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AI coding agents follow project architecture more consistently when design decisions live in version-controlled repository instructions and reusable task templates—not only in a one-off chat prompt. Put stable rules and links to source-of-truth documents in the repository’s agent instructions; use focused prompt files for repeatable work such as feature changes, migrations, reviews, and test repair. Then require the agent to discover, plan, implement, verify, and report its work against explicit acceptance criteria.
What a design template does for a coding agent
A design template is a maintained project artifact that translates an engineering decision into guidance an agent can apply repeatedly. It can tell the agent which layer owns a behavior, where implementation and tests belong, which interfaces must remain compatible, and what commands define a successful change. It should reduce guesswork—not replace design judgment or the project’s authoritative architecture and API documentation.
Think of the template as a small control system: repository instructions establish durable context; task-specific prompts describe a recurring job; source documents define contracts; and verification plus diff review catch departures. GitHub describes repository custom instructions as persistent project context and prompt files as reusable instructions for a particular interaction. Microsoft’s Visual Studio Code guidance likewise recommends giving agents codebase structure, commands, and conventions as repository customization rather than expecting them to infer those details.
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The practical distinction is scope. A rule such as “database access belongs in the repository layer” applies broadly and belongs in stable instructions. A procedure such as “for a schema migration, add a reversible migration, update fixtures, and run these checks” is specific to a task and belongs in a reusable prompt. Neither should duplicate a large architecture document: link to the current source of truth and explain which parts matter for the task.
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Build a reusable design-template file
Start with a focused Markdown document that a developer can read, review, and version alongside the code. Keep every instruction actionable: name paths, commands, interfaces, and observable outcomes. The following outline works as a task template; adapt it to the repository rather than copying it unchanged.
- Goal and scope. State the user-visible outcome and what the change must not alter. Define exclusions explicitly—for example, whether an existing API response shape is out of scope.
- Architecture boundary. Name the responsible layer or module, allowed dependencies, and shortcuts to avoid. Where a rule comes from, link to the project’s architecture document rather than paraphrasing a long specification.
- Interfaces and data contracts. Point to the API schema, event definitions, types, or examples the implementation must follow. Say whether compatibility must be preserved and how errors should be represented.
- Repository map. Give the actual locations for production code, tests, migrations, fixtures, and documentation. Explain which existing example is the closest pattern to follow.
- Implementation pattern. Describe the sequence or provide a short canonical example. Prefer a small, relevant example over a large code sample that may drift from the implementation.
- Quality gates. List exact build, lint, type-check, and test commands, including required environment or fixtures. Distinguish mandatory checks from optional ones.
- Acceptance criteria. Specify testable behavior, edge cases, security constraints, and known performance limits. Do not invent numerical limits when the project has not established them.
- Handoff. Require a changed-file summary, verification results, deviations from the template, and unresolved questions. Ask the agent to report a failed check rather than imply it passed.
GitHub’s guidance on repository instructions recommends covering architecture, important directories, build and test commands, coding conventions, and what completion requires. Treat those as useful categories, not a demand to turn every instruction file into a full handbook. Stable context should stay concise enough to remain discoverable; task detail belongs in the task prompt or linked documentation.
Example: a feature-work prompt
# Feature change: [short name]
## Goal and boundaries
Implement: [observable outcome].
Do not change: [interfaces, modules, or behavior out of scope].
## Read first
- Architecture: [repository path or link]
- Contract/schema: [repository path or link]
- Existing example to follow: [repository path]
## Placement and constraints
- Production code belongs in: [path]
- Tests belong in: [path]
- Keep dependencies within: [allowed boundary]
- Preserve: [compatibility or security requirement]
## Acceptance criteria
- [Observable behavior]
- [Failure or edge case]
## Verify
Run: [exact command]
Run: [exact command]
## Work sequence and handoff
First identify relevant files and propose a short plan. Make the smallest coherent change.
Report changed files, commands run and their results, deviations, and unresolved questions.
Replace each bracketed item with repository-specific facts before adopting the file. A template that says “run the tests” without identifying the command or environment leaves the agent—and the reviewer—to guess. Likewise, a file path should point to a real, maintained document, not a hoped-for convention.
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Put instructions where each tool can use them
Keep the underlying content portable where possible, but do not assume a filename or configuration mechanism is universal. GitHub documents repository custom instructions and prompt files; its agent-instruction conventions also name AGENTS.md, CLAUDE.md, and GEMINI.md. These names are conventions, not a guarantee that every agent automatically reads every file. Confirm the behavior of the specific tool and repository setup you use.
Cursor documents rules and skills alongside planning and review workflows. OpenAI provides Codex and agent-building documentation for coding-agent workflows, but the material here does not establish a complete, current feature-by-feature configuration matrix for Copilot, Cursor, and Codex. Before relying on a particular filename, prompt-file format, rule scope, or permission behavior, check that product’s current documentation and verify it in the repository.
