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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesBefore asking an AI coding agent to change a project, make the project’s constraints easy to find and review the plan for complex work. The useful “file order” is an order of information: repository facts and instructions first, a task-specific plan next, code changes after review, and validation before integration. No particular alphabetical or filesystem order is universally required, and official guidance does not quantify how much this workflow improves outcomes.
Why constraints should come before code
An AI coding agent does more than produce one block of code. It gathers context, takes tool actions, evaluates what happened, and repeats. If important project rules are absent or hard to locate, the agent may make decisions without the architecture, conventions, or requirements that should shape them.
For complex tasks, Visual Studio Code recommends researching the codebase, clarifying requirements, and proposing an implementation plan before code changes begin. That guidance supports a practical sequence—not a guarantee that a particular file arrangement produces better code. The official sources do not report a measured improvement for this exact workflow.
What to put in the repository first
Gather facts from the project
Start with the repository and its authoritative documentation. Identify relevant architecture, product behavior, dependencies, conventions, and local build and test practices. Use what the project actually says rather than asking the agent to infer constraints that could have been documented.
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VS Code’s context-engineering guidance describes using project Markdown and custom instructions to give an agent relevant context. GitHub recommends that an instruction file clearly summarize the codebase and what the software does. See VS Code’s context-engineering guide and GitHub’s project-improvement guidance for Copilot cloud agent.
Keep the entry point short and link to deeper documentation
Use a supported repository instruction file—such as AGENTS.md where the chosen agent recognizes it—as a concise map. Put stable, project-wide constraints and pointers there; keep fuller architecture, product, contributor, and technical-principle material in focused documents. OpenAI describes this as progressive disclosure: an oversized instruction file can crowd out task details, code, and relevant documentation, while a short entry point can guide the agent to a structured knowledge base.
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OpenAI’s account of its agent-first engineering workflow is one example of this approach. The exact instruction filename and loading behavior depend on the tool, so confirm the format supported by the agent or editor you use.
Scope special rules to the paths they govern
Keep repository-wide rules separate from instructions that apply only to a folder or file type. For example, a convention for one package should not be presented as a universal rule unless it truly applies everywhere. GitHub documents repository-wide and path-specific instructions as distinct mechanisms for Copilot; support varies across Copilot features and other coding agents.
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Use this order for a coding-agent task
- Gather current project facts. Inspect the relevant code, documentation, conventions, dependencies, and local build or test practices.
- Write or update the concise entry point. Put durable, project-wide constraints and links to deeper sources of truth in the supported repository instruction file.
- Add scoped instructions where needed. Put folder- or file-specific rules where the selected tool will load them, rather than making every constraint global.
- Describe the task and plan. For complex work, ask the agent to clarify requirements, inspect relevant code, and propose intended edits and useful checks before implementation. Review and refine that plan first.
- Implement against the reviewed plan. Ask the agent to make the agreed changes, using the project context and scope-specific rules.
- Validate and inspect before integrating. Review the diff, check assumptions and edge cases, assess error handling and security, and run relevant tests. Do not treat an agent’s report of success as a substitute for examining the result.
- Maintain the documentation. Update instructions when project conventions or structure change, and check that links and references remain useful.
VS Code’s overview of AI agents explains the agent loop and its Plan agent guidance. Its best-practices guidance also recommends planning first for complex work and reviewing and testing generated changes.
How much planning does the task need?
Match the process to the change. A small, self-contained edit may need only clear task context and the normal agent loop. A complex or multi-file change benefits from separating planning from implementation: inspect relevant code, resolve requirements, review the plan, then make and verify the changes. Planning is useful when it surfaces decisions before they become edits; adding paperwork to a trivial change is not the goal.
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A practical repository layout
This is an illustrative organization, not a required standard. Adapt it to the instruction formats your tool actually supports.
AGENTS.mdor the tool-supported repository instruction file: a short project map, hard constraints, and links to relevant documents.- Architecture, product, and contributor documentation: the detailed sources of truth for how the project works and how changes are made.
- Path-specific instruction files: conventions that apply only to particular directories or file types, if the agent supports them.
- A task plan: requirements, intended edits, and checks for substantial work.
- Source code and tests: the implementation and evidence used to verify it.
The point is not to make a coding agent read files in a universal physical sequence. It is to make stable project context discoverable before a task-specific plan is reviewed, and to put implementation and verification after that review.
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- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
What this workflow can—and cannot—promise
Official guidance from VS Code, OpenAI, and GitHub supports making repository context available, planning complex tasks, scoping instructions, and reviewing and testing generated code. It does not establish that writing constraints first guarantees correct output or produces a specific time saving, accuracy gain, or reduction in defects. AI-generated code can still contain bugs, security issues, and subtle logic errors, so human review and relevant validation remain part of the workflow.
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