spike

Create disposable Python experiments with Markdown documentation to validate ideas.

Updated Jun 4, 2026

Search Tags:#feasibility #prototype #experiment #spike #throwaway

Ready to run

npx skills add https://github.com/kngender5/hermes --skill spike-kngender5

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spike
Source: https://github.com/kngender5/hermes/tree/main/skills/software-development/spike
Command: npx skills add https://github.com/kngender5/hermes --skill spike-kngender5

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps users validate ideas by creating disposable experiments, allowing them to test feasibility and compare approaches before committing to a full build.

Core Features & Use Cases

Quick Prototyping: Validate feasibility and compare approaches for new ideas.
Disposable Experiments: Create throwaway experiments to explore ideas without commitment.
Use Case: When you want to test if a new feature can be implemented or compare different approaches to solving a problem.

Quick Start

Create a spike to test if a new feature for a web application can be implemented with minimal effort.

Frequently Asked Questions about spike

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

FAQPage Schema

What is a spike in software engineering?▼

A spike is a disposable experiment used to validate the feasibility of a new idea or compare different approaches before committing to a full build.

How do I test if a new feature is feasible before building it?▼

You can test feature feasibility by creating a quick prototype or throwaway experiment to explore the implementation approach with minimal effort and without full commitment.

Do I need Python to run disposable experiments for feasibility testing?▼

Yes, you need Python installed for scripting the disposable experiments, along with Markdown for documenting the exploratory research and feasibility testing results.

When should I use throwaway prototypes instead of full development?▼

Use throwaway prototypes when you need to quickly compare approaches to solving a problem or test if a new feature can be implemented, avoiding premature commitment to a full build.

What is the best way to compare approaches for a new software feature?▼

The best way to compare approaches is by running quick, disposable experiments that validate each option's feasibility, allowing you to evaluate different solutions before starting the actual implementation.