Agent skill
dev-sciomc
Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.
Install this agent skill to your Project
npx add-skill https://github.com/EvolutionAPI/evo-nexus/tree/main/.claude/skills/dev-sciomc
SKILL.md
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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