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speckit-plan

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npx machina-cli add skill dceoy/speckit-agent-skills/speckit-plan --openclaw
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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

  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:

    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 <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.

Source

git clone https://github.com/dceoy/speckit-agent-skills/blob/main/skills/speckit-plan/SKILL.mdView on GitHub

Overview

Speckit Plan automates the implementation planning workflow using the plan template to produce design artifacts. It consumes a feature spec, constitution, and an IMPL_PLAN template to generate phase-by-phase outputs such as research, data-model, contracts, and quickstart, while updating agent context for traceability.

How This Skill Works

The skill loads FEATURE_SPEC and constitution, reads the IMPL_PLAN template, and fills sections like Technical Context and Constitution Check. It then executes Phase 0 research to resolve NEEDS CLARIFICATION and Phase 1 design to produce data-model.md, contracts, and quickstart, followed by agent-context updates and a final constitution re-check.

When to Use It

  • You have a ready feature spec and need a concrete technical implementation plan.
  • You must generate design artifacts (data-model.md, contracts, quickstart) from a spec.
  • You need to resolve unresolved requirements by researching unknowns in Phase 0.
  • You want API contracts (REST/GraphQL) generated from functional requirements.
  • You need to update the runtime agent context and produce a plan-linked set of outputs.

Quick Start

  1. Step 1: Run .specify/scripts/bash/setup-plan.sh --json from repo root to load context.
  2. Step 2: Load FEATURE_SPEC and .specify/memory/constitution.md and copy IMPL_PLAN template.
  3. Step 3: Run Phase 0 research, Phase 1 design, then Phase 2 planning to generate artifacts and report.

Best Practices

  • Ensure the feature spec exists at specs/<feature>/spec.md before running.
  • Use absolute paths (e.g., specs/<feature>/contracts/) for all artifacts.
  • Label all unresolved items as NEEDS CLARIFICATION and generate research tasks.
  • Follow Phase 0 research, Phase 1 design, then Phase 2 planning in order.
  • Run the agent-context update step to synchronize runtime agent files.

Example Use Cases

  • A new payments feature: converts a vague spec into data-model.md and API contracts.
  • Migrating a user-profile feature into a complete plan with research.md and quickstart.
  • Adding a service plan where unknowns trigger research tasks and best-practices tasks.
  • Generating OpenAPI schemas from functional requirements into /contracts/.
  • Updating the agent context after design to reflect new technology choices.

Frequently Asked Questions

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