Speckit plan

Skill dceoy/speckit-agent-skills/skills/speckit-plan

Agent skills for Spec Kit

Install

npx -y skills add dceoy/speckit-agent-skills --skill speckit-plan

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What its author says it does

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

SKILL.md

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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 --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").
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 → research task
- For each dependency → best practices task
- For each integration → patterns task

Generate and dispatch research agents:

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.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

Extract entities from feature spec → 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 → 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.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.