methodologylisted

# AI-First Development Methodology ## What Is This A structured development approach for AI agents. Every feature goes through a pipeline: idea → spec → architecture → tasks → implementation → documentation update. Each stage has automated validators and quality gates. QA and deploy are regular tasks in the tech-spec, not separate pipeline steps. Core problems it solves: - **Context loss between sessions** — distributed knowledge base persists across sessions - **Quality without human review** — automated validators at every stage - **Scope creep** — specs approved before coding starts - **Outdated agent knowledge** — Context7 MCP fetches current library docs --- ## Development Pipeline The full path from idea to production. Each step has a command, a skill behind it, and validators. ### Step 1: User Spec — `/new-user-spec` **What:** Structured interview to capture requirements in human-readable form (Russian). **Process:** - Agent reads Project Knowledge files to understand the project - Scans codebase for relevant code, patterns, integration points - Runs 3 interview cycles with the user (general → code-informed → edge cases) - `interview-completeness-checker` agent verifies coverage - Creates `user-spec.md` from interview data → git commit draft - 2 validators run in parallel (up to 3 iterations): - `userspec-quality-validator` — document structure, acceptance criteria testability - `userspec-adequacy-validator` — solution feasibility, over/underengineering -