AI Tools for Developer Onboarding: From Repository to First Change

Four jobs sit between cloning a repository on your first day and getting your first change merged: locating the code that handles a thing, tracing one flow through it, explaining what you found to somebody else, and making a change that survives review. Different tools win different jobs. None of them wins all four, and free options cover three.

The split is not arbitrary. In a case study of onboarding inside a large software company, Ju, Sajnani, Kelly and Herzig found that most of the onboarding process consists of engineering tasks such as fixing bugs and implementing small features. The first task is the onboarding, not a warm-up before it.

Disclosure: Scrimba publishes this article and makes one of the tools in it. Scrimba Explain, listed in the ChatGPT plugin directory as Explain Video Generator, appears under job 3, and the tools that beat it at the other three are named with their prices. Every price below was checked against the vendor's own page on 8 September 2026.

The reading method this assumes is a separate guide, and the whole-working-day view of AI tooling is a broader roundup. This page narrows both to week one.

The Four Jobs Between a Clone and a Merged Change

They are four jobs rather than one because each has a different definition of done, and because the output of one is the input to the next. A located file does not tell you what runs. A traced flow does not mean you can explain it. An explanation does not get merged.

One constraint shapes every choice below, and it is specific to week one. A new joiner cannot grade an answer against prior knowledge. An experienced engineer reads a confident wrong explanation and feels the friction immediately. Somebody on day three has nothing to feel it against, so the cheap tool that produces something checkable beats the clever tool that produces something you have to take on faith.

Job 1: Locating the Code That Handles a Thing

Locating turns a behavior into a file path. It is the easiest of the four jobs and the one AI changed most, because the file tree is a filing decision and nothing in it is named after what your ticket describes.

Agentic search wins this outright. The clearest published evidence is Birgitta Böckeler's account of onboarding onto a legacy codebase with AI help from August 2024: asked where a document type was handled, GitHub Copilot pointed her at an enum that was, in her words, "exactly the right place."

Three routes, with what they cost:

• GitHub Copilot has a genuinely usable free plan: 2,000 completions a month, with CLI and agent mode included, per its plans page. Pro is $10 a month, Pro+ is $39.
• Cursor puts search in the editor. The Hobby plan is free with limited agent requests, and the Individual plan is $20 a month, per Cursor's pricing.
• Claude Code is the terminal option and is not on Anthropic's free plan. It starts with Pro at $17 a month billed annually, or $20 monthly, per Claude's pricing. Scrimba has a guide to using Claude Code, and to what agentic coding means if the category is new.

Since this page is published by the company that makes it: Scrimba Explain is not a code search tool and does not belong in this job. It explains something you already found. Sending it looking is the wrong instrument for the work.

Job 2: Tracing One Flow End to End

Locating gives you a file. Tracing gives you an order of operations, and the order is what a reviewer will quietly assume you understand.

DeepWiki is the strongest free option. It indexes a public GitHub repository into a wiki with architecture diagrams, generated documentation and links back to the source, then answers questions against it. Per Devin's documentation, the free version covers public repositories, private ones run through the Devin app, and wiki generation has effort levels where the default is free and higher levels bill against a subscription.

The source links matter more here than the diagrams do. An architecture diagram asserts a structure; a link lets you check one.

A generated map renders structure. Structure is not the same as the branch that actually executed for your input, and on day three you cannot tell the difference by reading.

The trace is finished when the code says so, not when the tool answers. Put a log line or a breakpoint on the path you were given, run it once, and read what comes back. Thirty seconds of runtime settles what a diagram can only claim, and it produces the one artifact you can show a colleague without hedging.

Job 3: Explaining It Back to Somebody

This is the job most onboarding advice skips, and it is not vanity. The ICSE case study frames onboarding tasks as doing three things at once: teaching the system, building the newcomer's confidence, and creating social contact with the team. Explaining what you found is the step that converts private reading into shared context, and it is the step a manager uses to decide whether you actually have it.

