattern |
| Context | Tone, constraints, or anything shaping the spec — satirical copy level, stack limits, open questions |
Do not write any files until the user has answered all three questions.
4. Read guidance files
Read specs/mission.md and specs/tech-stack.md before drafting.
5. Create the spec directory
Name: specs/YYYY-MM-DD-<feature-name>/ using today's date.
requirements.md
- Scope section: what is and is not included; field/data table if applicable
- Decisions section: choices made and why (draw from user answers)
- Context section: tone rules, stack pointers, existing patterns to follow
plan.md
- Numbered task groups: Database → Components → Page & Route → Navigation → Tests
- Each group has numbered sub-tasks; groups should be independently implementable
validation.md
- Automated:
npm testandnpm run typecheckpass; specific assertions required - Manual: browser walkthrough, form behaviour, edge cases
- Tone check if the feature has user-facing copy
- Definition of done
Stack constraints
- Hono 4.x + Hono JSX (SSR) + SQLite via
better-sqlite3+ Vitest — no new dependencies without user approval - POST forms use POST/redirect/GET
- No client-side JS
- Migrations in
src/db/migrations/as numbered.sqlfiles - Satirical tone in label/placeholder copy only, not in structure or logic