Dec 08, 2025·8 min
Claude Code for codebase onboarding: prompts that map your app
Claude Code for codebase onboarding: use Q&A prompts to map modules, key flows, and risks, then turn the notes into a short onboarding doc.
Claude Code for codebase onboarding: use Q&A prompts to map modules, key flows, and risks, then turn the notes into a short onboarding doc.
Reading files at random feels slow because most codebases aren't organized like a story. You open a folder, see ten names that look important, click one, and end up in helpers, configs, and edge cases. After an hour, you have lots of details but still can't explain how the app works.
A better goal for Claude Code during onboarding is to build a simple mental map. That map should answer three questions:
Good enough onboarding in 1-2 days isn't "I can explain every class." It's closer to this:
Some things can wait. Deep refactors, perfect understanding of every abstraction, and reading old code no one touches rarely get you useful fastest.
Think of onboarding as building a map, not memorizing streets. Your prompts should keep pulling you back to: "Where am I in the system, what happens next, and what could go wrong here?" Once you have that, details get easier to learn on demand.
Before you start asking questions, collect the basics you normally need on day one. Claude Code works best when it can react to real files, real config, and real behavior you can reproduce.
Start with access and a working run. Make sure you can clone the repo, install dependencies, and run the app (or at least a small slice) locally. If local setup is hard, get access to a staging environment and to wherever logs live, so you can verify what the code actually does.
Next, find the "source of truth" docs. You're looking for whatever the team actually updates when things change: a README, a short architecture note, an ADR folder, a runbook, or a deployment note. Even if they're messy, they give names to modules and flows, which makes Q&A far more precise.
Decide scope early. Many repos contain multiple apps, services, and shared packages. Pick boundaries like "only the API and the billing worker" or "only the web app and its auth flow." Clear scope prevents endless detours.
Write down assumptions you don't want the assistant to guess. This sounds small, but it prevents wrong mental models that waste hours later.
Here is a simple prep checklist:
If something is missing, capture it as a question for a teammate. Don't "work around" missing context with guesses.
A mental map is a small set of notes that answers: what are the main parts of this app, how do they talk to each other, and where can things go wrong. Done well, onboarding becomes less about browsing files and more about building a picture you can reuse.
Start by defining your outputs. You want a module list that is practical, not perfect. For each module, capture what it does, who owns it (team or person if you know), and its key dependencies (other modules, services, databases, external APIs). Also note main entry points: UI routes, API endpoints, background jobs, and scheduled tasks.
Next, pick a few user journeys that matter. Three to five is enough. Choose flows that touch money, permissions, or data changes. Examples: signup and email verification, creating a paid plan or purchase, an admin action that changes user access, and a critical daily-use flow that most users rely on.
Decide how you'll label risk before you start collecting notes. Keep categories simple so you can scan later. A useful set is security, data integrity, uptime, and cost. When you mark something as risky, add one sentence explaining why, plus what would prove it's safe (a test, a log, a permission check).
Use a consistent format so you can turn notes into an onboarding doc without rewriting everything:
Example: if Checkout calls Billing which writes to payments and invoices, tag it as data integrity and cost. Then note where retries happen and what prevents double-charging.
When you join a new repo, you want fast orientation, not perfect understanding. These prompts help you build a mental map in small, safe steps.
Start by giving the assistant the repo tree (or a pasted subset) and ask for a tour. Keep each round focused, then finish with one question that tells you what to read next.
1) Repo tour
"Here is the top-level folder list: <paste>. Explain what each folder likely contains and which ones matter for core product behavior."
2) Entry points
"Find the app entry points and boot process. What files start the app, set up routing, configure DI/env, and start background jobs? Name the exact files and what they do."
3) Module index
"Create a module index: module name, purpose, key files, and important external dependencies. Keep it to the modules that affect user-facing behavior."
4) Data model hints
"Based on migrations/models, list the key tables/entities, critical fields, and relationships. Call out fields that look security-sensitive or used for billing/permissions."
5) Flow trace
"Trace this flow end-to-end: <flow>. Where does the request/event start, where does it end, and what does it call in between? List the main functions/files in order."
6) Next inspection
"What should I inspect next and why? Give me 3 options: fastest clarity, riskiest area, and best long-term payoff."
