/speckit.plan

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Outline

1. Setup: Run .specify/scripts/bash/setup-plan.sh --json from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot").

2. Load context: Read FEATURE_SPEC and .specify/memory/constitution.md. Load IMPL_PLAN template (already copied).

3. Execute plan workflow: Follow the structure in IMPL_PLAN template to:

   • Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
   • Fill Constitution Check section from constitution
   • Evaluate gates (ERROR if violations unjustified)
   • Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
   • Phase 1: Generate data-model.md, contracts/, quickstart.md
   • Phase 1: Update agent context by running the agent script
   • Re-evaluate Constitution Check post-design

4. Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.

Phases

Phase 0: Outline & Research

1. Extract unknowns from Technical Context above:

   • For each NEEDS CLARIFICATION → research task
   • For each dependency → best practices task
   • For each integration → patterns task

2. Generate and dispatch research agents:

   For each unknown in Technical Context:
     Task: "Research {unknown} for {feature context}"
   For each technology choice:
     Task: "Find best practices for {tech} in {domain}"
   

3. Consolidate findings in research.md using format:

   • Decision: [what was chosen]
   • Rationale: [why chosen]
   • Alternatives considered: [what else evaluated]

Output: research.md with all NEEDS CLARIFICATION resolved

Phase 1: Design & Contracts

Prerequisites: research.md complete

1. Extract entities from feature spec → data-model.md:

   • Entity name, fields, relationships
   • Validation rules from requirements
   • State transitions if applicable

2. Generate API contracts from functional requirements:

   • For each user action → endpoint
   • Use standard REST/GraphQL patterns
   • Output OpenAPI/GraphQL schema to /contracts/

3. Agent context update:

   • Run .specify/scripts/bash/update-agent-context.sh claude
   • These scripts detect which AI agent is in use
   • Update the appropriate agent-specific context file
   • Add only new technology from current plan
   • Preserve manual additions between markers

Output: data-model.md, /contracts/*, quickstart.md, agent-specific file

Key rules

• Use absolute paths
• ERROR on gate failures or unresolved clarifications