Agent Skills › evolution-foundation/evo-nexus › dev-sciomc

dev-sciomc

GitHub

应用科学方法规范工程调查，强制提出可证伪假设、设计实验并收集证据，适用于性能优化及高成本错误场景的严谨因果推理。

.claude/skills/dev-sciomc/SKILL.md evolution-foundation/evo-nexus

Trigger Scenarios

需要超越直觉猜测的严谨调查性能优化需控制变量和测量两种实现的A/B对比

Install

npx skills add evolution-foundation/evo-nexus --skill dev-sciomc -g -y

More Options

Non-standard path

npx skills add https://github.com/evolution-foundation/evo-nexus/tree/main/.claude/skills/dev-sciomc -g -y

Use without installing

npx skills use evolution-foundation/evo-nexus@dev-sciomc

指定 Agent (Claude Code)

npx skills add evolution-foundation/evo-nexus --skill dev-sciomc -a claude-code -g -y

安装 repo 全部 skill

npx skills add evolution-foundation/evo-nexus --all -g -y

预览 repo 内 skill

npx skills add evolution-foundation/evo-nexus --list

SKILL.md

Frontmatter

{
    "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}