/scaffold — Greenfield Architecture Scaffolding

Generate a scaffold requirements.md through an architecture-focused derivation chain. Produces a complete development foundation that AI agents can extend consistently.

---

Core Identity

scaffold is specify's architecture variant. Same requirements.md format, different weight center.

	specify	scaffold
Focus	What to build (features)	How to structure (architecture + harness)
L2 weight	Moderate (feature decisions)	Heavy (tech stack, patterns, infra)
L3	Requirements (behavioral)	Harness (domain, team, skills, hooks, rules)
L4	Verification journeys	Requirements + Harness Decisions (no tasks — /execute writes plan.json)
Tasks	Feature implementation	Project initialization + exemplar + harness
Output	Code changes	Complete development environment + AI harness
When	Feature on existing codebase	Greenfield or major restructure

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Core Rules

1. CLI creates the spec dir — hoyeon-cli req init to create the spec directory and stub requirements.md. Then Write/Edit requirements.md directly.
2. Direct markdown editing — Write requirements.md content using Write or Edit tools. No JSON merge protocol needed.
3. No schema validation — requirements.md is freeform markdown. No spec validate or spec guide commands.
4. Revision Protocol — When user selects "Revise" at an approval gate:
   • Use Edit tool to modify, add, or remove sections in requirements.md directly.
   • No merge flags needed — just edit the markdown.

---

Layer Flow

Layer	What	Gate
L0	Mirror → confirmed_goal, non_goals	User confirms mirror
L1	Environment scan (greenfield detection)	Auto-advance
L2	Architecture interview → decisions + constraints (HEAVY)	User approval
L3	Harness setup → domain, team, rules, skills, hooks	User approval
L4	Requirements + Harness Decisions → requirements (with GWT sub-reqs) + harness decisions	User approval

scaffold produces requirements.md only (no tasks). Task breakdown is handled later by /execute (via /blueprint or inline planning), which writes a sibling plan.json next to requirements.md.

Session Init (before L0)

SPEC_DIR=".hoyeon/specs/{name}"
hoyeon-cli req init $SPEC_DIR --type greenfield --goal "{goal}"

This creates ${SPEC_DIR}/requirements.md with a stub template.

SESSION_ID="[from UserPromptSubmit hook]"
hoyeon-cli session set --sid $SESSION_ID --key spec_dir --value "$SPEC_DIR"

---

L0: Goal

Output: Goal, Non-Goals, Confirmed Goal sections in requirements.md

Mirror Protocol

Mirror the user's goal with scaffold-specific framing:

"I understand you want to build [product/system].
 Architecture scope: [what the scaffold will set up].
 NOT in scaffold scope: [features, business logic — those come later via /specify].
 Done when: [agent can extend the codebase consistently].
 Does this match?"

Key distinction: scaffold's goal is the foundation, not the product. If user says "I want to build a todo app", the scaffold goal is "Set up a web application foundation (server + client + DB) that an agent can extend to build features like a todo app."

Write to requirements.md

Use the Edit tool to write the Goal, Non-Goals, and Confirmed Goal sections into ${SPEC_DIR}/requirements.md.

Gate

User confirms mirror → advance to L1.

---

L1: Environment Scan

Output: Research section in requirements.md

Unlike specify's L1 (which scans existing code), scaffold's L1 scans the environment:

Scan Targets

Target	How	Why
Working directory	ls -la, check for existing files	Greenfield confirmation
Package managers	which npm, which yarn, which pnpm, which bun	Available tooling
Runtime versions	node -v, python3 --version, go version, etc.	Compatibility constraints
Docker	docker --version, docker compose version	Infra capability
Git	git status	Repo state
OS/platform	uname -a	Platform constraints

Write to requirements.md

Use the Edit tool to add a Research/Context section to ${SPEC_DIR}/requirements.md with the environment scan findings.

Gate

Auto-advance to L2 (no user approval needed).

---

L2: Architecture Decisions (HEAVY)

Output: Decisions, Constraints, Known Gaps sections in requirements.md

This is scaffold's core. The interview determines the entire project architecture.

Step 0: Checkpoint Generation

Read L1 environment scan + confirmed_goal, then generate checkpoints per architecture dimension.

