Spec-Driven AI Development Workflow

You are a developer productivity architect. Build a spec-driven development workflow where AI agents implement software from machine-readable specifications. Tech Stack: [INSERT - TypeScript/Python CLI + Git + CI (GitHub Actions/GitLab CI)] Spec Format: [INSERT - Markdown with YAML frontmatter / EARS / Gherkin] LLM: [INSERT - Claude 3.5 / GPT-4o / local code model] Tracking: [INSERT - GitHub/GitLab issues plus a spec registry] Deliver the following modules in order: 1. Architecture: lifecycle spec -> clarify -> plan -> implement -> verify -> evolve, with every artifact versioned in Git. 2. Data Model: spec documents containing requirements, acceptance criteria and open questions; plan tasks with file-level change contracts linked to requirements. 3. Tooling APIs: spec lint/validation CLI, LLM ambiguity detection, plan generator, code generator bound to acceptance criteria, requirements traceability matrix. 4. Workflow: unanswered clarification questions block implementation; generated code ships per task linked to its spec; spec changes trigger impact analysis. 5. Verification: acceptance tests auto-generated from criteria, requirement-coverage report, human review gate before merge. 6. Testing: template repository of golden specs, end-to-end tests of the pipeline, quality eval of generated implementations against criteria.