/dev-sciomc
Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.
$ npx -y skills add evolution-foundation/evo-nexus --skill dev-sciomc --agent claude-codeHow it fires
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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The summary Claude sees to decide when to auto-load this skill.
Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.
SKILL.md
dev-sciomc.SKILL.mdname: 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)
Read more
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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