survey-insight-extractor

Cluster open-text survey responses into themes, representative quotes, and outliers.

1|Updated Mar 16, 2026
One-click install
npx skills add https://github.com/00PrabalK00/claude-skills --skill survey-insight-extractor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: survey-insight-extractor
Source: https://github.com/00PrabalK00/claude-skills/tree/main/skills/survey-insight-extractor
Command: npx skills add https://github.com/00PrabalK00/claude-skills --skill survey-insight-extractor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Summarizes open-text survey responses into themes, representative quotes, and notable outliers, saving time and surfacing clear insights for action.

Core Features & Use Cases

  • Thematic clustering: groups related responses into coherent themes without losing important nuance.
  • Representative evidence: provides quotes and examples that illustrate each theme.
  • Outlier detection: highlights unusual or surprising responses for follow-up.

Quick Start

Provide the open-text survey responses as input to generate themes, quotes, and outliers ready for decision-making.

Frequently Asked Questions about survey-insight-extractor

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

FAQPage Schema
How do I summarize open-text survey responses into actionable themes?▼

Thematic clustering groups related open-text responses into coherent themes without losing important nuance. This qualitative text-analysis method ensures your survey summarization captures the full context of the feedback.

Can I extract representative quotes from large-scale survey datasets?▼

Yes, you can extract representative quotes from large-scale survey datasets. The system processes open-text responses across departments and products, providing specific evidence that illustrates each clustered theme.

Does this survey text-analysis tool detect outliers in qualitative feedback?▼

Yes, the survey text-analysis tool detects outliers by highlighting unusual or surprising responses. This outlier detection ensures atypical qualitative feedback is surfaced for follow-up review.

Are the survey summarization outputs structured and deterministic for human review?▼

Yes, the survey summarization outputs are fully deterministic and structured for human review. Guardrails are applied to avoid fabricating citations, ensuring the themes and quotes accurately reflect the input data.