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ai-workflow-course/modules/23-working-with-existing-codebases/lab/skills/map-this-repo.md
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claude fbec36cb67 feat(course): build out all 27 modules, capstone, scaffold, and conventions
Scaffold the course repo and author the full curriculum in dependency-chain
order, following the settled build decisions in handoff.md.

- Scaffold: course README, vendor-neutral AGENTS.md (dogfoods Module 5),
  _TEMPLATE.md (the fixed 9-section module shape), root .gitignore, ship config.
- Modules 1-2: reference exemplars (locked for tone/depth/lab style).
- Modules 3-27: full lessons + runnable labs, each following the template,
  respecting the chain, vendor/model-agnostic, with "feel the pain" labs.
- Module 8 hosting comparison web-researched and date-stamped (as of 2026-06-22),
  not written from memory; expansion-zone modules carry Verify-before-publish.
- Capstone: the full loop end to end on the running tasks-app example.

Lab code syntax-checked (Python/shell/YAML); every module has the 7 core
template sections.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
2026-06-22 12:18:30 -04:00

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Markdown

# Skill: Map this repo
A navigation playbook (a Module 21 skill) for orienting in a codebase you didn't write.
Point your agentic tool at this file as a skill, or paste it in as instructions. The goal is a
**read-only** mental model — no edits happen here.
## When to use
At the start of any session on an unfamiliar repo, before any change is discussed.
## Rules
- **Read only.** Do not edit, create, or delete files while mapping. No exceptions.
- **Cite real paths.** Every claim about the code must point to a file and, ideally, a line range.
If you can't cite it, say "unverified" instead of guessing.
- **Breadth before depth.** Establish the whole shape before diving into any one area.
- **No conclusions from file names alone.** A file called `auth.py` may not be where auth lives.
## Steps
1. Read the orientation pack (from `orient.py`), the README, and any `CONTRIBUTING`,
`ARCHITECTURE`, or committed AI-instructions file. Treat these as claims to verify, not truth.
2. Identify the **entry points**: how does this thing start? (CLI `main`, web server, library
exports.) Name the exact file(s).
3. Trace **one representative request/command end to end** — from entry point to where it does its
real work and back. List the files it passes through, in order.
4. Produce an **architecture summary** (max ~1 page):
- One paragraph: what this project does and how it's structured.
- A "where things live" table: concern -> directory/file.
- The build/test/run commands, confirmed against the README or CI config.
- 3-5 things that surprised you or look risky to touch.
5. List **open questions** you could not resolve from the code. Do not paper over them.
## Output
A single Markdown summary. End with: "Verified against: <list of files actually read>."