evidence-gated-investigation

Name: evidence-gated-investigation
Availability: InStock
Author: koki3141

Structure claims, generate hypotheses, and collect evidence for root cause analysis.

2|Updated Jul 9, 2026

Search Tags:#debugging #investigation #root cause analysis #data verification #evidence-based

Ready to run

npx skills add https://github.com/koki3141/codex-global-skills --skill evidence-gated-investigation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evidence-gated-investigation
Source: https://github.com/koki3141/codex-global-skills/tree/main/evidence-gated-investigation
Command: npx skills add https://github.com/koki3141/codex-global-skills --skill evidence-gated-investigation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the problem of drawing premature conclusions from incomplete evidence, ensuring a thorough and evidence-based approach to investigations.

Core Features & Use Cases

Evidence-Gated Analysis: Provides a structured process for defining claims, generating hypotheses, and collecting evidence.
Alternative Hypotheses: Encourages considering multiple plausible explanations before drawing conclusions.
Claim Classification: Categorizes tasks into specific claim types for accurate analysis.
Data Verification: Assists in verifying the integrity and completeness of data and documents.
Use Case: Ideal for debugging, provenance tracing, and root cause analysis in software development, data science, and cybersecurity.

Quick Start

Activate the evidence-gated-investigation skill and provide the claim you want to investigate.

Frequently Asked Questions about evidence-gated-investigation

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

FAQPage Schema

How do I structure a root cause analysis to avoid premature conclusions?▼

Root cause analysis requires structuring claims, generating alternative hypotheses, and collecting evidence before drawing conclusions. This evidence-gated approach categorizes claim types and verifies data integrity to prevent incomplete debugging outcomes.

What is the best way to verify data integrity during a debugging investigation?▼

Data verification during a debugging investigation involves using Python scripts with pandas and numpy to validate the completeness and integrity of datasets. This ensures hypotheses are tested against verified evidence rather than assumptions.

Do I need Python and pandas to perform evidence-based root cause analysis?▼

Yes, evidence-based root cause analysis requires Python scripts for data analysis and verification, specifically depending on pandas, numpy, and scipy to process data and validate hypotheses structured during the investigation.

How does generating alternative hypotheses improve root cause analysis?▼

Generating alternative hypotheses improves root cause analysis by forcing the consideration of multiple plausible explanations before concluding. This evidence-gated method prevents confirmation bias and ensures thorough investigation of debugging scenarios.

Can I use this evidence-gated investigation approach for cybersecurity provenance tracing?▼

Yes, evidence-gated investigation supports cybersecurity provenance tracing and data science debugging by structuring claims, classifying task types, and verifying document and data integrity to systematically trace root causes.