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/dev-sciomc

Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.

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evo-nexus
520193 skills38 agents40 commands9 MCP
Install
$ npx -y skills add evolution-foundation/evo-nexus --skill dev-sciomc --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/dev-sciomc

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Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.

SKILL.md

dev-sciomc.SKILL.md
name: dev-sciomc
description: Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.

Dev Sciomc (Scientific Method)

Derived from oh-my-claudecode (MIT, Yeachan Heo). Adapted for the EvoNexus Engineering Layer.

Scientific method discipline applied to engineering investigations. Forces explicit hypothesis statement, experimental design, evidence collection, and provisional conclusions.

Use When

  • Investigation requires rigor beyond "let me try X"
  • Performance optimization (you need controls and measurements, not guesses)
  • A/B comparison of two implementations
  • Anything where the cost of being wrong is high

Do Not Use When

  • Trivial bug → use `@hawk-debugger`
  • Pure exploration → use `@scout-explorer`

Workflow

Phase 1 — Hypothesis

  • State the hypothesis as a falsifiable claim
  • "X is faster than Y" not "X feels faster"
  • Identify the dependent variable, independent variables, controls

Phase 2 — Experiment Design

  • What measurement will prove/disprove the hypothesis?
  • What's the minimum sample size for statistical significance?
  • What confounders need to be controlled?

Phase 3 — Evidence Collection

  • Run the experiment
  • Collect raw data
  • Note environmental factors that could affect results

Phase 4 — Analysis

  • Apply statistical tests (delegate to `@prism-scientist`)
  • Calculate effect size, CI, p-value
  • Compare against the hypothesis

Phase 5 — Conclusion

  • Provisional, never absolute
  • State limitations
  • Identify follow-up experiments

Output

Saved to `workspace/development/research/[C]sciomc-{topic}-{date}.md`:

## Scientific Investigation — {topic}

### Hypothesis
{Falsifiable claim}

### Experimental Design
- Dependent variable: {what we measure}
- Independent variables: {what we vary}
- Controls: {what we hold constant}
- Sample size: {N}

### Method
{Step-by-step protocol}

### Results
{Raw data summary}

### Statistical Analysis
[delegated to @prism-scientist]

### Conclusion
{Provisional conclusion + limitations}

### Follow-ups
- {next experiment}

Pairs With

  • `@prism-scientist` (for statistical analysis)
  • `@trail-tracer` (when investigation is causal)
  • `@apex-architect` (when conclusion implies architecture change)
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