Note: This FAQ is a hypothetical example created for demonstration purposes. It illustrates how to document and answer common questions about the Spec-Driven Workflow and is included in this repository as sample documentation.
It eliminates inconsistent AI‑native delivery by packaging a repeatable specification workflow that keeps teams aligned on work breakdown, shared context artifacts, and the tooling handoffs needed to ship.
Any team trying to level up AI‑native delivery. You can adopt the workflow a piece at a time, layering in components without committing to the full stack on day one.
Teams install the workflow via package managers or MCP, then use its commands to maintain context, drive consistent work breakdown, and keep AI agents operating inside agreed guardrails.
The workflow runs with minimal setup—many teams start with the prompts alone and layer in automation when they are ready.
The workflow is designed to be usable with many different AI agents and work‑tracking systems—even multiple tools in the same repo or project. It exposes connectors for AI agents, ticketing systems, and documentation hubs so the same plan, specs, and progress data is available everywhere, or teams can skip connectors and keep everything in‑repo as Markdown.
Templates stay static; the workflow ships as a versioned package that you upgrade like any package, so improvements arrive without overwriting your customizations. The workflow also provides working commands and tools, not just documentation guidelines.
You keep it. The workflow provides commands that wire into your existing project structure—your current ADR folders, ticket conventions, or roadmaps. You don't need to rewrite your documentation or reorganize your repos. The workflow layers consistency on top of what is already working.
Commands and scaffolds steer teams toward skateboard‑to‑scooter increments: create small testable slices, validate learning, and only then scale. Prompts explicitly ask you to define the skateboard (minimal testable value), scooter (enhanced but still lean), and car (complete product) so teams discuss iteration sizes up front.
Yes. You can keep everything in a single repository using Markdown files with no external dependencies. Tool integrations and multi‑repo features are optional.
The planning step includes a mandatory audit gate after task generation. The AI produces an audit report that checks requirement coverage, proof artifact quality, repository standards consistency, open-question handling, and context alignment against related docs (such as specs, PRDs, or roadmaps). Required failures block implementation handoff until remediated.
No. Remediation is human-in-the-loop. The AI presents findings and a concrete remediation plan, then waits for explicit user approval before editing planning artifacts. After approved edits, the AI reruns the audit. This loop repeats until required audit gates pass.
Yes. The same context and work‑breakdown helpers make it easy to pause and resume personal projects while keeping AI assistance on track.
Through layering. Local overrides and configuration live outside the distributed files, so teams version their adjustments separately and apply workflow updates without merge conflicts. The specific mechanism is still being refined.
Minimal. If you can write Markdown, you can use the workflow. The templates and commands guide you through the process. Most teams are productive in their first session.
Rapid experimentation with AI agents creates drift between squads. Spec‑Driven Workflow delivers a shared operating model with minimal setup, giving leaders confidence that every iteration follows the same proven playbook. The cost of adoption is low and benefits compound as your team scales.
The workflow is open source. Enterprise‑grade connectors and support bundles are available as add‑ons.