dev-sciomc

Structure falsifiable engineering hypotheses with experiment design and evidence collection.

520|175|Updated Apr 8, 2026

Search Tags:#experiment design #performance optimization #hypothesis testing #statistical analysis #scientific method #causal reasoning #engineering investigation

Ready to run

npx skills add https://github.com/evolution-foundation/evo-nexus --skill dev-sciomc-evolution-foundation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dev-sciomc
Source: https://github.com/evolution-foundation/evo-nexus/tree/main/.claude/skills/dev-sciomc
Command: npx skills add https://github.com/evolution-foundation/evo-nexus --skill dev-sciomc-evolution-foundation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of making engineering decisions based on unclear assumptions, uncontrolled changes, or “vibes-based” debugging by forcing an explicit, falsifiable investigation plan.

Core Features & Use Cases

Hypothesis-driven debugging: Turns your suspicion into a falsifiable claim with defined variables and controls.
Rigor-first experiment design: Specifies measurements, sample size expectations, and confounders to reduce false conclusions.
Evidence-to-conclusion workflow: Collects raw evidence, delegates statistical analysis, and produces a provisional conclusion with limitations.
Use case: You suspect an optimization in a request pipeline improves latency; use this skill to compare implementations with clear metrics, controls, and statistically grounded results.

Quick Start

Use dev-sciomc to investigate why a change affected system latency by stating your falsifiable hypothesis, designing the measurement experiment, collecting the raw data, and concluding with limitations.

Frequently Asked Questions about dev-sciomc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design an experiment to test an engineering hypothesis?▼

To design an experiment for an engineering hypothesis, you structure falsifiable claims, define variables, specify measurements, and control confounders to reduce false conclusions. This approach ensures rigor-first investigation for measurable outcomes.

What's the best way to run A/B comparisons for performance optimization?▼

Running A/B comparisons for performance optimization requires defining clear metrics, establishing controls, and collecting raw evidence to compare implementations. This skill structures the investigation to produce statistically grounded results.

Can I use causal reasoning to fix vibes-based debugging?▼

Yes, causal reasoning fixes vibes-based debugging by turning suspicions into falsifiable claims with defined variables and controls. It replaces uncontrolled changes with an explicit, rigorous investigation plan.

Does this skill perform statistical analysis for experiment results?▼

No, this skill does not perform statistical analysis itself. It structures the evidence collection workflow and delegates the statistical analysis to a dedicated statistics specialist to ensure accurate conclusions.

How do I structure hypothesis-driven debugging for system latency?▼

Hypothesis-driven debugging for system latency involves stating a falsifiable hypothesis, designing a measurement experiment, collecting raw data, and concluding with limitations. It applies causal reasoning to pinpoint why changes affect system performance.

What are the limitations of using scientific investigations for causal debugging?▼

Scientific investigations for causal debugging produce provisional conclusions with explicitly stated limitations. They require predefined sample size expectations and strict confounder controls to avoid false conclusions from uncontrolled variables.