idea

Parse natural language ideas into structured issue specs with feasibility analysis.

13|6|Updated Apr 14, 2026
One-click install
npx skills add https://github.com/baekenough/second-brain --skill idea-baekenough
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: idea
Source: https://github.com/baekenough/second-brain/tree/main/.claude/skills/idea
Command: npx skills add https://github.com/baekenough/second-brain --skill idea-baekenough

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze natural language ideas against the current codebase and translate them into actionable issue specs.

Core Features & Use Cases

  • This skill maps a natural language idea to specific modules, files, and tasks, generating structured issue specs for adoption.

Quick Start

Provide a concise feasibility analysis of the given idea against the current codebase and return structured issue specs.

Frequently Asked Questions about idea

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

FAQPage Schema
How do I turn natural language ideas into structured issue specs for a codebase?

To turn natural language ideas into structured issue specs, this skill parses your input to extract core intent, analyzes the codebase for affected modules and dependencies, and returns a JSON block with title, scope, estimated issues, details, and acceptance criteria.

How do I analyze codebase feasibility before planning a new feature?

To analyze codebase feasibility, this skill maps your idea against current repository modules and files, assessing scope, complexity, and risks, then outputs a structured JSON feasibility analysis for planning.

Can I generate acceptance criteria automatically from a feature idea?

Yes, you can generate acceptance criteria automatically from a feature idea by parsing the natural language intent and returning structured issue specs that include specific acceptance criteria within the JSON output.

What is the best way to estimate issue scope for a codebase automation task?

The best way to estimate issue scope for a codebase automation task is to analyze affected modules and dependencies, which produces a JSON block containing estimated issues, complexity assessment, and detailed scope.

Does this issue spec generation work without external dependencies?

Yes, this issue spec generation works without external dependencies, relying solely on parsing natural language input and analyzing the current codebase to produce structured feasibility and issue specifications.