How does Spec help with building AI features?

It provides a dedicated framework for AI products, covering context data requirements, behavior examples (good/bad/reject), model cost projections, and human-in-the-loop control handoffs.

Does this skill follow the Linear Method?

Yes, Spec is grounded in Linear Method principles: prioritizing issues over user stories, focusing on brevity, and emphasizing momentum over exhaustive documentation.

What is the progressive disclosure approach in Spec?

Progressive disclosure means the skill only asks for the depth of information required for the specific task. Simple tasks remain brief issues, while complex AI features trigger deep-dive requirements for context and evals.

Can I use Spec to create tasks directly in my project management tool?

While Spec generates the content, if you have the Linear MCP enabled, it can offer to create those issues and projects directly within your Linear workspace.

Spec: AI Product Thought Partner

Name: Spec: AI Product Thought Partner
Author: breethomas

bybreethomas

•

生産性とワークフロー

Streamlines product specification through progressive disclosure, from simple Linear issues to complex AI feature requirements.

概要

Spec is a specialized Claude Code Skill designed to help product managers and developers write the right level of documentation for any project. Grounded in the Linear Method philosophy of brevity and momentum, it guides users through three distinct depths: quick tasks, lite PRDs for features, and specialized AI feature specs. By focusing on 'writing what is needed and skipping what is not,' it ensures that critical AI-specific details—like context requirements, behavior examples, and eval strategies—are never overlooked, while preventing unnecessary bloat for simpler tasks.

主な機能

Context requirement mapping to ensure high-quality AI outputs
Agency progression planning for evolving autonomous features
Progressive disclosure routing for task-appropriate documentation depth
10 GitHub stars
Linear Method integration for high-momentum issue creation
Specialized AI product framework including eval strategies and cost modeling

ユースケース

Generating clear, actionable Linear issues from brief task descriptions
Defining robust behavior examples and failure modes for new AI-driven features
Drafting lite PRDs that prioritize user problems and success metrics over bloat

概要

主な機能

Context requirement mapping to ensure high-quality AI outputs
Agency progression planning for evolving autonomous features
Progressive disclosure routing for task-appropriate documentation depth
10 GitHub stars
Linear Method integration for high-momentum issue creation
Specialized AI product framework including eval strategies and cost modeling

ユースケース

Generating clear, actionable Linear issues from brief task descriptions
Defining robust behavior examples and failure modes for new AI-driven features
Drafting lite PRDs that prioritize user problems and success metrics over bloat