Validate the riskiest feature unknown with a throwaway implementation and SPIKE.md verdict.
npx skills add https://github.com/mujtaba3B/gstack-extensions --skill spike-mujtaba3b
Please help me install this Agent Skill. Skill: spike Source: https://github.com/mujtaba3B/gstack-extensions/tree/main/eng/skills/spike Command: npx skills add https://github.com/mujtaba3B/gstack-extensions --skill spike-mujtaba3b
This Skill helps you answer the single riskiest question in a feature before you commit time to planning, scaffolding, or production-quality implementation.
Use the spike skill to test the riskiest part of this feature with the smallest throwaway implementation and record the verdict in SPIKE.md.
High-intent search queries and answers about installing and using this skill.
A spike validates the riskiest unknown of a feature using the smallest throwaway implementation before committing to planning or production work. Use it to test uncertain integrations, API behaviors, or UI mechanics.
Define a one-line outcome, isolate your work in a dedicated spike branch or worktree, and record findings in a live SPIKE.md ledger. Complete the feasibility check by landing a final verdict before cleaning up the throwaway code.
When your feature spike gets blocked, you should escalate by seeking a second opinion and requesting user input. This prevents wasting time on repeated failed attempts during risk validation.
Yes, you can test mechanic-level experiments with the smallest throwaway implementation in an isolated spike branch or workwork. This avoids production setup overhead while confirming whether a specific capability is possible.
A spike focuses solely on validating the single riskiest unknown with minimal throwaway code, whereas a prototype typically explores broader user flows. Spikes require a recorded verdict in a ledger before cleanup, ensuring focused risk validation.
Recording a final verdict in SPIKE.md ensures the outcome of your risk validation is documented before cleanup. This structured ledger prevents lost findings and informs whether to proceed with full feature planning.
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