sdd-plan

Refine draft task specifications into implementation-ready tasks via multi-agent workflow.

Updated Mar 10, 2026
One-click install
npx skills add https://github.com/Gamezar/opencode-cek --skill sdd-plan-gamezar
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sdd-plan
Source: https://github.com/Gamezar/opencode-cek/tree/main/plugins/sdd/skills/sdd-plan
Command: npx skills add https://github.com/Gamezar/opencode-cek --skill sdd-plan-gamezar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of refining draft task specifications into actionable, well-planned implementation steps, ensuring quality and efficiency through a multi-agent workflow.

Core Features & Use Cases

  • Task Refinement: Transforms vague task descriptions into detailed, actionable plans.
  • Parallel Analysis: Conducts research, codebase analysis, and business analysis concurrently.
  • Quality Gates: Integrates LLM-as-Judge validation at each stage to ensure high standards.
  • Use Case: A product manager drafts a new feature. This Skill takes that draft, analyzes its technical feasibility, synthesizes an architecture, breaks it into parallelizable steps, and verifies each phase, producing a ready-to-implement task.

Quick Start

Refine the draft task specification located at '.specs/tasks/draft/new-feature.feature.md' using the sdd-plan skill.

Frequently Asked Questions about sdd-plan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I refine draft task specifications into implementation-ready tasks?

To refine draft task specifications, you can use a multi-agent workflow that conducts parallel analysis, synthesizes architecture, breaks down tasks, and verifies each phase with LLM-as-Judge quality gates to produce actionable plans.

What is the best way to parallelize software development tasks from a vague feature draft?

The best way to parallelize tasks from a vague draft is by running concurrent research, codebase analysis, and business analysis, then synthesizing the results into an architecture that can be decomposed into parallelizable implementation steps.

How does an LLM-as-Judge quality gate work in task decomposition workflows?

An LLM-as-Judge quality gate validates output at each stage of the task decomposition workflow, ensuring draft specifications meet high technical standards before being parallelized and verified as implementation-ready steps.

Can I use human-in-the-loop checkpoints during task specification refinement?

Yes, you can use human-in-the-loop checkpoints during task specification refinement to manually verify phases, and the workflow supports incremental refinement via git diff to track specification changes.

Does multi-agent workflow automation support incremental refinement using git diff?

Yes, multi-agent workflow automation supports incremental refinement using git diff, allowing you to track and process only the modified parts of draft task specifications through the verification phases.

When do I need specification decomposition for software engineering projects?

You need specification decomposition when transforming vague product feature drafts into detailed, actionable, and parallelizable implementation steps that require integrated research, codebase analysis, and business analysis.