architect

name	architect
description	Interactive stack interrogation, design artifact generation, decision verification, and learnings persistence. Runs after BRD approval, before spec decomposition.
argument-hint	[--post-build]

Architect Skill

Interactive technical design phase. The architect reads the BRD, interviews the human about stack and deployment decisions, generates machine-readable design artifacts, verifies completeness, and persists decisions for cross-project reuse.

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Usage

/architect              # Full interactive flow (Phases 1-4 + learnings)
/architect --post-build  # Post-build learnings update only (Phase 5)

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Prerequisites

• specs/brd/ must contain an approved BRD (app spec and/or feature specs)
• .claude/learnings/ should exist (created by /scaffold)

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Interactive Flow (default)

Step 1 — Read Context

Read in order:

1. specs/brd/ — all approved specs
2. .claude/learnings/stack-decisions/ — all prior project records
3. .claude/learnings/integration-notes/ — any relevant API notes
4. .claude/learnings/failure-patterns/common-failures.md

Summarize relevant learnings to the human before starting questions.

Step 2 — Stack Interrogation (up to 11 rounds)

Conduct one round at a time. Wait for human response before proceeding. Rounds 7-9 are conditional — only activate if the BRD describes the relevant requirements (agentic system, ML, user data).

Round 1 — Backend: Present 2-3 options informed by BRD requirements. Challenge mismatches. Round 2 — Database: Analyze data patterns from BRD. Challenge schema-stack mismatches. Round 3 — Frontend: Framework, styling, state management. Round 4 — AI/LLM Model Selection: Which models power the forge agents? Cloud-only, hybrid, or local-only routing. Round 5 — Deployment: Dev environment, target deployment, CI/CD, external services. Round 6 — Verification mode: Docker/local/stub verification config. Round 7 — Agentic Architecture (if BRD describes agents): Agent count, protocols (MCP/A2A), communication pattern, framework, human oversight model. Round 8 — AI/ML Pipeline (if BRD involves ML): Models, training/inference, batch/real-time, RAG components, vector DB, monitoring. Round 9 — Governance & Compliance (if BRD involves user data or AI decisions): Regulations (GDPR/HIPAA/SOC2/AI Act), PII handling, fairness requirements, audit trail. Round 10 — Context & Cost Budget: Build budget, token strategy, prompt caching. Round 11 — Verify & Challenge: Summarize ALL decisions (including AI/compliance). List concerns.

See agents/architect.md for detailed question templates and challenge examples.

Step 3 — Generate Project README and Makefile

Immediately after stack confirmation (not post-build), generate:

1. README.md at project root — fill the template from .claude/templates/README.project.template.md with all stack decisions, setup instructions, run commands. Developers need this from day one to make install && make dev.

2. Makefile at project root — fill from .claude/templates/Makefile.template. Must include: install, dev, test, lint, typecheck, migrate, seed, docker-up, docker-down, clean. Adapt commands to the chosen stack (e.g., uv run pytest vs pytest, pnpm vs npm).

These are generated NOW, not post-build. A project without a README and Makefile is not usable.

Step 4 — Generate Design Artifacts

After human confirms stack, generate all artifacts to specs/design/:

• architecture.md — component diagram, tech choices with rationale
• api-contracts.md + api-contracts.schema.json — typed endpoints
• data-models.md + data-models.schema.json — Pydantic + TS + DB
• component-map.md — story → file mapping with Produces/Consumes
• folder-structure.md — complete file tree
• deployment.md — Docker Compose, env vars, migrations

Also generate/update:

• project-manifest.json — complete stack config (fills skeleton from scaffold)
• calibration-profile.json — UI standards config based on project type

Step 4 — Decision Verification Gate

Run self-check before presenting to human. Flag any gaps:

• Every BRD feature → API endpoint
• Every data entity → model
• No upward layer imports in folder structure
• External APIs → typed wrappers in component map
• All env vars documented

Step 5 — Write Learnings

Write stack decision record to .claude/learnings/stack-decisions/{project-name}-stack.md. Update _index.md.

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Post-Build Mode (--post-build)

Invoked by /auto after build completes. Reads learned-rules.md and failures.md, fills in:

• "Verdict after build" per decision
• "Patterns That Worked"
• "Patterns to Avoid Next Time"
• "Recommendations for Similar Projects"

Also writes/updates integration notes for any external APIs used during the build.

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Gotchas

• Not reading learnings first: Always read existing stack-decisions before interviewing. Past project experience prevents repeating mistakes.
• Accepting weak reasoning: "I'm familiar with it" is not sufficient for a production choice. Challenge with BRD-specific trade-offs.
• Incomplete component map: The generator needs unambiguous file ownership. If two stories might touch the same file, designate an integrator.
• Missing env vars: Every external service needs env vars in .env.example. Missing vars cause deploy failures.
• Skipping verification gate: Gaps found at implementation time cost 10x more. Run the self-check.