115 lines
6.0 KiB
Markdown
115 lines
6.0 KiB
Markdown
---
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description: Execute the implementation planning workflow using the plan template to generate design artifacts.
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handoffs:
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- label: Create Tasks
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agent: speckit.tasks
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prompt: Break the plan into tasks
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send: true
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- label: Create Checklist
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agent: speckit.checklist
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prompt: Create a checklist for the following domain...
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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 `.ai/ROOT.md` and `.ai/PROJECT_MAP.md` to understand the project structure and navigation. Then read required standards: `.ai/standards/constitution.md` and `.ai/standards/semantics.md`. Load IMPL_PLAN template.
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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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0. **Validate Design against UX Reference**:
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- Check if the proposed architecture supports the latency, interactivity, and flow defined in `ux_reference.md`.
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- **Linkage**: Ensure key UI states from `ux_reference.md` map to Component Contracts (`@UX_STATE`).
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- **CRITICAL**: If the technical plan compromises the UX (e.g. "We can't do real-time validation"), you **MUST STOP** and warn the user.
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1. **Extract entities from feature spec** → `data-model.md`:
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- Entity name, fields, relationships, validation rules.
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2. **Design & Verify Contracts (Semantic Protocol)**:
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- **Drafting**: Define semantic headers, metadata, and closing anchors for all new modules strictly from `.ai/standards/semantics.md`.
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- **Complexity Classification**: Classify each contract with `@COMPLEXITY: [1|2|3|4|5]` or `@C:`. Treat `@TIER` only as a legacy compatibility hint and never as the primary rule source.
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- **Adaptive Contract Requirements**:
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- **Complexity 1**: anchors only; `@PURPOSE` optional.
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- **Complexity 2**: require `@PURPOSE`.
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- **Complexity 3**: require `@PURPOSE` and `@RELATION`; UI also requires `@UX_STATE`.
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- **Complexity 4**: require `@PURPOSE`, `@RELATION`, `@PRE`, `@POST`, `@SIDE_EFFECT`; Python modules must define a meaningful `logger.reason()` / `logger.reflect()` path or equivalent belief-state mechanism.
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- **Complexity 5**: require full level-4 contract plus `@DATA_CONTRACT` and `@INVARIANT`; Python modules must require `belief_scope`; UI modules must define UX contracts including `@UX_STATE`, `@UX_FEEDBACK`, `@UX_RECOVERY`, and `@UX_REACTIVITY`.
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- **Relation Syntax**: Write dependency edges in canonical GraphRAG form: `@RELATION: [PREDICATE] ->[TARGET_ID]`.
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- **Context Guard**: If a target relation, DTO, or required dependency cannot be named confidently, stop generation and emit `[NEED_CONTEXT: target]` instead of inventing placeholders.
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- **Testing Contracts**: Add `@TEST_CONTRACT`, `@TEST_SCENARIO`, `@TEST_FIXTURE`, `@TEST_EDGE`, and `@TEST_INVARIANT` when the design introduces audit-critical or explicitly test-governed contracts, especially for Complexity 5 boundaries.
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- **Self-Review**:
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- *Complexity Fit*: Does each contract include exactly the metadata and contract density required by its complexity level?
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- *Completeness*: Do `@PRE`/`@POST`, `@SIDE_EFFECT`, `@DATA_CONTRACT`, and UX tags cover the edge cases identified in Research and UX Reference?
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- *Connectivity*: Do `@RELATION` tags form a coherent graph using canonical `@RELATION: [PREDICATE] ->[TARGET_ID]` syntax?
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- *Compliance*: Are all anchors properly opened and closed, and does the chosen comment syntax match the target medium?
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- *Belief-State Requirements*: Do Complexity 4/5 Python modules explicitly account for `logger.reason()`, `logger.reflect()`, and `belief_scope` requirements?
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- **Output**: Write verified contracts to `contracts/modules.md`.
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3. **Simulate Contract Usage**:
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- Trace one key user scenario through the defined contracts to ensure data flow continuity.
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- If a contract interface mismatch is found, fix it immediately.
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4. **Generate API contracts**:
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- Output OpenAPI/GraphQL schema to `/contracts/` for backend-frontend sync.
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5. **Agent context update**:
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- Run `.specify/scripts/bash/update-agent-context.sh kilocode`
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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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