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docs/faq.md

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+# Frequently Asked Questions
+
+### What problem does the Spec‑Driven Workflow solve?
+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.
+
+### Who should use it?
+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.
+
+### How is it installed?
+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.
+
+### Do we need any prerequisites?
+The workflow runs with minimal setup—many teams start with the prompts alone and layer in automation when they are ready.
+
+### How does it work with different tools?
+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.
+
+### What makes it different from documentation templates?
+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.
+
+### What if we already have an established process?
+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.
+
+### How does it guide iteration size?
+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.
+
+### Can it work entirely in Markdown in one repo?
+Yes. You can keep everything in a single repository using Markdown files with no external dependencies. Tool integrations and multi‑repo features are optional.
+
+### Can solo developers use it?
+Yes. The same context and work‑breakdown helpers make it easy to pause and resume personal projects while keeping AI assistance on track.
+
+### How does customization work?
+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.
+
+### What’s the learning curve?
+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.
+
+### Why adopt a spec‑driven workflow now?
+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.
+
+### What does it cost?
+The workflow is open source. Enterprise‑grade connectors and support bundles are available as add‑ons.

docs/press-release.md

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+# Liatrio Launches Spec-Driven Workflow: A Codified, Versioned, Installable Framework for AI-Native Development
+
+**October 18, 2025 —** Liatrio, an enterprise transformation consultancy, announced the Spec-Driven Workflow, an installable and versioned package that gives AI–native development teams a consistent way to ship features across products and platforms. The workflow drops into any repository like a standard package and instantly aligns contributors on the same work breakdown and execution model.
+
+## The Problem: AI–Native Development Creates Inconsistency
+
+Engineering teams adopting AI coding assistants face a challenge: each developer creates their own approach to prompting AI agents, organizing context and breaking down work. Architecture decisions scatter across Slack threads and wikis. One team ships small batch increments while another over‑engineers the first iteration. Context gets lost between sessions and across repositories. The problem further compounds across repositories – a platform with ten microservices needs ten consistent context stores, but maintaining that consistency manually is brittle. Teams either give up on standardization or spend engineering cycles fighting merge conflicts in shared templates.
+
+## The Solution: Workflow as a Versioned Component
+
+Liatrio’s Spec‑Driven Workflow treats your development process like any other library in your stack. Install it via `npx` or use it as an MCP, and it wires consistent structured context, work breakdown patterns and AI‑agent guidance into your project — whether you’re working on a weekend prototype or a multi‑repo platform. It provides a stable foundation even as teams use different or evolving AI tools, creating consistency where tool diversity surfaces friction.
+
+The workflow features:
+
+- Repeatable work breakdown guides small batch deployable increments that keep feedback tight and progress visible.
+- Scales from solo developers to enterprise platforms and keeps artifacts in predictable locations across single repos, multi‑repo platforms, or external storage — the same logical artifacts and semantic links regardless of location.
+- Lightweight and minimal framework avoids overburdening context for AI agents and humans.
+
+Engineering leaders already see impact:
+
+> “Liatrio’s Spec‑Driven Workflow cut our context switching in half,” said Jordan Ramos, VP Engineering at VectorScale. “Specs, tickets, and agent prompts now live in the same backbone, and in our pilot quarter AI‑generated changes landed right the first time 40 percent more often.”
+>
+> “Product teams finally describe outcomes the same way engineering builds them,” added Maya Chen, Director of Product at Skyline Robotics. “We now draft outcome specs in hours instead of days because the workflow keeps every ticket, prompt, and roadmap in sync.”
+
+## Built to Scale
+
+The workflow scales from solo developers to enterprise platforms:
+
+- **Single repository**: Keep everything local in Markdown files.
+- **Multi‑repository**: Maintain consistent context across repos with 1:1 mappings between context artifacts.
+- **External context storage**: Context can live outside repos entirely while maintaining the same structure.
+- **Prompt‑first**: Works with just prompts — no heavy tooling required.
+- **Multi‑tool**: Use different AI agents and work‑tracking systems together in the same project.