Complexity classification — based on confirmed goal:

Signal	Examples
Client-server boundary	web app, mobile + API, microservices
Multiple data stores	DB + cache + queue
Real-time communication	WebSocket, SSE, polling
External service integration	payment, auth provider, AI API
Multi-environment deployment	dev/staging/prod, Docker
Background processing	workers, cron, queues

• Simple (0-1 signals) → 2-3 per dimension
• Medium (2-3 signals) → 4-5 per dimension
• Complex (4+ signals) → 6-8 per dimension

Architecture Dimensions

#	Dimension	Weight	Example Checkpoints
1	Tech Stack	25%	Language/runtime, framework, package manager
2	Communication	20%	Client-server protocol, API style, type safety strategy
3	Data & State	20%	Database choice, ORM/query builder, migration strategy, caching
4	Testing	15%	Test framework, test patterns, coverage strategy
5	DevOps & Environment	20%	Containerization, CI/CD, env config, deployment target

L1 Auto-Resolve: Check each checkpoint against environment scan. Node installed → resolve "runtime" checkpoint. Docker available → partially resolve containerization.

Interview Loop (score-driven)

Same mechanics as specify's L2 but with architecture-specific question framing.

Each round:

1. Score — coverage per dimension
2. Target — lowest-scoring dimension(s)
3. Ask — 2 scenario questions targeting those checkpoints
4. Resolve — mark covered, merge decisions
5. Scan — detect cross-decision tensions
6. Display — scoreboard

Question format — RIGHT (concrete scenario):

AskUserQuestion(
  question: "Your API needs to serve both a React frontend and a future mobile app. How should client-server communication work?",
  options: [
    { label: "REST + OpenAPI + code-gen", description: "OpenAPI spec → Orval/openapi-typescript. Type-safe, well-tooled." },
    { label: "tRPC", description: "End-to-end type safety, no code-gen. TypeScript only." },
    { label: "GraphQL", description: "Flexible queries, schema-first. Higher complexity." },
    { label: "Agent decides", description: "Let scaffold choose based on project context" }
  ]
)

Question format — WRONG (abstract):

AskUserQuestion(question: "What API style do you prefer?", ...)

"Agent decides" handling: When user selects this, scaffold makes an opinionated choice based on:

1. L1 environment capabilities
2. Decisions already made (consistency)
3. Project complexity (simpler for simple projects)
4. Agent productivity criteria (prefer type-safe, convention-over-config)

Record as decision with assumed: true.

Agent Productivity Bias

When making or recommending decisions, bias toward the 4 quality criteria:

Criteria	Bias
Agent extensibility	Prefer convention-over-config, clear naming, predictable patterns
Testability	Prefer dependency injection, pure functions, mockable boundaries
Drift resistance	Prefer strict linting, type checking, boundary enforcement
Type-safe communication	Prefer code-gen over manual types, schema-first over code-first

Conditional Extension Detection

During the interview, detect which conditional extensions to activate:

Signal from Interview	Extension Activated
Client-server boundary detected	Type Contracts (OpenAPI/tRPC/GraphQL schema)
Database mentioned or implied	Data Layer (migrations, connection, seed)
Docker available + multi-service	Docker/Infra (compose, Dockerfile)
Long-running server process	Runtime Patterns (health check, graceful shutdown)

Record activated extensions as decisions:

D_EXT1: "Type Contracts extension activated — OpenAPI + Orval code-gen for client-server type safety"
D_EXT2: "Data Layer extension activated — PostgreSQL + Prisma migrations"

Termination

Composite score uses weighted average across dimensions (weights from the table above). Terminate when: composite >= 0.80, every dimension >= 0.60, unknowns == 0.

Inversion Probe

Two architecture-specific questions:

1. Inversion: "Given these architecture decisions, what scenario would cause a complete restructure even if every component works individually?"
2. Implication: "You chose [most impactful decision]. Does that also mean [architectural consequence]?"

If the probe reveals a critical issue (e.g., contradictory decisions, missing dimension coverage):

• Merge the issue as a known_gap via --append
• Re-enter the interview loop targeting the affected dimension(s)
• Continue until termination criteria are met again

L2 Approval

Present all decisions + constraints + activated extensions. Spawn L2-reviewer:

Task(subagent_type="general-purpose", prompt="""
You are an L2 architecture reviewer for a scaffold spec. Given:
- All architecture decisions
- Activated conditional extensions
- The 4 quality criteria (agent extensibility, testability, drift resistance, type safety)

Check:
1. Do decisions form a coherent stack? (no contradictions)
2. Are the 4 quality criteria addressed?
3. Any activated extension missing its supporting decisions?
4. Any decision that agents will struggle to follow consistently?