A concrete example: if you're mapping "user signs up and creates their first project," ask for the API route handler, validation, DB write, and any async job that sends emails or provisions resources. Then rerun the flow trace for "user deletes project" to spot cleanup gaps.
To keep answers actionable, ask for specific artifacts, not just summaries:
The biggest onboarding win is turning scattered Q&A into notes another developer can reuse. If the notes only make sense to you, you'll redo the same digging later.
A simple structure beats long pages. After each exploration session, save the answers into five small artifacts (one file or doc is fine): a module table, a glossary, key flows, unknowns, and a risk register.
Here is a compact template you can paste into your notes and fill as you go:
Module table
- Module:
Owns:
Touches:
Entry points:
Glossary
- Term:
Meaning:
Code name(s):
Key flow (name)
1.
2.
3.
Unknowns
- Question:
Best person to ask:
Where to look next:
Risk register
- Risk:
Location:
Why it matters:
How to verify:
Keep key flows short on purpose. Example: 1) user signs in, 2) backend creates a session, 3) client loads the dashboard, 4) API fetches data, 5) UI renders and handles errors. If you can't fit a flow into five steps, split it (login vs dashboard load).
When using Claude Code, add one line to every answer: "How would I test this?" That single line turns passive notes into a checklist you can run later, especially when unknowns and risks start overlapping.
If you're building in a vibe-coding platform like Koder.ai, this kind of note-taking also helps you spot where generated changes might have side effects. Modules with lots of touchpoints tend to be change magnets.
Risk in a codebase is rarely random. It clusters where the app decides who you are, changes data, talks to other systems, or runs work in the background. You can find most of it with targeted questions and a few focused searches.
Start with identity. Ask where authentication happens (login, session, tokens) and where authorization decisions live (role checks, feature flags, ownership rules). A common trap is checks scattered across UI, API handlers, and database queries with no single source of truth.
Next, map the write paths. Find endpoints or functions that create, update, or delete records, plus the migrations that reshape data over time. Include background jobs too. Many mystery bugs come from async workers writing unexpected values long after a request finished.
Prompts that surface risk quickly:
Then check configuration and secrets handling. Look for environment variables, runtime config files, and default fallbacks. Defaults are useful, but risky when they hide misconfigurations (for example, using a dev key in production because a value was missing).
A quick example: in a Go backend with PostgreSQL, you might find a "send email" job that retries on failure. If it retries without an idempotency key, users can get duplicate emails. If failures only log a warning and no alert exists, it breaks silently. That's a high-risk area worth documenting and testing early.
Use one real flow to build your first end-to-end thread through the system. Login is a good starter because it touches routing, validation, sessions or tokens, and database reads.
Scenario: a React web app calls a Go API, and the API reads and writes PostgreSQL. Your goal isn't to understand every file. It's to answer: "When a user clicks Login, what code runs next, what data moves, and what can break?" This is how onboarding stays concrete.
Start at the UI and walk forward, one hop at a time. Ask for specific file names, functions, and request and response shapes.
After each answer, write one short line in your mental map: "UI component -> API endpoint -> handler -> service -> DB query -> response." Include the names, not just "some function."
Once you have the path, verify it with a small test run. You're checking that the code path you mapped is the code path the app actually uses.
Watch network requests in the browser dev tools (path, status code, response body). Add or enable server logs around the handler and the DB call (request ID if available). Query PostgreSQL for expected changes (for login, maybe last_login_at, sessions, or audit rows). Force one failure (wrong password, missing field) and note where the error message is created and where it's shown. Record expected responses for success and failure (status codes and key fields), so the next developer can sanity-check quickly.
This single flow often exposes ownership boundaries: what the UI trusts, what the API enforces, and where errors disappear or get double-handled.
Once you have a decent mental map, freeze it into a 1-2 page note. The goal isn't to be complete. It's to help the next developer answer: what is this app, where do I look first, and what's most likely to break?
If you're using Claude Code, treat the doc as the output of your Q&A: clear, concrete, and easy to skim.
Keep the doc predictable so people can find things fast. A good structure is:
For "Where things live," include pointers like "Auth starts in X, session logic in Y, UI routes in Z." Avoid dumping a full tree. Pick only what people will touch.
