repo-atlas

name	repo-atlas
description	Build a self-contained persistent context system (atlas) for any repository. Use when asked to create a repo map, generate codebase documentation for LLM agents, set up an atlas, or create onboarding docs for a codebase. Also use when asked to "map this repo", "document this codebase", or "create context docs".

Repo Atlas

Build an in-repo persistent context system so engineers and LLM agents can understand any codebase quickly with minimal searching.

Hard Constraints

• Do NOT change product/runtime behavior
• No paid/hosted tooling — everything lives in the repo
• Zero or minimal dependencies (Python 3 standard library only)
• All generated content must reflect real repo specifics, not generic filler

Workflow

Phase 1: Reconnaissance

Before writing anything, understand the repo:

1. Read the top-level directory structure
2. Identify the repo type:
   • App (web, mobile, desktop) — has screens/views, state managers, routes
   • Backend/API — has controllers, routes, middleware, models
   • Library — has public exports, module structure, build config
   • Monorepo — multiple packages/services
   • CLI tool — has command handlers, argument parsing
   • Infrastructure — has deployment configs, IaC files
3. Identify the primary language(s) and framework(s)
4. Find entrypoints, build configs, CI files
5. Read 5-10 key files to understand architecture patterns

Phase 2: Run the Generator Script

Copy scripts/generate_atlas.py (bundled with this skill) to the repo at scripts/atlas/generate_atlas.py. Then customize and run it:

1. Copy the script to the target repo
2. Review and adjust the configuration section at the top:
   • IGNORE_NAMES — add repo-specific directory names to ignore (exact segment match)
   • TREE_ANNOTATIONS — add short descriptions for key directories
   • ENTRYPOINT_NAMES — add framework-specific entry filenames
   • ENTRYPOINT_PATH_PATTERNS — add path-based patterns (fnmatch style, e.g., cmd/*/main.go)
   • ENTRYPOINT_CONTENT_MARKERS — add code markers that identify entry points
   • CONVENTIONAL_COMMITS — adjust if repo uses different commit conventions
   • CHANGELOG_DAYS — change the changelog lookback window (default: 14)
3. Run: python3 scripts/atlas/generate_atlas.py --write

This auto-generates:

• docs/atlas/repo-map.md — directory tree + entrypoints + file stats
• docs/atlas/08_CHANGELOG_LAST_14_DAYS.md — categorized recent commits

Phase 3: Enhance repo-map.md

After the script generates the skeleton, manually add these sections to repo-map.md:

Router Table — "Where to look for X" (10-15 rows):

## Where to Look for X

| Task | Start Here |
|------|-----------|
| Fix [domain concept] | `path/to/file.ext` |

Map the top 10-15 tasks someone would do in this repo to specific files.

Danger Zones — fragile files/areas:

## Danger Zones

| File/Area | Why It's Fragile |
|-----------|-----------------|
| `path/to/file` | Reason |

Phase 4: Write Manual Atlas Docs

Create docs/atlas/ with these files. See references/atlas-templates.md for structure guidance on each.

File	Content Source
00_README.md	How to use the atlas + agent workflow conventions
01_ARCHITECTURE.md	Read entrypoints, DI setup, module boundaries
02_DOMAIN_MODEL.md	Read models/types, identify state machines
03_CRITICAL_FLOWS.md	Trace top 3-5 user flows through the code
04_STATE_SOURCES_OF_TRUTH.md	Identify all state stores (DB, cache, files, memory)
05_EXTERNAL_DEPENDENCIES.md	Read package manifests + integration code
06_GOTCHAS.md	Look for race conditions, init ordering, fragile patterns
07_TEST_MATRIX.md	Read test configs, describe how to run tests

Each doc should be 50-150 lines with real paths, real code references, and real gotchas from the codebase. Not generic advice.

Phase 5: Add Agent On-Ramp

Add an atlas section to the repo's CLAUDE.md (or create one). Include:

## Atlas — Persistent Context System

The `docs/atlas/` folder contains structured documentation for fast codebase onboarding.

### Agent Workflow

**Agent A (Plan + Execute)**:
1. Load `docs/atlas/repo-map.md` for orientation
2. Load the domain-specific atlas doc for your task
3. Read source files only after the atlas narrows your search
4. Implement changes following the patterns in the atlas

**Agent B (Verify)**:
1. Review diffs against `docs/atlas/06_GOTCHAS.md`
2. Verify changes match the flow described in `03_CRITICAL_FLOWS.md`
3. Confirm tests pass per `07_TEST_MATRIX.md`
4. Check state consistency against `04_STATE_SOURCES_OF_TRUTH.md`

### Working Rules
- **Analysis first**: Read the relevant atlas docs before writing code
- **Verify behavior**: After changes, confirm critical flows still work
- **No test-cheating**: Tests must pass because the code is correct
- **Update atlas**: If changes alter architecture/flows/state, update the relevant doc
- **Regenerate**: Run `make atlas-generate` after structural changes

Phase 6: Add Build Targets

Add to Makefile (create if needed):

atlas-generate:
	python3 scripts/atlas/generate_atlas.py --write

atlas-check:
	python3 scripts/atlas/generate_atlas.py --check

If the repo uses package.json, also add to scripts:

"atlas:generate": "python3 scripts/atlas/generate_atlas.py --write",
"atlas:check": "python3 scripts/atlas/generate_atlas.py --check"

Phase 7: Verify

1. Run atlas-generate — must complete without errors
2. Run atlas-check — must exit 0 immediately after generation
3. Confirm every atlas doc has real file paths and repo-specific content
4. Confirm no runtime/product code was changed

Output Summary

After completing all phases, report:

• List of created files
• How to run atlas generation/check
• 10-line "How an agent should use this atlas" quick reference

Resources

• Generator script: See scripts/generate_atlas.py — copy to target repo and customize
• Doc templates: See references/atlas-templates.md for structure guidance on each manual doc