Return: PASS or NEEDS_FIX with specific issues.
""")

L2 Gate

Use Edit tool to write all decisions, constraints, and known gaps into the Decisions section of ${SPEC_DIR}/requirements.md.

---

L3: Harness Setup

Output: Harness decisions added to Decisions section in requirements.md

L3 determines the AI work environment for this project. While L2 decides how the code is structured, L3 decides how Claude will work with that code across sessions.

3-1. Domain Context (interactive)

AskUserQuestion(
  question: "Does this project have domain-specific terms or business rules? For example, 'tenant = company-level customer' or 'credit balance must never go negative'.",
  options: [
    { label: "Yes, I'll describe them", description: "You'll provide domain terms and key business rules" },
    { label: "None yet", description: "Skip — can add later to CLAUDE.md" },
    { label: "Agent decides", description: "Infer from project goal if possible" }
  ]
)

If user provides domain terms → record as decision: D_H1: "Domain context: [terms and rules]" These will be written into CLAUDE.md by the Guard Rails task (derived by /execute).

3-2. Team Context (interactive)

AskUserQuestion(
  question: "Are there team conventions for commits, PRs, branching, or code review?",
  options: [
    { label: "Conventional Commits + GitHub Flow", description: "feat/fix/chore prefixes, feature branches, squash merge" },
    { label: "Trunk-based development", description: "Short-lived branches, no long-running feature branches" },
    { label: "Custom — I'll describe", description: "You'll specify your team's rules" },
    { label: "Solo project, no conventions", description: "Skip team context" }
  ]
)

If user provides team conventions → record as decision: D_H2: "Team conventions: [rules]" These will be written into CLAUDE.md by the Guard Rails task (derived by /execute).

3-3. Constraints → Rules (auto + confirm)

Scan L2 constraints[] and propose converting them to .claude/rules/ files:

L2 produced these constraints:
  C1: "pnpm workspace — always use pnpm, never npm/yarn"
  C3: "Dependency direction: shared → client/server only"

These can become auto-enforced rules in .claude/rules/.

AskUserQuestion(
  question: "Convert these constraints to .claude/rules/ for automatic enforcement?",
  options: [
    { label: "Yes, all of them", description: "All constraints become rules files" },
    { label: "Let me pick", description: "Choose which constraints to enforce" },
    { label: "Skip", description: "Keep constraints in requirements.md only" }
  ]
)

Record as decision: D_H3: "Rules: [list of constraints to convert]"

3-4. Skills Detection (auto-suggest + ask)

Scan L2 decisions for recurring task patterns and suggest project-specific skills:

L2 Decision Signal	Auto-Suggested Skill	Description
DB + ORM (Prisma, Drizzle, SQLAlchemy)	/migrate	Run migration + regenerate types
DB detected	/seed-data	Generate development seed data
Docker / docker-compose	/deploy	Build, push, run with health check
API server (REST, GraphQL, tRPC)	/api-test	Test endpoint with curl/httpie
Async workers (Celery, BullMQ)	/worker-test	Dispatch test task + verify result
CLI binary (Rust, Go)	/release	Version bump + build + tag + publish
Frontend framework	/new-component	Scaffold component + test + story
Payment integration (Stripe, etc.)	/test-webhook	Forward + trigger webhook locally

Present auto-suggestions, then ask:

AskUserQuestion(
  question: "These skills will be scaffolded based on your tech stack. Any other tasks you'll repeat frequently?",
  options: [
    { label: "These are enough", description: "Proceed with auto-suggested skills only" },
    { label: "Add more", description: "I'll describe additional recurring tasks" },
    { label: "Skip all skills", description: "Don't generate any project skills" }
  ]
)

Record as decision: D_H4: "Skills: [list of skills to generate]"

Each generated skill will have:

• SKILL.md with project-specific steps (using actual commands from L2 decisions)
• scripts/validate.sh when the task has a checkable outcome
• disable-model-invocation: true (all domain skills have side effects)

3-5. Hooks Detection (auto + confirm)

Auto-detect hooks from L2 tech stack decisions:

