dev-sciomc

What problem does it solve?

Provides a rigorous scaffold for engineering investigations that replaces guesswork with falsifiable hypotheses, controlled experiments, and evidence-based conclusions so teams can make reproducible, statistically supported decisions.

Core Features & Use Cases

• Hypothesis framing: forces explicit, falsifiable claims and identifies dependent and independent variables.
• Experiment design: defines measurements, controls, sample size, and confounder mitigation for performance and A/B comparisons.
• Evidence collection & analysis: guides data gathering and delegates statistical testing to a specialist agent for effect size, confidence intervals, and p-values.
• Outputs a structured investigation report and follow-up experiments for engineering, optimization, and causal debugging workflows.

Quick Start

Perform a Sciomc investigation on performance regression in module X, state a falsifiable hypothesis, design controlled experiments, collect evidence, run statistical analysis, and draft a provisional conclusion.