82 lines
2.8 KiB
Markdown
82 lines
2.8 KiB
Markdown
---
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description: Execute the implementation planning workflow using the plan template to generate design artifacts.
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---
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## User Input
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```text
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$ARGUMENTS
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```
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You **MUST** consider the user input before proceeding (if not empty).
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## Outline
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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").
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2. **Load context**: Read FEATURE_SPEC and `.specify/memory/constitution.md`. Load IMPL_PLAN template (already copied).
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3. **Execute plan workflow**: Follow the structure in IMPL_PLAN template to:
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- Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
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- Fill Constitution Check section from constitution
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- Evaluate gates (ERROR if violations unjustified)
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- Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
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- Phase 1: Generate data-model.md, contracts/, quickstart.md
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- Phase 1: Update agent context by running the agent script
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- Re-evaluate Constitution Check post-design
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4. **Stop and report**: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.
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## Phases
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### Phase 0: Outline & Research
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1. **Extract unknowns from Technical Context** above:
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- For each NEEDS CLARIFICATION → research task
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- For each dependency → best practices task
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- For each integration → patterns task
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2. **Generate and dispatch research agents**:
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```text
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For each unknown in Technical Context:
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Task: "Research {unknown} for {feature context}"
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For each technology choice:
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Task: "Find best practices for {tech} in {domain}"
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```
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3. **Consolidate findings** in `research.md` using format:
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- Decision: [what was chosen]
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- Rationale: [why chosen]
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- Alternatives considered: [what else evaluated]
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**Output**: research.md with all NEEDS CLARIFICATION resolved
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### Phase 1: Design & Contracts
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**Prerequisites:** `research.md` complete
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1. **Extract entities from feature spec** → `data-model.md`:
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- Entity name, fields, relationships
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- Validation rules from requirements
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- State transitions if applicable
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2. **Generate API contracts** from functional requirements:
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- For each user action → endpoint
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- Use standard REST/GraphQL patterns
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- Output OpenAPI/GraphQL schema to `/contracts/`
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3. **Agent context update**:
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- Run `.specify/scripts/bash/update-agent-context.sh claude`
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- These scripts detect which AI agent is in use
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- Update the appropriate agent-specific context file
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- Add only new technology from current plan
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- Preserve manual additions between markers
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**Output**: data-model.md, /contracts/*, quickstart.md, agent-specific file
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## Key rules
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- Use absolute paths
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- ERROR on gate failures or unresolved clarifications
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