Three options, judged on the same criteria.

Agent chat is the fastest and evaporates. You get an answer in the sidebar, you nod, and nothing exists afterward that anybody else can read.

DeepWiki's question answering is grounded and links back to source, which makes it the easiest to audit. Its weakness is that a wiki answer is a reference document, and reference documents are not how you get somebody's attention in a team channel.

Scrimba Explain, whose plugin listing is named above, takes the third route. You ask a question, and it returns a narrated video explainer instead of a wall of text. Playback pauses partway through on a comprehension check, the property neither of the other two has: it tests whether the person watching followed. It is free during open beta.

For a codebase, the natural route is MCP through a coding agent such as Claude Code or Codex, because the agent already holds the repository in context. Scrimba has a walkthrough of Explain itself and one on adding MCP to Claude Code. The plugin does nothing until it is installed. Naming it in a ChatGPT session that has not installed it returns an ordinary text answer, which reads exactly like the product failing.

The use case comes from the vendor rather than from this page. The listing's own use-case list includes "Explain a pull request or a codebase to the rest of the team" and "Onboard new employees without filming anything."

The limits. Free during open beta means no announced pricing and nothing to compare plans against. Explain's FAQ says that like any AI tool it can make mistakes and that anything important should be double-checked. It does not search code and it does not edit code. Where DeepWiki ties a claim to a line you can open, a narrated explanation is harder to audit, and that is the trade.

Scrimba publishes a gallery of roughly 150 explanations with named authors, runtimes and view counts, which shows the product ships finished output. Those are general topics. The onboarding case is documented by the vendor, not demonstrated here. Run one on a system you already understand before promising it to a team.

Job 4: Making the Change and Getting It Merged

The definition of done changes here. Written is not merged, and merging is a social act, which is why this is the job AI helps with least.

Picking the change. A first change worth making touches the flow you just traced, is small enough to describe in one sentence, has a test that fails before it passes, and sits in an area somebody owns and is available this week. Open source has a name for this task and a large body of evidence behind it. Analyzing 48,402 good first issues across 964 repositories, Tan and colleagues found about 70% had expert participation, and half received a first expert comment within 8.5 hours of a newcomer's.

Writing it. The delegated route is real: GitHub's Copilot cloud agent researches a repository, plans, changes code on a branch and opens a pull request, and it is available on all paid Copilot plans. Cursor and Claude Code do the same work interactively.

One warning belongs next to that, and it is specific to a first change. Böckeler's experiment failed hardest on generated tests, which mocked parts of the code that should not have been mocked and set up test data only one level deep. A new joiner cannot spot either mistake, and a green test suite that proves nothing is worse than no tests, because it survives review.

Getting it merged. Run machine review first so human review is spent on judgment rather than typos. CodeRabbit is free forever on public repositories, then $30 per developer a month, or $24 billed annually. Greptile offers a free Starter plan with 50 credits a month for one active developer, $30 per seat on Pro, and free access for qualified non-commercial projects under MIT or Apache licenses. Scrimba covers what these bots catch and miss in more depth.

The same study reports one more result worth carrying into month two: expert involvement correlates positively with a newcomer's successful contribution and negatively with their retention. A reviewer is what gets your first change merged. Needing one every time is not the goal.

Where This Goes Wrong in Week One

Three failure modes, all specific to having no prior knowledge of the system.

1. You cannot grade the answer. A confident wrong explanation costs an experienced engineer a minute and costs you a day, because you have nothing to check it against. This is the argument for preferring the tool whose output links to a line.
2. You ask the tool what you should ask the team. The confidence and social-contact effects in the ICSE study only happen between people. A colleague who answers you now knows what you are working on, and an agent does not tell anybody.
3. You lose the week to the environment. Böckeler's clearest negative result was that AI does not rescue an undocumented, unautomated setup. Ask for the setup script on day one, and if there is not one, writing it is a legitimate first change.

