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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteUse an AI coding assistant as a guide before treating it as a coder: ask it to map the repository, trace one real feature, and find the project’s setup and test commands. Verify its explanations against the files, then give it a small, reviewable change. Tests, a full diff review, and the team’s normal security and pull-request controls still apply.
Start by exploring, not editing
An unfamiliar repository is a poor place to begin with a broad request such as “fix something” or “explain everything.” First establish what the assistant can access and what the developer is meant to work on. Keep the initial session read-only in practice: ask questions, check the answers, and delay edits until the project’s layout and boundaries are clearer.
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- Confirm the checkout and boundaries. Check the repository, branch, development environment, and project area assigned to the developer. Do not give an agent access to secrets or production systems as a shortcut to learning the codebase.
- Ask for a repository map. Request the main languages, directories, application entry points, services, configuration, and communication between components. Ask for file paths so you can inspect the evidence.
- Trace a real behavior. Choose a small user-visible feature or API behavior and follow it from entry point through implementation, data or service boundaries, and tests. Ask the assistant to separate what it observed in the code from what it inferred, and to flag uncertainty.
- Find the project’s build and test workflow. Ask where setup, run, lint, and test instructions live, and what commands the project defines. Check those against scripts and documentation, then run the relevant commands yourself.
- Write down verified context. Record useful architectural landmarks, setup steps, commands, conventions, and boundaries in the repository’s existing documentation or AI instruction files.
- Only then choose a bounded first change. Ask for a proposed plan, likely files, relevant tests, and risks before allowing edits. Review the plan, keep the task small, and ask for an explanation of the resulting diff.
These steps are a practical workflow, not a demonstrated guarantee of faster onboarding. No directly relevant, independently attributable measurement of onboarding-time or productivity effects is established here.
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Adapt these prompts to the repository and your team’s rules. They are examples, not quotations or guaranteed tool behavior.
#1 Best Overall
Repository orientation
I’m new to this repository. Do not edit files. Map the main application entry points and components, and find how to run the project and its tests. For each finding, give the supporting file path or command, and label anything you are inferring.
Trace a behavior
Trace how [specific behavior] works from its entry point to the implementation and relevant tests. Explain the steps in order, name the files you inspected, and say what remains uncertain. Do not make changes.
Discover tests
Find the tests most relevant to [module or behavior]. Explain what they cover and give the project-defined command to run them. Do not claim a test passed unless you ran it and saw the result.
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Plan a first change
Propose a plan for [small change]. Identify likely files, conventions, tests, and risks or assumptions. Wait for my review before editing. After the change, summarize the diff and the verification you actually performed.
How to tell whether the explanation is reliable
An AI-generated map is a set of claims to verify, not authoritative documentation. Follow the cited paths, check that commands match the repository’s scripts and docs, and distinguish a test the assistant found from one it actually ran. If it cannot identify its evidence, ask it to inspect specific files or mark the point as uncertain rather than filling gaps with a confident guess.
- Check that named entry points, modules, and tests exist and match the stated behavior.
- Compare generated commands with project scripts and maintained setup documentation.
- Ask which parts are directly supported by code and which are inferred.
- Run relevant tests and static checks locally, and read their output.
- Inspect the complete diff, including files the assistant did not mention in its summary.
Give the assistant useful repository context
Concise, maintained context can help a tool find the right files and follow local conventions. Useful material includes the project’s purpose, major components, setup and test commands, important data flows, representative features and tests, and conventions that are not obvious from the code. Note areas that need extra review, ownership, or permission.
Do not copy a whole handbook into an AI instruction file. Link to maintained documentation where possible, and ask the assistant to consult current files: instructions, indexes, and architecture notes can become stale as code changes.
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A useful general pattern is to keep always-needed rules concise and broad, place path-specific rules near the files they govern, and put specialized procedures in reusable, on-demand guidance. The implementation varies by tool.
For example, Anthropic describes Claude Code navigating repositories by traversing files, searching, and following references. Its documentation discusses root and subdirectory CLAUDE.md files for broad and local conventions, and skills for specialized workflows that load when relevant. It also describes hooks for deterministic automation and language-server integrations for symbol-level navigation. These are Claude Code-specific mechanisms, not features to assume in every assistant. Anthropic’s Claude Code memory documentation explains its context approach.
GitHub documents Copilot repository-wide instructions, path-specific instructions, shared AGENTS.md guidance for multiple agents, and task-specific skills. Check the current documentation for the feature and configuration that apply to your tool and plan. GitHub’s repository-instructions documentation describes these options.
Move from orientation to a safe first change
Once the developer understands the relevant code path and how to verify it, delegate a task small enough to reason about. Have the assistant propose a plan and identify files and tests before editing. Review that plan first; after the change, compare its explanation with the actual diff.
A good first task is limited to a known component and has a clear expected result. Avoid combining unrelated cleanup, broad refactoring, and a feature request: smaller changes are easier for a new developer to understand and for a reviewer to assess.
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Verify changes and preserve human review
Run the relevant tests and static checks, inspect the full diff, and follow the same pull-request and security process used for other code. Passing tests do not establish that a change is correct or safe, and an AI review does not replace required human approval.
GitHub recommends requiring an approved pull request before changes can be merged into production codebases and other important branches. Its Copilot code-review documentation says Copilot reviews do not count toward required approvals by default. Availability and configuration can vary by plan and repository settings, so teams should check the current documentation. GitHub’s codebase-standards guidance covers pull requests, testing, scanning, and instructions; its Copilot code-review documentation describes review behavior.
Account for agent permissions
A coding agent that can read files or use tools can encounter malicious or misleading instructions embedded in content. Anthropic identifies prompt injection as a risk and describes sandboxing controls for filesystem and network access in Claude Code. Those controls are product-specific; do not assume another assistant has the same safeguards. Set permissions deliberately and consult the documentation for the tool your team uses. Anthropic’s sandboxing article discusses the risk and Claude Code controls.
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Make onboarding repeatable across the team
Turn useful discoveries into a maintained repository orientation guide: validated setup and test commands, architectural landmarks, approved tool settings, and links to deeper documentation. Assign an owner to keep it current and collect recurring questions from new developers. Training, workshops, or short internal videos can help establish expectations about how to check AI-generated explanations and review agent-authored changes.
Best Value
GitHub recommends custom instructions, AI-tool training, and onboarding resources, along with ongoing support. Anthropic describes shared configuration and conventions as useful in its large-scale deployments; that is vendor guidance, not independent comparative evidence. GitHub’s codebase-standards guidance and Anthropic’s Claude Code best-practices article describe their respective recommendations.
Choose tools by the work they need to support
There is no neutral product ranking established by these vendor materials. Compare tools against your repository, team workflow, and security requirements rather than choosing by a feature label alone.
| What to compare | Questions to ask |
|---|---|
| Repository context and navigation | Does it inspect the live working tree, use an index, follow symbol references, or rely on context you supply? How does it handle a monorepo or multiple services? |
| Instructions and workflows | Can you provide repository-wide and path-specific rules, shared agent guidance, or reusable specialized procedures? |
| Integration | Does it fit the editor, terminal, source control, issue tracker, documentation, and test workflow developers already use? |
| Security and permissions | What can the agent read, modify, execute, or access over the network? Are the permission and sandbox controls documented? |
| Verification and review | Can it run tests and checks, show a reviewable diff, and preserve the team’s human approval requirements? |
| Administration and cost | Which plan or organization settings are required, and how are usage and budgets managed? |
Features, plan requirements, settings, and costs change. Check the current documentation for the products and organization configuration you are evaluating.
Quick Recap
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