How does this skill help reduce survey bias?

It uses a structured framework to avoid double-barreled questions, randomize answers, and implement rigorous sampling rules to ensure data integrity.

Is this skill part of a larger suite?

Yes, it is part of the customer-feedback-orchestration plugin within the GTM Agents repository, designed for sales, marketing, and success workflows.

What templates are included in this skill?

The skill includes templates for survey briefs, categorized question banks (satisfaction, pricing, roadmap), and QA scripts for final reviews.

Can I use this for complex branching logic?

Yes, the skill provides detailed blueprints for conditional flows, progress indicators, and completion time targets to ensure a smooth user experience.

Customer Survey Designer

Name: Customer Survey Designer
Author: gtmagents

bygtmagents

•

Marketing Automation

Designs high-quality, bias-free survey instruments with complex logic and rigorous QA standards for GTM teams.

About

The survey-design skill provides a comprehensive framework for creating professional customer feedback instruments, ensuring data integrity through structured sampling, logical branching, and rigorous quality assurance. It helps Go-To-Market (GTM) teams avoid common pitfalls like response bias or poor completion rates by offering pre-built templates for survey briefs and categorized question banks, making it an essential tool for launching new feedback programs or refining existing ones within the Claude Code environment.

Key Features

29 GitHub stars
Structured question architecture for quantitative and qualitative data collection
Comprehensive sampling and audience persona definition frameworks
Categorized question banks for adoption, satisfaction, and roadmap feedback
Advanced logic and branching workflows to optimize respondent paths
Integrated QA checklists for device compatibility and data validation

Use Cases

Diagnosing and fixing low response rates or data bias in existing feedback loops
Training marketing and customer success teams on survey design best practices
Launching product satisfaction (CSAT) or NPS surveys with high response quality

About

Key Features

29 GitHub stars
Structured question architecture for quantitative and qualitative data collection
Comprehensive sampling and audience persona definition frameworks
Categorized question banks for adoption, satisfaction, and roadmap feedback
Advanced logic and branching workflows to optimize respondent paths
Integrated QA checklists for device compatibility and data validation

Use Cases

Diagnosing and fixing low response rates or data bias in existing feedback loops
Training marketing and customer success teams on survey design best practices
Launching product satisfaction (CSAT) or NPS surveys with high response quality