Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
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
-
Setup: Run
.specify/scripts/bash/setup-plan.sh --jsonfrom 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"). -
Load context: Read FEATURE_SPEC and
.specify/memory/constitution.md. Load IMPL_PLAN template (already copied). -
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
-
Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.
Phases
Phase 0: Outline & Research
- Extract unknowns from Technical Context above:
- For each NEEDS CLARIFICATION β research task
- For each dependency β best practices task
-
For each integration β patterns task
-
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}"
- Consolidate findings in
research.mdusing 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
- Extract entities from feature spec β
data-model.md: - Entity name, fields, relationships
- Validation rules from requirements
-
State transitions if applicable
-
Generate API contracts from functional requirements:
- For each user action β endpoint
- Use standard REST/GraphQL patterns
-
Output OpenAPI/GraphQL schema to
/contracts/ -
Agent context update:
- Run
.specify/scripts/bash/update-agent-context.sh <agent_type> - Use the current runtime agent type (e.g., claude, codex, copilot, gemini). Leave empty to update all existing agent files.
- 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
Outputs
specs/<feature>/plan.md(filled implementation plan)specs/<feature>/research.mdspecs/<feature>/data-model.mdspecs/<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.