Development & Integrations

How to Organize AI Coding Requirements with SPECS.md.

A workflow that fixes AI coding requirements in a SPECS.md file and separates spec, plan, tasks, and implementation so you do not lose track of changes and review criteria.

Step-by-step instructions

  1. 1

    Write down the success conditions in a document

    Record user flows, constraints, completion criteria, and how you will verify them in SPECS.md before making any implementation request.

  2. 2

    Break the spec into an implementation plan

    Lay out the files and data to change, the risks, and the test approach as a separate plan, reducing how much the agent has to guess.

  3. 3

    Split the work into verifiable units

    Break the task list into small completion conditions and tie the tests needed to each task.

  4. 4

    Reflect changes in the spec, not just the conversation

    When requirements change, update SPECS.md and the plan first, then check again that the implementation and the documents still match.

Leave a reference document instead of a conversation

In AI coding, the longer a conversation runs, the easier it is to lose track of what has been settled. SPECS.md keeps product requirements and completion criteria in one place, so a new session or a different agent works from the same standard.

Don't mix the spec and the implementation in one step

GitHub Spec Kit's basic flow separates Spec, Plan, Tasks, and Implement. Each output becomes the input to the next step, so it is easier to find missing constraints and tests before implementation starts.

Documents are also subject to change

If requirements change and only the code changes, the earlier assumptions come back to life in the next task. Reflect the reason for the change and its scope of impact in the spec, plan, and task list together, and check them against the actual implementation.

Official resource: GitHub Spec Kit