smith-plan

---
name: smith-plan
description: Execute the implementation planning workflow using the plan template to generate design artifacts.
handoffs:
  - label: Create Tasks
    agent: smith-tasks
    prompt: Break the plan into tasks
    send: true
  - label: Create Checklist
    agent: smith-checklist
    prompt: Create a checklist for the following domain...
---

> **Workflow requirement (Smith 20+):** This skill must be invoked from inside an active top-level workflow (`/smith-new`, `/smith-bugfix`, `/smith-debug`, `/smith-build`). The workflow-gate hook will block standalone invocation. To use this skill standalone, start a top-level workflow first.

## Vault Logging

Throughout this action, log significant events to the vault session log. Read the session log path from `.smith/vault/.current-session`. If the file is missing or the vault is not initialized, skip all logging silently.

**Timestamps are UTC.** `HH:MM:SS` comes from `date -u +%H:%M:%S` — never a local clock, never a time from memory. The append already runs through a shell, so read the clock in that same command (`TS=$(date -u +%H:%M:%S)`) and spend no extra tool call. Substitute the resulting value: an unexpanded `$(...)` reaching the log is a known regression and will not parse. Every hook-written stamp in this log is `date -u`, so a local-clock entry lands hours away from its neighbours and skews the workflow window `hooks/workflow_summary_lib.py` derives from them.

Append entries using this format:

```
### [HH:MM:SS] /smith-plan <event>

**User Request:**
> <verbatim user message that triggered this action>

**Synthesized Input:** <brief summary>
**Outcome:** <what happened>
**Artifacts:** <files created/modified>
**Systems affected:** <system IDs>
```

Log at these points:
1. **On invocation** — which spec is being planned (feature name and path)
2. **After architecture decisions** — brief summary of major design choices (tech stack, patterns, key tradeoffs)
3. **After plan creation** — path to plan.md and any supporting artifacts (research.md, data-model.md, contracts/)

## User Input

```text
$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**:

   ```text
   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