Cross-Platform AI Usage

Agents.md

What It Is

Agents define a structured workflow so tasks are broken into clear steps, with explicit inputs and outputs.

How to Use It

Read Agents.md.
Choose a workflow that matches your task.
Provide the inputs in a short, bounded request.

Example Request

Goal: Improve readability in this module
Inputs: src/foo/Bar.kt, keep behavior the same
Output: A small refactor and a short summary
Verification: No tests needed

When to Use Agents vs Chat

Use agents for multi-step tasks like refactors, doc audits, or migrations.
Use chat for quick questions or one-off explanations.

Request Template

Use this structure to get consistent results:

Goal: <one sentence>
Inputs: <files, constraints, context>
Output: <expected deliverable>
Verification: <tests or checks>

Prompting Patterns

Refactors: "Refactor this file to improve readability without changing behavior."
Tests: "Add unit tests for this service. Keep the existing public API."
Debugging: "Explain this error and list likely fixes in order."
Understanding code: "Summarize what this module does and call out risks."

Example: Too Broad vs Scoped

Too broad: "Fix everything in this project." Scoped: "Refactor this file to remove duplication. Do not change behavior."

Plan-First Workflow

Ask for a short plan.
Approve or adjust the plan.
Ask for targeted changes.
Verify with tests or review.

Example Plan Request

"Provide a 5-step plan to refactor this module. Wait for approval before edits."

Connecting Skills

Use skills for tasks with known workflows.
Combine multiple skills when a task spans domains.
Pass concise context between steps to reduce repetition.

Efficiency Tips

Keep prompts small and scoped.
Reuse context from earlier steps instead of repeating it.
Ask for summaries before asking for edits.

Example Summary Request

"Summarize the decisions so far in 6 bullets so I can start a new chat."

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