Where does the skill store its analysis?

The skill generates a detailed perspective file in Markdown format within the 'idea/perspectives' directory of your workspace.

Which academic sources can this skill access?

It integrates with MCP servers to search and retrieve research papers from arXiv, Google Scholar, and bioRxiv.

How does it improve experimental reproducibility?

By providing frameworks for standardized documentation and data quality checks, ensuring that experiments are structured to be auditable and repeatable.

Who should use the Sibyl Empiricist skill?

It is designed for data scientists, academic researchers, and software engineers working on complex systems that require validation against scientific literature.

What is the primary purpose of Sibyl Empiricist?

It is a skill focused on applying scientific rigor, experimental design, and data quality standards to technical and AI-driven projects.

Sibyl Empiricist

Name: Sibyl Empiricist
Author: qcai0427

byqcai0427

0•

Ciencia de Datos y ML

Facilitates rigorous experimental design and ensures data reproducibility by integrating academic research tools into AI workflows.

Sibyl Empiricist is a specialized capability designed to bring scientific rigor and evidence-based methodology to Claude's development process. It focuses on experimental reproducibility, data quality, and structural integrity by leveraging integrated search tools for academic databases including arXiv, Google Scholar, and bioRxiv. This skill is ideal for researchers and engineers who need to ground their technical implementations in peer-reviewed literature and maintain high standards of validation for their data-driven experiments.

Características Principales

01Generates structured empirical perspective reports within the workspace

02Evaluates data quality and experimental design rigors automatically

03Ensures experimental reproducibility through standardized documentation patterns

04Automates the synthesis of scientific literature into actionable technical insights

050 GitHub stars

06Integrates academic search capabilities for arXiv, Google Scholar, and bioRxiv

Casos de Uso

01Designing and documenting reproducible software experiments and benchmarks

02Validating technical implementations against established academic frameworks and research

03Conducting literature reviews and data validation for machine learning projects

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add qcai0427/sibyl-codex sibyl-empiricist

For use in Claude.ai and ChatGPT

Características Principales

01Generates structured empirical perspective reports within the workspace

02Evaluates data quality and experimental design rigors automatically

03Ensures experimental reproducibility through standardized documentation patterns

04Automates the synthesis of scientific literature into actionable technical insights

050 GitHub stars

06Integrates academic search capabilities for arXiv, Google Scholar, and bioRxiv

Casos de Uso

01Designing and documenting reproducible software experiments and benchmarks

02Validating technical implementations against established academic frameworks and research

03Conducting literature reviews and data validation for machine learning projects

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add qcai0427/sibyl-codex sibyl-empiricist

For use in Claude.ai and ChatGPT