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How the workflow fits together
GitHub Spec Kit describes its core process as Specify → Plan → Tasks → Implement → Converge. The sequence makes intent, technical choices, work items, and remaining gaps visible at different stages. It is a process for directing and reviewing an agent—not a guarantee that generated code is correct.
- Specify: describe the user, problem, expected behavior, and success criteria.
- Plan: add the stack, architecture, integrations, and other implementation constraints.
- Tasks: turn the plan into small, ordered, testable pieces of work.
- Implement: ask the agent to execute those tasks, reviewing changes as it goes.
- Converge: compare the implementation with the specification, plan, and task list; add and complete tasks for any gaps.
This structure is useful whether you are starting a project, adding a feature to an existing codebase, or modernizing legacy software. GitHub presents these as intended use cases, not as independent evidence that the approach improves delivery speed or quality. GitHub Spec Kit documentation and GitHub’s explanation of specification-driven development describe the rationale and workflow.
What should go in a software feature specification?
Keep the specification focused on the feature’s purpose and observable behavior. It should help a person decide whether the finished feature meets the need without prematurely locking in the technical design.
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- Purpose: the problem being solved and the intended outcome.
- Users and journeys: who uses the feature and what they need to accomplish.
- Expected behavior: what the system should do in the normal case and relevant edge cases.
- Acceptance expectations: concrete conditions that would demonstrate the requirement is met.
- Open questions: assumptions and ambiguities the agent should surface rather than silently resolve.
Ask the agent to draft the specification from your initial description, then review the wording and answer its questions. Correct misunderstandings before moving on: a polished specification can still encode the wrong requirement.
Keep the specification, plan, and tasks distinct
Each artifact answers a different question. Keeping those answers separate makes it easier to spot when a technical proposal has changed the user-facing requirement or when a task does not serve an agreed outcome.
| Artifact | What belongs in it | Question it answers |
|---|---|---|
| Specification | User-facing behavior, goals, user stories, outcomes, edge cases, acceptance expectations | What should happen, and why? |
| Plan | Technology stack, architecture, integration strategy, technical constraints, design decisions | How should the accepted requirements fit the system? |
| Tasks | Ordered implementation steps, dependencies, and concrete completion criteria | What work needs to happen, and in what order? |
| Verification record | Checks performed, observed results, remaining gaps, and follow-up tasks | What has actually been checked? |
The first three distinctions reflect the Spec Kit quickstart and workflow guidance. Treat the verification record as your own oversight: do not mark a test as passing merely because an agent generated it or says it ran successfully.
How to get an AI coding agent to follow a specification
1. Establish project principles
For recurring work, record durable project principles once—for example, conventions or rules that later specifications and plans should respect. In Spec Kit, the constitution serves this role. It provides project context; it does not replace the feature’s requirements.
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2. Draft and review the specification
Give the agent the feature goal and relevant user context. Ask it to produce a focused specification, identify assumptions, and list unanswered questions. Read the result yourself and make the expected behavior and acceptance conditions specific enough to review.
3. Clarify consequential ambiguity
Resolve uncertain behavior before planning when it could affect permissions, edge cases, or whether the feature is acceptable. Spec Kit’s clarify step is an optional quality gate; it is particularly useful when requirements are incomplete or open to materially different interpretations.
4. Provide technical constraints for the plan
Once behavior is agreed, give the agent the information it needs to fit that behavior into the system. Include the required stack, architecture, integration boundaries, performance requirements, security or compliance needs, and existing project conventions. For an existing codebase, repository conventions and system boundaries matter alongside the feature itself.
5. Check quality and consistency before implementation
For consequential work, review a requirements checklist and analyze the specification, plan, and tasks for omissions or conflicts. Fix problems in the artifacts themselves and run the review again before asking the agent to code. This catches mismatches early—for example, a plan that omits an acceptance condition or a task list that never implements a specified behavior.
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Ask for concrete, reviewable tasks with dependencies represented clearly. A task should be small enough to inspect and test, and its completion criteria should connect to the requirement it supports. Parallelize only when the work is separable; otherwise, dependent changes are easier to review in sequence.
7. Implement with review points
Have the agent work through the tasks incrementally. Inspect focused changes and verify behavior as the work progresses, rather than treating generated artifacts or code as proof of completion. Developer judgment remains necessary: a mistaken requirement, incomplete plan, or missed task can still lead to the wrong result.
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8. Converge against the agreed intent
Compare the finished codebase with the specification, plan, and task list. If behavior is missing or a requirement is not met, add the necessary task, implement it, and check again. Report only checks that were actually performed and results you have evidence for.
Should you write a spec before asking AI to code?
For a small, straightforward change, the specification can be brief, and the process need not become ceremony. The Spec Kit quickstart’s shorter route is constitution, specify, plan, tasks, implement, and converge. For production work or requirements with important ambiguity, the fuller route adds clarification, a requirements checklist, and cross-artifact analysis before implementation.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Using Spec Kit with an agent
Spec Kit’s documentation lists integrations including GitHub Copilot and Codex, as well as a generic integration for other tools. The list and command syntax can change, so check the current Spec Kit documentation and its Agentic SDD reference for your chosen agent. The reference documents /speckit-* commands for Copilot’s skills mode and $speckit-* for Codex and some other agents; invocation depends on integration and mode.
The installation guide documents installing the Specify CLI with Python package tooling and initializing a project with an explicit integration. For example:
uv tool install specify-cli
specify init my-project --integration copilotFor an existing, non-empty project, follow the installation guide and its existing-project instructions; the guide documents a force option that acknowledges a merge warning. Git is optional for the core setup and is required only if you enable the Git extension. Verify the current commands and version guidance in the official guide before using them.
What changes when requirements or interfaces change?
Decide how your team will keep specifications, plans, and tasks current as requirements evolve. The Spec Kit concept documentation does not prescribe one universal way to preserve or update spec.md, plan.md, and tasks.md. Establish a local rule for recording a changed requirement and updating the implementation work it affects; otherwise, artifacts can stop describing the code being built.
When separate components expose interfaces to outside consumers, the documentation recommends agreeing on observable obligations before either side is implemented; it points to contract-driven development for that purpose. See What is Spec-Driven Development?.
What the method does—and does not—establish
Specification-driven development makes intent and review points explicit, but it cannot by itself ensure that requirements are right, constraints are complete, or tasks cover every necessary change. The developer still needs to review the artifacts, inspect the code, and verify the behavior. The official materials cited here do not provide an independent effectiveness statistic or controlled comparison showing how much the method improves software outcomes.
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