dceoy

speckit-plan

9
0
# Install this skill:
npx skills add dceoy/speckit-agent-skills --skill "speckit-plan"

Install specific skill from multi-skill repository

# Description

Execute the implementation planning workflow using the plan template to generate design artifacts.

# SKILL.md


name: speckit-plan
description: Execute the implementation planning workflow using the plan template to generate design artifacts.


Spec Kit Plan Skill

When to Use

  • The feature spec is ready and you need a technical implementation plan.

Inputs

  • specs/<feature>/spec.md
  • Repo context and .specify/ templates
  • User-provided constraints or tech preferences (if any)

If the spec is missing, ask the user to run speckit-specify first.

Workflow

  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:

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

  11. 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:
  2. For each NEEDS CLARIFICATION β†’ research task
  3. For each dependency β†’ best practices task
  4. For each integration β†’ patterns task

  5. 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}"

  1. Consolidate findings in research.md using format:
  2. Decision: [what was chosen]
  3. Rationale: [why chosen]
  4. 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 β†’ data-model.md:
  2. Entity name, fields, relationships
  3. Validation rules from requirements
  4. State transitions if applicable

  5. Generate API contracts from functional requirements:

  6. For each user action β†’ endpoint
  7. Use standard REST/GraphQL patterns
  8. Output OpenAPI/GraphQL schema to /contracts/

  9. Agent context update:

  10. Run .specify/scripts/bash/update-agent-context.sh <agent_type>
  11. Use the current runtime agent type (e.g., claude, codex, copilot, gemini). Leave empty to update all existing agent files.
  12. Update the appropriate agent-specific context file
  13. Add only new technology from current plan
  14. 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

Outputs

  • specs/<feature>/plan.md (filled implementation plan)
  • specs/<feature>/research.md
  • specs/<feature>/data-model.md
  • specs/<feature>/contracts/ (API schemas)
  • specs/<feature>/quickstart.md
  • Updated agent context file (runtime-specific)

Next Steps

After planning:

  • Generate tasks with speckit-tasks.
  • Create a checklist with speckit-checklist when a quality gate is needed.

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.