The rule underneath all three: use the tools to produce something checkable, then spend your human budget on checking rather than on discovering.

A First Week That Ends in a Merged Change

A schedule, not a method. Each day produces one artifact.

1. Monday. Get it running and ask for the setup script. Artifact: a working local environment, and a list of every step the README was missing.
2. Tuesday. Locate. Pick one behavior from a real ticket and find the code that handles it with agentic search. Artifact: three file paths and one sentence about what each does.
3. Wednesday. Trace. Generate the map, then confirm it with a breakpoint or a log line on one real run. Artifact: an ordered list of what executes.
4. Thursday. Explain. Turn Wednesday's trace into something a colleague can consume, then have somebody who knows the system correct it. Artifact: the corrections.
5. Friday. Change. Take the smallest change that touches the flow you traced, open it, get it reviewed, merge it.

The mechanics nobody teaches on the way in are the review ones: branching, resolving conflicts, and responding to review comments without redoing the work. Scrimba's Learn Git and GitHub covers them in 103 minutes with Gregor Thomson on the Pro plan, $24.50 a month billed annually with location-based, student and promotional discounts available. The non-technical half of the week is covered in what to expect from your first week.

Frequently Asked Questions

What are the best AI tools for onboarding a developer to a new codebase?

There is no single answer, because onboarding is four jobs. Agentic search in Copilot, Cursor or Claude Code wins locating. DeepWiki wins tracing. Scrimba Explain wins explaining what you found to a colleague. A code review bot plus a human reviewer wins getting a first change merged.

Can AI explain a codebase to a new team member?

Yes, with a caveat that matters more for a newcomer than for anyone else. Tools that link every claim back to a line in the repository can be checked. Tools that narrate cannot, so verify anything you plan to repeat in front of the team. No vendor here publishes an accuracy guarantee.

How long should it take to make your first change at a new job?

Within the first week is a reasonable target for a small change on a flow you have traced. The constraint is rarely writing the code. It is finding a reviewer, which is why picking work in an area somebody owns and is available for matters more than picking easy work.

Are free AI tools enough for developer onboarding?

For three of the four jobs, yes. Copilot's free plan covers locating, DeepWiki is free on public repositories, Scrimba Explain is free during open beta, and CodeRabbit reviews public repositories at no cost. Private repositories are where the free options thin out fastest.

What should you not use AI for when joining a new team?

Anything that is really a social task. Deciding what to work on, learning why a decision was made, finding out what is fragile, and building the relationships you will need in month two. An agent will answer all four confidently and none of those answers gets you a reviewer.

Key Takeaways

• Onboarding is four jobs, not one: locating code, tracing a flow, explaining it, and making a change that gets merged. Each has a different winner.
• No tool wins all four. Agentic search leads on locating, DeepWiki on tracing, Scrimba Explain on explaining, and a human reviewer on merging.
• Checkability beats cleverness in week one, because a newcomer has nothing to grade a confident wrong answer against.
• Free options cover three of the four jobs on public repositories: Copilot Free, DeepWiki, Explain during open beta, and CodeRabbit.
• Merged is the definition of done, and merging is social. Expert involvement is what correlates with a newcomer's contribution landing.

Sources

• Ju, Sajnani, Kelly and Herzig, A Case Study of Onboarding in Software Teams: Tasks and Strategies, ICSE 2021
• Tan, Chen, Wu, Zhou and Zhang, Is It Enough to Recommend Tasks to Newcomers?, 2023
• Birgitta Böckeler, onboarding onto a legacy codebase with AI help, August 2024
• GitHub Copilot plans and Copilot cloud agent
• Cursor pricing and Claude pricing
• DeepWiki and Devin's DeepWiki documentation
• CodeRabbit pricing and Greptile pricing
• Scrimba Explain, the explainer gallery, and the Explain Video Generator listing

Vendor pricing accessed 8 September 2026.