qualitative-analysis

Name: qualitative-analysis
Availability: InStock
Author: franciscomelloc

Extract themes from open-ended text with traceable quotes and auditable steps.

7|2|Updated Apr 11, 2026

Search Tags:#prompting #equity #policy-research #sentiment-analysis #qualitative-analysis #theme-extraction #text-coding

Ready to run

npx skills add https://github.com/franciscomelloc/ai-for-social-impact --skill qualitative-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: qualitative-analysis
Source: https://github.com/franciscomelloc/ai-for-social-impact/tree/main/skills/qualitative-analysis
Command: npx skills add https://github.com/franciscomelloc/ai-for-social-impact --skill qualitative-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers and policy teams often spend excessive time coding open-ended texts without a reproducible, equity-focused framework that guarantees traceable quotes and prevents demographic inferences.

Core Features & Use Cases

End-to-end qualitative analysis of surveys, interviews, and free-form notes that distinguishes manifest vs latent themes with auditable quotes.
Association of exact quotes with themes using IDs to ensure traceability.
Optional sentiment analysis and stratification by available demographics to reveal representational gaps.
Human-in-the-loop workflow with explicit checkpoints and frontmatter-driven configuration for repeatable results.

Quick Start

Provide a corpus and metadata; the system will extract themes, apply equity checks, and output auditable results.

Frequently Asked Questions about qualitative-analysis

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

FAQPage Schema

How do I extract themes from open-ended survey responses with traceable quotes?▼

Qualitative analysis of open-ended survey responses identifies manifest and latent themes while assigning traceable quote IDs. It outputs a theme catalog and per-unit taggings, ensuring reproducible qualitative coding with auditable verification steps.

What is the best way to perform equity-aware qualitative text analysis for policy research?▼

Equity-aware qualitative text analysis for policy research stratifies themes by available demographics to reveal representational gaps. It processes interviews and free-form notes, applying equity checks to prevent unsupported demographic inferences during theme extraction.

Can I stratify interview sentiment analysis by demographic groups?▼

Interview sentiment analysis can be stratified by available demographic groups to highlight representational gaps. This group stratification reveals equity disparities in public perception studies, supported by auditable quotes linked to specific theme IDs.

Do I need YAML configuration to run qualitative coding on interview transcripts?▼

YAML frontmatter configuration is required to establish repeatable parameters for qualitative coding on interview transcripts. This human-in-the-loop setup drives the extraction process and generates a governance-ready human-validation checklist for review.

How do I distinguish manifest versus latent themes when coding free-form notes?▼

Theme extraction distinguishes manifest versus latent themes when coding free-form notes by associating exact quotes with themes using IDs. This ensures traceable, auditable steps that output a theme catalog and per-unit taggings for human validation.

What limitations exist when using automated theme extraction for public perception studies?▼

Automated theme extraction for public perception studies requires a human-in-the-loop workflow with explicit checkpoints to validate results. It outputs a governance-ready human-validation checklist because automated stratification cannot independently make demographic inferences.