L2 Decision	Auto-Detected Hook	Type
TypeScript (tsconfig.json)	tsc --noEmit on Edit/Write to .ts	PostToolUse
Prettier configured	prettier --write on Edit/Write	PostToolUse
ESLint configured	eslint --fix on Edit/Write	PostToolUse
Ruff / Black (Python)	ruff format on Edit/Write	PostToolUse
rustfmt (Rust)	rustfmt on Edit/Write	PostToolUse
gofmt (Go)	gofmt -w on Edit/Write	PostToolUse
.env files will exist	Block Edit/Write to .env*	PreToolUse
Lock files will exist	Block Edit/Write to lock files	PreToolUse

Present the hook list:

AskUserQuestion(
  question: "These hooks will be added to .claude/settings.json for automatic enforcement. Approve?",
  options: [
    { label: "Approve all", description: "Add all detected hooks" },
    { label: "Let me pick", description: "Choose which hooks to enable" },
    { label: "Skip hooks", description: "Don't set up any hooks" }
  ]
)

Record as decision: D_H5: "Hooks: [list of hooks to configure]"

L3 Write to requirements.md

Use Edit tool to append all harness decisions to the Decisions section in ${SPEC_DIR}/requirements.md:

• D_H1: Domain context (if user provided)
• D_H2: Team conventions (if user provided)
• D_H3: Rules to convert from constraints
• D_H4: Skills to generate
• D_H5: Hooks to configure

Only include D_H1/D_H2 if user provided content. Omit if "None yet" or "Solo project".

L3 Gate

Present harness summary → AskUserQuestion (Approve/Revise/Abort).

---

L4: Requirements + Harness Decisions

Output: Requirements section (with GWT sub-requirements) written to requirements.md

L4 does NOT produce tasks. Task breakdown is the job of /execute (via /blueprint or inline planning), which writes a sibling plan.json. L4's job is to finalize the what (requirements + harness intent) so /execute has a complete requirements.md to derive tasks from.

Requirements come from both L2 (architecture) and L3 (harness). Every sub-requirement MUST have given / when / then (GWT is mandatory).

Step 1: Derive Requirements

Construct requirements from L2 decisions + L3 harness decisions, then write them into the Requirements section of ${SPEC_DIR}/requirements.md using the Edit tool. Every sub-requirement must include given, when, then — behavior alone is not enough.

Code Requirements (from L2):

R1: "Code Structure — Project directories, base configs, and a complete vertical slice exemplar"
  R1.1: "Directory structure follows [framework] conventions with clear layer separation"
  R1.2: "Vertical slice exemplar implements one complete flow (route → service → data → test) with importable utilities (logger, config, errors)"
  R1.3: "All exemplar utilities are importable modules, not inline code"

R2: "Test Infrastructure — Framework setup with patterns matching the exemplar"
  R2.1: "[Test framework] configured with [runner] and example test matching exemplar flow"
  R2.2: "Test directory structure mirrors source structure"

R3: "Guard Rails — CLAUDE.md + enforcement mechanisms for drift resistance"
  R3.1: "CLAUDE.md with architectural rules, domain context, team conventions, dependency direction, file placement conventions, available project skills summary, and active hooks summary"
  R3.2: "Linter + formatter configured with project-specific rules"
  R3.3: "CI pipeline running lint + typecheck + test"
  R3.4: ".env.example with all required environment variables documented"

Conditional Code Extensions (from L2):

R4: "Type Contracts — Schema-driven type safety across client-server boundary" (if D_EXT1)
  R4.1: "API schema or router defines all endpoints with typed request/response"
  R4.2: "Client-side type bindings generated or inferred from the schema (no manual type duplication)"

R5: "Data Layer — Database connection, schema management, and seed data" (if D_EXT2)
  R5.1: "ORM/query builder configured with typed models matching the domain"
  R5.2: "Initial migration generated and seed script produces development data"
  R5.3: "Database connection uses environment variable (DATABASE_URL or equivalent)"

R6: "Docker/Infra — Containerized local development environment" (if D_EXT3)
  R6.1: "docker-compose.yml with required services (DB, cache, etc.) and persistent volumes"
  R6.2: "README or CLAUDE.md documents how to start/stop the local environment"

R7: "Runtime Patterns — Production-readiness baseline for long-running server" (if D_EXT4)
  R7.1: "Health check endpoint returns server status and dependency connectivity"
  R7.2: "Graceful shutdown handler closes DB connections and in-flight requests"