For "Key flows," write 4-7 steps per flow: trigger, controller or handler, core module, database call, and the outward effect (email sent, state updated, job queued). Add file names at each step.
For "Risky areas," name the failure mode and the fastest safety check (a specific test, a smoke run, or a log to watch).
End with a small first-tasks list so someone can contribute safely:
The fastest way to waste an assistant is to ask for "a full explanation of the whole repo." You get a long summary that sounds confident but stays vague. Instead, pick a small slice that matters (one module plus one user flow), then expand outward.
A close second mistake is not naming which journeys matter. If you don't say "checkout," "login," or "admin edit," answers drift into generic architecture talk. Start each session with one concrete goal: "Help me understand the signup flow end to end, including validation, error states, and where data is stored."
Another common trap is letting the assistant guess. When something is unclear, force it to label uncertainty. Ask it to separate what it can prove from code vs what it is inferring.
Use a simple rule in your notes: every claim must be tagged as one of these.
Notes also fall apart when they're collected with no structure. A pile of chat snippets is hard to turn into a mental map. Keep a consistent template: modules involved, entry point, key functions and files, data touched, side effects, error paths, and tests to run.
Even with Claude Code, treat the output as a draft. Verify key flows in the running app, especially the parts that can break production: auth, payments, permissions, background jobs, and migrations.
A practical example: if the assistant says "password reset sends an email via X," confirm it by triggering a reset in a dev environment and checking logs or the email sandbox. That reality check prevents you from onboarding into a story that isn't true.
You don't need to memorize the repo. You need enough confidence to make a safe change, debug a real issue, and explain the system to the next person.
Before you call yourself onboarded, make sure you can answer these without guessing:
If you're missing one item, do a small focused pass instead of a broad search. Pick one flow, follow it until the database boundary, then stop and write down what you learned. When something is unclear, capture it as a question, not a paragraph. "Where is role X created?" is more useful than "auth is confusing."
A good final test: imagine you're asked to add a small feature behind a flag. If you can name the files you'd touch, the tests you'd run, and the failure modes you'd watch for, you're onboarded enough to contribute responsibly.
A mental map is only useful while it matches reality. Treat it like a living artifact, not a one-time task. The easiest way to keep it honest is to update it right after changes that affect behavior.
A lightweight routine beats big rewrites. Tie updates to work you're already doing:
Keep the onboarding doc close to the code and version it with the same discipline as the codebase. Small diffs get read. Big doc rewrites usually get skipped.
When deployments are risky, write down what would help the next person recover fast: what changed, what to watch, and how to roll back. If your platform supports snapshots and rollback, add the snapshot name, reason, and what "good" looks like after the fix.
If you build with Koder.ai (koder.ai), planning mode can help you draft a consistent module map and onboarding note from your Q&A, and source code export gives reviewers a clean way to validate the result.
Finally, define a handoff checklist the next developer can follow without guessing:
Done well, Claude Code for codebase onboarding becomes a habit: each change leaves behind a clearer map for the next person.
Aim for a usable mental map, not total understanding.
A solid 1–2 day outcome is:
Give it concrete artifacts so it can point to real code instead of guessing:
Pick a narrow slice with clear boundaries.
A good default scope is:
Write down what’s explicitly out of scope (other services, legacy modules, rarely used features) so the assistant doesn’t wander.
Start from known triggers, then walk forward:
Ask for file paths and function names in order, and end with: “How would I test this quickly?”
Look where the system makes decisions or changes state:
Use a simple label system and attach one proof step.
Example format:
Force the assistant to separate evidence from inference.
Ask it to tag each claim as one of:
When something is unknown, turn it into a teammate question (“Where is role X defined?”) instead of filling the gap with a guess.
Keep one lightweight note file with five sections:
Default to a quick, real check:
This validates you mapped the path the app actually uses.
Use platform features to reduce blast radius and keep changes reviewable.
Practical defaults:
Then ask: “What breaks silently, and how would we notice?”
Keep it short so you actually update it as you learn.
Add one line to each flow: “How would I test this?” so it becomes a checklist.
This works especially well for onboarding tasks like “add a guardrail,” “tighten validation,” or “improve an error path.”