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数据与研究 · SKILL.md

survey-analyzer

survey-analyzer

【数据与研究】Analyze survey results and generate actionable insights. TRIGGERS - Use when user wants to analyze survey data, questionnaire results, or feedback forms.

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技能说明

GitHub

name: survey-analyzer description: | Analyze survey results and generate actionable insights. TRIGGERS - Use when user wants to analyze survey data, questionnaire results, or feedback forms.

Survey Analyzer

Overview

Analyzes survey responses to extract patterns, insights, and actionable recommendations.

Workflow

Step 1: Receive the Data

Accept survey data in any format (CSV, JSON, pasted text, or described verbally).

Step 2: Analyze

  1. Response overview: Total responses, completion rate, date range
  2. Quantitative analysis: Averages, distributions, correlations
  3. Qualitative analysis: Theme extraction from open-ended responses
  4. Segment analysis: Differences between groups
  5. Trend identification: Patterns and outliers

Step 3: Generate Report

Output Format

# Survey Analysis: [Survey Name]

## Overview
- **Total responses**: [N]
- **Completion rate**: [X%]
- **Date range**: [dates]
- **Key finding**: [One-sentence headline result]

## Executive Summary
[3-5 sentences covering the most important findings]

## Key Metrics

| Question/Metric | Result | Benchmark | Status |
|----------------|--------|-----------|--------|
| [Metric 1] | [value] | [if known] | 🟢/🟡/🔴 |

## Detailed Findings

### Finding 1: [Title]
**Data**: [what the numbers show]
**Insight**: [what it means]
**Action**: [what to do about it]

[Repeat for top findings]

## Open-Ended Response Themes
| Theme | Frequency | Sentiment | Sample Quote |
|-------|-----------|-----------|-------------|
| [Theme] | [X mentions] | Positive/Negative | "[quote]" |

## Recommendations
1. [Action item with priority]
2. [Action item with priority]
3. [Action item with priority]

## Methodology Notes
[How analysis was conducted, limitations]

Quality Checklist

  • Key finding stated upfront
  • Quantitative and qualitative data combined
  • Recommendations are specific and actionable
  • Visualizations suggested for key data
  • Limitations acknowledged
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