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Use an AI coding assistant as a scoped collaborator, not as an unchecked shell operator. Match its autonomy to the task: use a completion or focused edit for a small change, Ask or chat to understand code, request a plan before a broad or ambiguous change, and use an agent when the work genuinely spans files or requires tools. Then inspect the diff, run the project’s own checks, and keep only changes you can explain and verify.
That loop matters on Linux because an agent may be able to read and change workspace files or run commands with your user’s permissions. The safeguards are task boundaries, suitable approvals, careful handling of context, and independent review—not confidence in a polished response.
Choose the least autonomous workflow that fits the task
Coding assistants offer different interaction styles: inline suggestions, focused edits, chat or Ask, planning, and multi-step agents. A useful choice depends on the work you need done, not on which mode sounds most advanced. Use the narrowest mode that can reasonably complete the task; widen the scope only when the work requires it.
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Small completion or local edit
For a short function, a small refactor, or a localized change whose behavior you already understand, start with an inline suggestion or focused editor request. You can see the surrounding code, judge whether the proposed change fits, and accept or reject it without handing over a broader task. This is usually a better fit than asking an agent to inspect and act across the repository for a one-line adjustment.
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Be specific about the local outcome. For example, ask for a function to reject an empty input while preserving its current return type, rather than requesting a general “cleanup.” Review the complete changed function and its callers if relevant; a small edit can still alter behavior beyond the line where it appears.
Questions about unfamiliar code
Use Ask or chat when the immediate goal is to understand a code path, identify where a behavior is implemented, or get an explanation before making a change. Provide relevant repository context through supported references or files, then inspect which references the assistant used. GitHub documents Ask mode as optimized for codebase questions and describes context variables and repository instructions for its IDE workflow. Those capabilities can help direct a question, but you should still check the cited code yourself.
Good questions are bounded: “Where is this configuration parsed, and which callers depend on the default?” is easier to evaluate than “Explain this repository.” If the answer names a file or symbol, open it and verify the explanation against the implementation. Treat an answer as a map for inspection, not as proof that the behavior is correct.
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Ask for a plan before implementation when a task affects several components, changes an interface, or leaves important choices unresolved. State the intended behavior and constraints, then review the proposed steps before allowing edits. Planning gives you a chance to catch an incorrect assumption while it is still only a proposal.
GitHub documents separate Plan and Agent modes; Visual Studio Code also describes planning as useful before implementation. These labels and workflows can change, so check the current interface and official documentation for the tool you use. The practical principle is stable: agree on the approach before delegating an uncertain change.
Bounded multi-file work
Use an agent when the task actually requires it to find relevant files, make coordinated edits, or run tools. The Visual Studio Code documentation describes an agent as using a language model to reason about a request and tools to act on the development environment. That capability is useful, but it also makes scope and permissions important: an agent is not merely generating text when it can act on the repository.
Give the agent a bounded result to achieve, keep follow-up steering available, and review its final diff and validation. An agent’s ability to finish several steps does not make every step necessary, safe, or correct.
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Prepare the Linux repository before asking for changes
Confirm where you are and what is already changed
Start from the intended repository directory. Check the current Git state before delegating consequential work so you can distinguish your existing changes from the assistant’s edits. A simple baseline is:
pwd
git status --shortIf you have uncommitted work, decide how to protect it before proceeding. Make a commit or another checkpoint when appropriate, or at least know which files and changes were already yours. Codex CLI’s official documentation recommends Git checkpoints around a task. A checkpoint makes recovery easier; it does not replace reviewing what the assistant changes.
Define the result, not just the activity
Describe the observable behavior you want. Include relevant files or subsystem if you know them, what must remain unchanged, and how you will decide the work is done. “Update the parser” names an activity; “accept the new optional field, preserve the old input format, and add coverage for both forms” defines a result and constraints.
When you do not know where the change belongs, ask the assistant to inspect and propose likely files before editing. When you do know, name the relevant area so it does not needlessly search or modify unrelated code. Avoid repository-wide requests such as “modernize everything” unless you intend to review a broad, high-risk diff.
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Supply only useful context
Context should help answer the task, not expose the entire workspace by default. Point to the relevant implementation, tests, configuration, error output, or project instructions using the editor or assistant’s supported references. In an IDE, inspect the references used in the chat and correct missing or misleading context. A model can miss a relevant file, rely on an outdated instruction, or infer a relationship that the code does not support.
Terminal output can be useful context, but it can also contain credentials, internal paths, customer data, or proprietary details. Before pasting logs or diagnostics, inspect them and remove secrets and unrelated sensitive material. Never put credentials into prompts as a shortcut to making a command work.
