Spark

Generate structured markdown feature proposals from product data and signals.

68|14|Updated Jan 7, 2026
One-click install
npx skills add https://github.com/simota/agent-skills --skill spark
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Spark
Source: https://github.com/simota/agent-skills/tree/main/spark
Command: npx skills add https://github.com/simota/agent-skills --skill spark

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you generate innovative feature proposals by leveraging existing data, logic, and product signals, ensuring new features align with business goals and user needs.

Core Features & Use Cases

  • Feature Ideation: Generates new feature ideas based on product data and user feedback.
  • Proposal Writing: Creates structured markdown specifications for proposed features.
  • Prioritization: Assesses features using frameworks like RICE and Impact-Effort.
  • Use Case: You have usage metrics showing a drop-off in a specific user flow. Use Spark to analyze this data and propose a new feature that addresses the friction point, complete with a business rationale and measurable hypothesis.

Quick Start

Use the spark skill to propose a new feature based on recent user feedback.

Frequently Asked Questions about Spark

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

FAQPage Schema
How do I generate new feature ideas from existing product data and user feedback?

Feature ideation analyzes existing product signals and user feedback to propose new features. It leverages current data to ensure new capabilities align with business goals and address specific user needs.

How do I write a structured product proposal for a new feature?

Writing a product proposal generates structured markdown specifications for proposed features. It creates complete documentation including business rationale, measurable hypotheses, and user personas.

What is the best way to prioritize features using RICE and MoSCoW frameworks?

Feature prioritization assesses proposed capabilities using frameworks like RICE and MoSCoW. This evaluates impact versus effort to ensure resources target high-value features aligned with product strategy.

Can I use JTBD analysis to propose features targeting specific user personas?

JTBD analysis supports proposing features by targeting specific user personas and their jobs to be done. It connects user needs with product strategy to generate relevant feature proposals.

How do I address user flow drop-off by proposing features based on usage metrics?

Analyzing usage metrics identifies friction points in user flows to propose targeted features. This process generates a new feature with a business rationale and measurable hypothesis to resolve the drop-off.

What format do feature proposals output when generating ideas from product signals?

Feature proposals output structured markdown specifications when generating ideas from product signals. This format ensures proposals include clear logic, business rationale, and measurable hypotheses.