analytics-data-analysis

Name: analytics-data-analysis
Availability: InStock
Author: Rylaispirit

Build reproducible analytics scripts and notebooks for data ingestion, validation, transformation, and visualization.

55|41|Updated Aug 9, 2026

Search Tags:#data validation #data analysis #visualization #statistics #data cleaning #notebooks #reproducible

Ready to run

npx skills add https://github.com/Rylaispirit/rylai-codex-hermes-skills --skill analytics-data-analysis-rylaispirit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analytics-data-analysis
Source: https://github.com/Rylaispirit/rylai-codex-hermes-skills/tree/main/skills/analytics-data-analysis
Command: npx skills add https://github.com/Rylaispirit/rylai-codex-hermes-skills --skill analytics-data-analysis-rylaispirit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Data analysis often fails because of unvalidated inputs, hidden data loss, non-reproducible notebooks, and results that cannot be audited. This Skill enforces a disciplined workflow so every analysis can be rerun, verified, and defended. ## Core Features & Use Cases - Structured Analysis Workflow: Guides inventory, validation, transformation, analysis, visualization, packaging, and verification as explicit stages. - Data Quality Guardrails: Detects missingness, duplicates, encoding issues, and referential integrity problems before they corrupt results. - Reproducible Delivery: Produces scripts or notebooks with clean-run verification, documented assumptions, and reconciled totals. - Use Case: Given a raw CSV export of sales transactions, build a validated cleaning pipeline, compute distribution statistics, generate labeled charts, and deliver a notebook another analyst can rerun from scratch. ## Quick Start Use the analytics-data-analysis skill to build a reproducible analysis of my dataset, validating the inputs and verifying every output.

Frequently Asked Questions about analytics-data-analysis

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

FAQPage Schema

How do I make a data analysis reproducible?▼

Keep raw inputs unchanged, make cleaning steps explicit and deterministic, separate configuration from logic, and run the full analysis from a clean start. Record assumptions, methods, and any random seeds so another person can rerun and audit the result.

How to validate data before running analysis?▼

Check missingness, value ranges, uniqueness, referential integrity, and impossible values before transforming anything. Separate genuine source defects from intentional filtering, and quarantine records when a defect would invalidate the result.

What should a reproducible analysis notebook contain?▼

State the purpose and assumptions before the first analysis cell, then organize setup, loading, validation, transformation, analysis, and conclusions into visible sections. Cells must run top to bottom without relying on stale state.

Does this workflow require specific Python libraries?▼

No specific libraries are mandated. The workflow uses tools already available in the workspace and explains the smallest installation or fallback when a dependency is missing, keeping paths portable across environments.

When should statistical significance tests not be used alone?▼

Significance tests should be paired with effect size or practical magnitude, and the underlying assumptions must be tested with a fallback when they fail. Never infer causation from association alone.