Write a prompt that can be checked
A practical request separates five things: the goal, context, constraints, verification, and limits. This is a working prompt pattern synthesized from documented task, context, tool, and review workflows; it is not a vendor-guaranteed formula.
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- Goal: State the behavior or result you want, preferably in terms a test or reviewer can observe.
- Context: Identify the files, subsystem, error, or relevant references. Ask for an inspection step first if you do not know where the change belongs.
- Constraints: Say which interfaces, behavior, compatibility, or style must be preserved.
- Verification: Name the project’s relevant tests, formatter, linter, or build checks if known. Ask the assistant to report which checks it actually ran.
- Limits: Say when it should stop and ask, especially before destructive changes, external actions, or a scope expansion.
For example, a bounded request can look like this:
Goal: Make the configuration reader accept an optional timeout value.
Context: Inspect the configuration reader and its existing tests first.
Constraints: Preserve the current behavior when the value is absent; do not change the public function signature.
Verification: Add or update focused tests and run the relevant test command already used by this project.
Limits: Do not edit generated files or run destructive commands. If the format or default value is ambiguous, ask before changing it.Replace the example with the actual behavior, files, and checks for your repository. Do not assume the assistant knows your project’s conventions because it can see source code; tell it which constraints matter, then confirm they were respected.
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Inspect the diff before accepting the result
Read the full diff rather than relying on the assistant’s summary. Look for unrelated edits, changed defaults, altered public interfaces, removed error handling, generated-file changes, accidental formatting churn, and tests that were weakened to make a change pass. Check whether the implementation matches the requested behavior and whether nearby callers or tests reveal compatibility requirements.
Useful Git checks include:
git status --short
git diff --check
git diffgit diff --check can identify whitespace errors in a diff; it does not establish that the code builds or behaves correctly. The full diff is the review artifact. If it is unexpectedly large, stop and ask the assistant to explain or narrow the edits before moving on.
Review shell commands before they run
An assistant may propose commands as part of an agent workflow. Read commands before execution, particularly commands that delete, overwrite, install, change permissions, access the network, or operate outside the repository. Confirm what a command affects and whether it is necessary. If the tool prompts for approval, treat the prompt as a review point rather than clicking through automatically.
Prefer manual approval or narrowly scoped command approvals for risky actions. Broad auto-approval can turn an otherwise bounded code task into permission to do unrelated work. Permission controls vary among tools and versions, so review the actual settings for the harness in use instead of assuming another product’s controls apply.
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Use the existing build, formatter, linter, and tests that apply to the change. The exact commands are project-specific; inspect the repository’s instructions and configuration rather than inventing a universal Linux command. A successful assistant response is not evidence that a check ran, and a successful check is not evidence that the test suite covers every affected behavior.
After the checks, inspect the Git state again. Verify that the assistant did not leave unexpected files, alter unrelated work, or report a check it did not run. Decide what to keep, revise, or revert based on the diff and test evidence. If a check could not run, report that limitation accurately rather than implying the change is verified.
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Set permissions to the risk of the task
AI agents can be able to create, modify, or delete workspace files and run commands with the user’s permissions. This means the risk depends not only on the model’s answer but also on the tools and access the agent has. A small code change in a disposable branch is different from a task that can reach sensitive files, credentials, external services, or important data.
Use layered safeguards
- Keep a recoverable Git checkpoint for consequential tasks.
- Limit the requested scope to the files and behavior needed.
- Require approval for consequential commands or external actions.
- Review the actual diff and command before execution.
- Use sandboxing where it is supported and appropriate, without treating it as a complete security boundary.
Visual Studio Code documents OS-level agent sandboxing for supported Linux hosts. Its documentation explicitly cautions that sandboxing is an added layer, not a standalone security boundary or replacement for endpoint security. Injected credentials, allowed paths, network access, unsandboxed fallback, or bypass can weaken it. Review the exact platform support and configuration in current documentation before relying on a sandbox.
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Protect code, credentials, and untrusted context
Consider what may enter model or tool context: workspace files, terminal output, diagnostics, selected code, repository instructions, and retrieved content. Some of that material may be proprietary or sensitive. Keep secrets out of prompts and logs, and follow your organization’s tool and data policies. Before connecting a repository or sharing an error trace, determine whether the content is allowed to be sent to that service and under what terms.
