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petekppetekp/claude-code-setup/skills/spike/SKILL.md

spike

Skill48 starsChanged 51 days agoMITView on GitHub

Run a disposable experiment to resolve a specific feasibility question. Use for an explicit spike or when inspection cannot resolve an implementation-blocking uncertainty.

54 other projects use a version of this.

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Projects with it also use competitor-news-monitor, blocked-page-recovery and 10 more.

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The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 50 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep experiment code separate from the implementation.

    line 10

“Common” is measured: kept by at least 6 in 10 of the 50 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Spike

State the question, what result would change the decision, and a small experiment budget. Use inspection alone when it can answer the question. A request for a prototype or an estimate does not by itself authorize disposable code or a larger experiment.

Keep experiment code separate from the implementation. Use a temporary directory for self-contained checks. If real project imports are needed, use a task-owned, locally ignored scratch directory or an isolated worktree. Preserve unrelated work and the existing scope and approval boundaries.

Build only enough to measure the unknown. Do not mock the behavior being measured. Include realistic inputs and failure cases when they could change the answer; skip unrelated production polish. Do not use production credentials or mutate shared data without authorization.

Report the observed result, the evidence, its limits, and what it implies for the proposed implementation. A negative result is a valid outcome. Stop when the question is answered or the stated budget is exhausted; do not turn the experiment into an unrequested feature.

Record useful findings before removing task-created scratch code. Treat the implementation as separate work; do not silently promote experimental code. If the user asks to retain the prototype or measurement artifact, preserve it and clearly identify its limitations. Never remove someone else's files, branch, or worktree.

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