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bcg-survey

Analyze survey datasets with natural language queries and automatic answer decoding.

Updated Aug 23, 2026
Search Tags:#pandas#market research#customer insights#polars#large data#data decoding#survey analysis
One-click install
npx skills add https://github.com/aayush-gc/claude-agent-skill-utilities --skill bcg-survey
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bcg-survey
Source: https://github.com/aayush-gc/claude-agent-skill-utilities/tree/main/.claude/skills/bcg-survey
Command: npx skills add https://github.com/aayush-gc/claude-agent-skill-utilities --skill bcg-survey

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires polars, pandas, openpyxl, and includes scripts (resource) and resources (resource) components.

What problem does it solve?

Analyzing massive survey datasets manually or with traditional tools is slow, memory-intensive, and requires coding expertise. This skill automates the process, allowing natural language queries on millions of rows, with automatic decoding of complex answer codes, saving significant time and effort.

Core Features & Use Cases

  • High-Performance Polars Engine: Queries millions of survey rows 10x faster than traditional methods, with lazy loading to prevent memory issues and ensure efficiency.
  • Automatic Answer Decoding: Converts encoded survey responses (e.g., Q1A1) into human-readable values ("Very Satisfied") on the fly, simplifying interpretation.
  • Conversational Data Analysis: Enables users to ask complex questions about survey demographics, satisfaction, and trends using natural language, making data accessible to non-technical users.
  • Use Case: A market research analyst can upload a multi-million row customer satisfaction survey and instantly ask "What is the average age of 'Very Satisfied' female respondents who heard about us via social media?" without writing a single line of code, accelerating insight generation.

Quick Start

Analyze the survey data and tell me the average age of respondents.