Also treat repository files, terminal output, and fetched webpages as data, not as trusted instructions. A file or webpage can contain text that attempts to redirect an assistant or persuade it to reveal information or take an action. Visual Studio Code documents prompt-injection risk in retrieved user-generated content. A tool should follow your task and your organization’s policy, not instructions embedded in untrusted data.
If the assistant surfaces directions from a README, issue, log, or webpage that conflict with your request, inspect the source and decide whether the content is relevant. Do not give it credentials or approve a command merely because retrieved text says to do so. Separate the information needed to solve the task from instructions that would expand access or change the task.
Choose a tool by workflow, not by a universal ranking
Official product documentation establishes capabilities, not a universal winner or independent performance comparison. Choose according to where you work, how much autonomy the task needs, what the tool can access, and your organization’s rules. Product features, availability, limits, and subscription terms change; verify current official terms before making a decision based on them.
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| Workflow | Documented fit | Best use in a Linux repository | What to verify |
|---|---|---|---|
| Visual Studio Code AI features | Suggestions and focused edits through full agents; OS-level agent sandboxing is documented for supported Linux hosts. | Stay in the editor for local changes, questions, planning, or agent work when those features fit your project. | Current feature labels, supported Linux hosts, sandbox configuration, and available permission controls. |
| GitHub Copilot in an IDE | Ask, Plan, and Agent workflows are documented; Ask is optimized for codebase questions, with context variables and repository instructions. | Use Ask for understanding, Plan for a proposed approach, and Agent when a bounded task needs tool-assisted edits. | Current IDE support, workflow availability, context behavior, usage limits, and organizational policy. |
| Codex CLI | Official documentation describes Linux support, local repository work, permissions, interactive use, review, scripting, and CI. | Consider it when a terminal-centered workflow and local project tools suit the task. | Current Linux installation instructions, permission behavior, plan terms, and how the CLI will access the working directory. |
The descriptions above reflect vendor documentation accessed on October 13, 2026; they are capability descriptions, not comparative performance results. The relevant sources are Visual Studio Code’s “Build with AI in VS Code” and “Secure AI-assisted development in VS Code,” GitHub Docs’ “Asking GitHub Copilot questions in your IDE,” and OpenAI’s “Codex CLI.” Check those official documents for current setup details rather than relying on remembered UI labels or commands.
To compare tools for your own setup, ask the same operational questions of each option:
- Interaction surface: Does it fit your existing editor, terminal, or both?
- Autonomy: Does it only suggest code, answer questions, edit a selection, plan, or act across multiple steps?
- Execution and access: Does work happen in the local repository or a cloud environment, and what shell, network, files, and credentials can it reach?
- Permissions and review: Can you approve commands, restrict scope, use a sandbox, and inspect edits before accepting them?
- Context integration: Can you provide selected files, repository instructions, references, terminal output, or connected tools in a way you can inspect?
- Operational fit: Does it meet your Linux setup, organization policy, usage requirements, and current cost or plan constraints?
Do not use marketing claims or a feature checklist as a substitute for those questions. The official sources establish that these workflows exist; they do not establish that one assistant is more accurate, faster, or more productive for every developer.
Recover when the work goes off track
The assistant edits unrelated files
Stop before asking it to continue. Compare the current state with your baseline and inspect the diff. Tell it which files are in scope and ask for an explanation of each out-of-scope change. Keep only changes that are justified and reviewable; revert unrelated edits carefully so you do not discard your own work.
The proposed plan assumes the wrong behavior
Do not let implementation momentum decide the question. Correct the requirement, identify the assumption that was wrong, and request a revised plan. If the ambiguity affects compatibility or a public interface, resolve it before authorizing a broad edit.
A command or check fails
Read the full output and determine whether the failure comes from the change, an existing environment issue, or a missing prerequisite. Share only the relevant sanitized excerpt. Ask the assistant to explain the failure and propose a minimal next step; inspect that step before running it. Do not let repeated retries expand into destructive cleanup or system-wide changes without a clear reason.
The answer sounds plausible but cannot be verified
Ask for the file, symbol, test, or command supporting the claim, then inspect it yourself. If the repository does not establish the behavior, say so and seek the missing requirement rather than allowing an inference to become a code change. A clear uncertainty is safer than a confident, unsupported implementation.
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A practical decision rule
Before each task, ask: “What is the smallest useful action the assistant can take, and how will I verify it?” If the answer is a local suggestion, stay with completion or focused editing. If it is an explanation, use chat with relevant context. If the desired implementation has unresolved decisions, ask for a plan. If it needs coordinated changes and tools, use a bounded agent loop with approvals and review. In every case, the code that remains should be justified by the requirement and validated by checks you understand.
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