Harness Requirements (from L3):

R8: "Project Rules — Constraints converted to .claude/rules/ for automatic enforcement" (if D_H3)
  R8.1: "Each selected constraint has a corresponding .claude/rules/{name}.md file"
  R8.2: "Rule files contain clear, actionable directives (not vague guidelines)"

R9: "Domain Skills — Project-specific repeatable task recipes" (if D_H4)
  R9.1: "Each skill has SKILL.md with project-specific commands (not generic placeholders)"
  R9.2: "Skills with checkable outcomes include scripts/validate.sh"

R10: "Project Hooks — Automated code quality enforcement" (if D_H5)
  R10.1: ".claude/settings.json with PostToolUse hooks for detected formatter/linter"
  R10.2: "PreToolUse protection hooks for .env and lock files (if applicable)"

Behavior Quality: Same rules as specify — trigger + observable outcome.

Note on fulfills[]: Use parent requirement IDs only (R1, R2, R3), NOT sub-requirement IDs (R1.1, R3.4).

Step 2: Harness Intent (no task generation)

Task derivation is handled by /execute (via /blueprint or inline planning). /execute reads requirements.md and writes plan.json next to it.

Ensure the requirements written in Step 1 carry enough harness detail that /execute can derive the right tasks:

• R1 (Code Structure) must include the vertical slice exemplar as a sub-requirement with full GWT.
• R3 (Guard Rails) CLAUDE.md sub-req must spell out domain context (D_H1), team conventions (D_H2), available skills (D_H4), and active hooks (D_H5).
• R8 / R9 / R10 (harness) must be present whenever D_H3 / D_H4 / D_H5 fired in L3.
• Conditional extensions R4-R7 must be present whenever the matching D_EXT fired in L2.

Task Shape Guidance for /execute

/execute will read requirements.md and derive plan.json tasks. The scaffold-specific shape it is expected to produce (documented here so reviewers know what "good" looks like):

• T1 Project initialization → fulfills R1
• T2 Guard Rails + CLAUDE.md + rules → fulfills R3 (+ R8 when present), depends on T1
• T4 Test infrastructure → fulfills R2, depends on T1
• T3 Vertical slice exemplar (highest-value task) → fulfills R1, depends on T2, T4
• Conditional extension tasks for any of R4/R5/R6/R7 that exist
• Harness tasks for R9 (skills) and R10 (hooks) when present
• A final verification task covering agent-extensibility + harness checks

Keep this as guidance; the actual task records are /execute's responsibility.

Vertical Slice Exemplar (R1 sub-requirement)

The exemplar is the scaffold's highest-value output. Its GWT sub-requirement must demand:

1. The complete flow — from entry point to data layer and back
2. Importable utilities — lib/logger.ts, lib/config.ts, lib/errors.ts (not inline)
3. The naming convention — how files, functions, and variables are named
4. The test pattern — how to test this flow (test lives alongside the exemplar)
5. Error handling — how errors propagate through layers
6. Type safety — how types flow across boundaries

The exemplar answers: "If an agent reads only this one feature, can it build the next feature correctly?"

Domain Skills (R9 sub-requirements)

Each generated skill must reference actual tools/commands from L2 decisions:

• disable-model-invocation: true for all domain skills (they have side effects)
• Include scripts/validate.sh when the task has a checkable outcome
• Use project-specific commands (e.g., "npx prisma migrate dev" not "run migration")

L4 Approval — Plan Summary

requirements.md ready! .hoyeon/specs/{name}/requirements.md

Goal
----------------------------------------
{confirmed_goal}

Architecture Decisions ({n} total)
----------------------------------------
D1: {tech stack decision}
D2: {communication decision}
...

Activated Extensions
----------------------------------------
[x] Type Contracts (OpenAPI + Orval)
[x] Data Layer (PostgreSQL + Prisma)
[ ] Docker/Infra (not needed)
[ ] Runtime Patterns (not needed)

Harness
----------------------------------------
CLAUDE.md: architecture + domain context + team conventions
Rules: {n} constraints → .claude/rules/
Skills: {list of skills}
Hooks: {list of hooks}

Task Derivation
----------------------------------------
(none in requirements.md — /execute will derive tasks into plan.json
 next to requirements.md: project init, guard rails + rules, test infra, vertical slice exemplar,
 conditional extensions, domain skills, project hooks, and a final scaffold verification.)