skills.rest
j-walheim
j-walheimCOMMUNITY
Explore all skills by this author →
GitHub

bias-detection

Detect methodological bias patterns in scientific hypotheses with severity assessments.

Updated Aug 23, 2026
Search Tags:#bias detection#study design#confounding#methodological bias#clinical research#hypothesis evaluation
One-click install
npx skills add https://github.com/j-walheim/Critical-AI-Scientist --skill bias-detection-j-walheim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bias-detection
Source: https://github.com/j-walheim/Critical-AI-Scientist/tree/main/agent_definition/.claude/skills/bias-detection
Command: npx skills add https://github.com/j-walheim/Critical-AI-Scientist --skill bias-detection-j-walheim

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill systematically identifies and assesses methodological biases in scientific hypotheses, acting as a crucial "code smell" detector for study designs to ensure robust and reliable conclusions.

Core Features & Use Cases

  • Comprehensive Bias Taxonomy: Covers a wide range of biases including time-zero, censoring, selection, confounding, and information biases.
  • Type-Specific Emphasis: Tailors bias assessment based on the specific type of study (e.g., RCTs, observational studies).
  • Baseline Characteristics Analysis: Mandates detailed examination of baseline data to check for imbalances and assess confounding.
  • Use Case: When reviewing a new clinical trial hypothesis, this Skill will automatically scan for potential biases like immortal time bias or confounding by indication, providing a structured assessment of their applicability, severity, and potential mitigation strategies.

Quick Start

Use the bias-detection skill to assess the hypothesis in ./parsed_hypothesis.json and write the findings to ./bias_assessment.json.

Frequently Asked Questions about bias-detection

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

FAQPage Schema
How do I detect methodological bias in a clinical research study design?▼

To detect methodological bias in clinical research, systematically scan hypotheses for patterns across categories like time-zero, censoring, selection, confounding, and information biases. This process assesses applicability and severity to ensure reliable conclusions.

What is the best way to check baseline characteristics for confounding in observational studies?▼

Checking baseline characteristics for confounding in observational studies requires examining data for imbalances and assessing adjustment adequacy. This includes evaluating confounding risk and calculating E-values to quantify robustness.

How does an E-value calculation help evaluate confounding risk in a study hypothesis?▼

An E-value calculation helps evaluate confounding risk by quantifying the minimum strength of association an unmeasured confounder must have to explain away observed effects. This metric assesses adjustment adequacy and vulnerability to bias.

Can I assess immortal time bias and censoring bias in both RCTs and observational studies?▼

Yes, you can assess immortal time bias and censoring bias in both RCTs and observational studies. The assessment systematically tailors its bias taxonomy checks based on the specific study type provided in the hypothesis.

What types of information bias are identified during a hypothesis evaluation?▼

During a hypothesis evaluation, information bias is identified alongside time-zero, selection, and confounding biases. The assessment outputs detailed severity calibrations and recommendations for design and analysis mitigation strategies.

Related Skills

Explore More Education & Research Skills→
zivtechzivtech

study-design-planner

Operationalize research questions into PICO frameworks and causal DAGs.

Official
Advanced
lluppesmslluppesms

ai-prompt-engineering-safety-review

Analyze AI prompts for safety, bias, security, and effectiveness.

Community
Advanced
NeuroAIHubNeuroAIHub
269

clinical-trials-database

Query the ClinicalTrials.gov API v2 for structured trial data by condition and intervention.

Official
Intermediate
xjtulycxjtulyc
33

causal-inference

Estimate causal treatment effects from observational datasets using regression, propensity score weighting, and doubly robust methods.

Community
Advanced