Files
ai-workflow-course/modules/23-working-with-existing-codebases/lab/skills/map-this-repo.md
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claude f925fd9645 fix(M7-27+capstone): apply AI-drives-git reframe, lesson=theory, de-slop course-wide
Phase 2 sweep — all modules are post-pivot, so the learner directs the AI agent
(Claude Code as the worked example) to do the git/setup work and verifies, instead
of typing commands by hand; no re-teaching basics. Lesson sections are theory with
example output; all execution lives in the labs. De-slopped ("prose" etc. gone
course-wide, em-dash density thinned). /path/to placeholders -> ~/ai-workflow-course.

Every deliberate teaching device verified intact: M10 ai-change.patch trap,
M12 bad-clear-snippet, M13/M27 planted pending_count bug, M15 secret+typosquat+MD5,
M18 BREAK=1, M21 absent-.gitignore, M22 poisoned skill, M24 no-op patch, M25 --simulate.
Labs compile/parse (py/sh/yaml/json); no junk.

Closes #83
Closes #86
Closes #89

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
2026-06-22 21:58:17 -04:00

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1.8 KiB
Markdown

# Skill: Map this repo
A navigation playbook (a Module 21 skill) for orienting in a codebase you didn't write.
Point Claude Code (or sub your own agent) 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 going deep on 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>."