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user-research-cookiy

Orchestrate end-to-end user research workflows via Cookiy AI.

1.6k|59|Updated Apr 10, 2026
Search Tags:#interviews#surveys#synthesis#user-research#qualitative#quantitative
One-click install
npx skills add https://github.com/cookiy-ai/user-research-skill --skill user-research-cookiy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: user-research-cookiy
Source: https://github.com/cookiy-ai/user-research-skill/tree/main
Command: npx skills add https://github.com/cookiy-ai/user-research-skill --skill user-research-cookiy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, curl, sed, grep, tr, and includes references (resource) components.

What problem does it solve?

Complex, multi-stage human-in-the-loop research for AI agents is often disjointed, forcing teams to stitch together planning, execution, and synthesis across separate tools and processes. This skill centralizes the entire user research lifecycle for AI agents, enabling seamless orchestration from study design to final reports.

Core Features & Use Cases

  • End-to-end lifecycle support: plan qualitative and quantitative studies, generate discussion/interview guides, recruit participants, run AI-moderated interviews (real or synthetic), design and distribute multilingual surveys, and synthesize transcripts into structured reports.
  • Route-based orchestration: choose routes for planning, synthesis, or end-to-end execution via Cookiy AI, with natural-language prompts guiding the workflow.
  • Evidence-backed deliverables: produce codebooks, personas, and prioritized findings, all grounded in transcripts and notes and exportable as reports.

Quick Start

Describe your research objective to the agent and initiate an end-to-end workflow to plan, recruit, run, and synthesize results via Cookiy AI.

Frequently Asked Questions about user-research-cookiy

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

FAQPage Schema
How do I conduct end-to-end user research with AI agents?▼

End-to-end user research with AI agents involves orchestrating the full lifecycle from study design to final reports. This skill centralizes planning, recruiting, executing AI-moderated interviews, and synthesizing findings into structured deliverables.

What is the best way to plan both qualitative and quantitative studies?▼

Planning qualitative and quantitative studies requires generating discussion guides, designing multilingual surveys, and defining participant criteria. Route-based orchestration guides the workflow to establish study design before execution and synthesis.

Can I run AI-moderated interviews and synthesize transcripts into structured reports?▼

Yes, AI-moderated interviews can be run with real or synthetic participants. Transcripts and notes are then synthesized into evidence-backed deliverables like codebooks, personas, and prioritized findings exportable as reports.

Do I need specific command-line dependencies to orchestrate user research workflows?▼

Orchestrating these user research workflows relies on command-line dependencies including jq, curl, sed, grep, and tr. These tools handle data routing and processing for the lifecycle execution.

How are research findings synthesized into prioritized deliverables?▼

Research findings are synthesized by processing interview transcripts and survey data to produce evidence-backed deliverables. This includes generating codebooks, personas, and prioritized findings grounded in the collected qualitative and quantitative data.

Does this approach work for multilingual survey distribution and participant recruitment?▼

Yes, the workflow supports designing and distributing multilingual surveys alongside recruiting participants. It routes these tasks through an orchestrated process to manage the study execution end-to-end.

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