A sensible organization is to keep broadly applicable project context in the repository’s instruction mechanism, then add task prompts for jobs with their own steps and checks. Avoid maintaining two competing copies of the same architecture rule. If a task prompt must repeat a constraint for clarity, point back to the canonical rule and update both deliberately when it changes.
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Run the template as a workflow
1. Discover before editing
Ask the agent to read the repository instructions and the relevant linked documents first. It should identify the modules, existing implementation examples, tests, and commands that apply to this change. This step is especially useful when a repository has multiple applications or layers with similar names: the template should lead the agent to the correct one, not merely describe the whole system.
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For substantial work, request a short plan before implementation. It should name likely files, interfaces, risks, and verification checks. Cursor’s documented workflow presents planning, building, and review as stages; the same separation is useful regardless of which agent you use. Correct a mistaken plan before asking the agent to proceed, particularly when it proposes crossing an architecture boundary or adding an unnecessary abstraction.
3. Implement the smallest coherent change
Direct the agent to reuse an existing component or example when one fits, and to avoid expanding scope to adjacent refactors. A template should clarify the intended pattern without prescribing every line. If a decision is genuinely unsettled—such as how a new failure case should appear in the interface—the agent should surface the question rather than silently create a new convention.
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4. Verify against explicit gates
Require the exact checks named in the template, and ask for the outcome of each. A command that cannot run because a service, fixture, or environment variable is unavailable is not a passing check; record the limitation and the next step. Where behavior is important but not covered by the listed suite, add or identify a focused test rather than treating a successful build as proof of acceptance.
5. Review the diff and hand off
Inspect the changed files against the architecture boundary, contracts, acceptance criteria, and exclusions. The agent’s handoff should make it possible to distinguish implemented behavior from claims about verification. If the output exposes ambiguity—for example, two plausible test locations or an unstated error contract—revise the template or its linked source document, then use the corrected guidance on the next task.
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Choose based on how a tool takes repository context, supports reusable task guidance, and lets your team verify work—not on model branding alone. The documented capabilities below are deliberately limited to what is established here; availability and exact configuration can change, so consult the current product documentation before standardizing a setup.
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| Tool | Documented configuration or workflow | What to verify for your repository |
|---|---|---|
| GitHub Copilot | GitHub documents persistent repository custom instructions, reusable prompt files, and agent-instruction file conventions. | Which instruction and prompt-file formats your Copilot surface and repository setup currently recognize. |
| Cursor | Cursor documents planning, rules, skills, MCP connections, and review workflows. | How your version scopes rules and skills, selects repository context, and handles tool permissions. |
| OpenAI Codex | OpenAI provides Codex and agent-building documentation for coding-agent workflows. | The exact repository instruction, prompt, permission, and review controls available in the Codex environment you use. |
For all three, check repository indexing and context selection as well as configuration: an instruction is useful only if the agent can find the relevant files and source documents. Also decide who owns the template, how changes are reviewed, and where teammates will discover it. Plain Markdown is relatively portable as content, but product-specific files and controls still need validation in each tool.
Troubleshoot templates that produce inconsistent results
- The agent ignores a rule. Confirm that the selected tool reads the file and that it is in the supported repository location. Put the requirement in the applicable instruction mechanism, make it concrete, and ask during discovery which instructions it found.
- The agent edits the wrong layer. Add the responsible module path, an existing example to follow, and the dependency boundary. A broad instruction such as “follow the architecture” does not identify the correct implementation location.
- The agent invents commands or reports checks vaguely. List exact commands and any required environment. Require the handoff to include commands actually run and results, with blocked or unavailable checks labeled as such.
- The prompt conflicts with the code or another document. Identify and update the source of truth, then remove stale duplicate wording. Ask the agent to surface the conflict rather than choose silently.
- The template is too long or too generic. Move detailed contracts into linked documents and split recurring jobs into focused prompts. Retain only context that materially changes the task’s implementation or verification.
- The output meets the prompt but violates an unstated convention. Add the missing path, example, constraint, or acceptance test. Treat the failure as evidence that the template needs maintenance, not as a reason to add a vague instruction such as “be careful.”
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Maintain templates like engineering code
Assign an owner, review changes alongside the architecture they encode, and keep task prompts discoverable in the repository. When a framework, directory structure, command, or API contract changes, update the corresponding guidance. A good template does not promise that an agent will always infer intent correctly; it makes the intended boundaries visible, the required checks repeatable, and mistakes easier to catch in review.
Frequently Asked Questions
Can one Markdown template work across different coding agents?
The core guidance can be written portably in Markdown, but each product may require its own supported file location or configuration. Verify the format and scope in the current documentation for the agent you use.
Should a design template replace architecture documentation?
No. Keep detailed architectural decisions and contracts in their authoritative documents; have the template link to the relevant sections and explain what the agent must apply for the task.
Quick Recap
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