survey-analyzer
Analyze survey responses with Likert scale analysis, cross-tabulations, sentiment scoring, and frequency distributions with visualizations.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill survey-analyzer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads survey-analyzer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Survey Analyzer
Comprehensive survey data analysis with Likert scales, cross-tabs, and sentiment analysis.
Features
- Likert Scale Analysis: Agreement scale scoring and visualization
- Cross-Tabulation: Relationship analysis between categorical variables
- Frequency Analysis: Response distributions and percentages
- Sentiment Scoring: Text response sentiment analysis
- Open-Ended Analysis: Theme extraction from text responses
- Statistical Tests: Chi-square, correlations, significance testing
- Visualizations: Bar charts, heatmaps, word clouds, distribution plots
- Report Generation: Comprehensive PDF/HTML reports
Quick Start
from survey_analyzer import SurveyAnalyzer
analyzer = SurveyAnalyzer()
# Load survey data
analyzer.load_csv('survey_responses.csv')
# Analyze Likert scale question
results = analyzer.likert_analysis('satisfaction', scale_type='agreement')
print(f"Mean score: {results['mean_score']:.2f}")
# Cross-tabulation
crosstab = analyzer.crosstab('age_group', 'product_preference')
print(crosstab)
# Generate report
analyzer.generate_report('survey_report.pdf')
CLI Usage
# Analyze Likert scale
python survey_analyzer.py --data survey.csv --likert satisfaction --output results.pdf
# Cross-tabulation
python survey_analyzer.py --data survey.csv --crosstab age_group product --output crosstab.png
# Sentiment analysis
python survey_analyzer.py --data survey.csv --sentiment comments --output sentiment.html
# Full report
python survey_analyzer.py --data survey.csv --report --output full_report.pdf
API Reference
SurveyAnalyzer Class
class SurveyAnalyzer:
def __init__(self)
# Data Loading
def load_csv(self, filepath, **kwargs) -> 'SurveyAnalyzer'
def load_data(self, data: pd.DataFrame) -> 'SurveyAnalyzer'
# Likert Scale Analysis
def likert_analysis(self, column, scale_type='agreement') -> Dict
def likert_comparison(self, columns: List[str]) -> pd.DataFrame
def plot_likert(self, column, output, scale_type='agreement') -> str
# Frequency Analysis
def frequency_table(self, column) -> pd.DataFrame
def multiple_choice(self, column, delimiter=',') -> pd.DataFrame
def plot_frequencies(self, column, output, top_n=None) -> str
# Cross-Tabulation
def crosstab(self, row_var, col_var, normalize=None) -> pd.DataFrame
def chi_square_test(self, row_var, col_var) -> Dict
def plot_crosstab(self, row_var, col_var, output) -> str
# Sentiment Analysis
def sentiment_analysis(self, column) -> pd.DataFrame
def sentiment_summary(self, column) -> Dict
def plot_sentiment(self, column, output) -> str
# Open-Ended Analysis
def word_frequency(self, column, top_n=20) -> pd.DataFrame
def word_cloud(self, column, output) -> str
def extract_themes(self, column, n_themes=5) -> List[str]
# Statistics
def satisfaction_score(self, columns: List[str]) -> Dict
def response_rate(self) -> Dict
def demographics_summary(self, columns: List[str]) -> pd.DataFrame
# Reporting
def generate_report(self, output, format='pdf') -> str
def summary(self) -> str
Likert Scale Analysis
Standard Scales
# 5-point agreement scale
analyzer.likert_analysis('satisfaction', scale_type='agreement')
# 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree, 5=Strongly Agree
# 5-point frequency scale
analyzer.likert_analysis('usage', scale_type='frequency')
# 1=Never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always
# Custom scale
analyzer.likert_analysis('rating', scale_type='custom',
labels=['Poor', 'Fair', 'Good', 'Excellent'])
Results
results = analyzer.likert_analysis('satisfaction')
# {
# 'mean_score': 4.2,
# 'median': 4,
# 'mode': 5,
# 'distribution': {1: 2, 2: 5, 3: 15, 4: 40, 5: 38},
# 'percentages': {1: 2%, 2: 5%, 3: 15%, 4: 40%, 5: 38%},
# 'top_2_box': 78%, # % Agree + Strongly Agree
# 'bottom_2_box': 7% # % Disagree + Strongly Disagree
# }
Visualization
# Stacked bar chart
analyzer.plot_likert('satisfaction', 'likert_chart.png')
# Compare multiple questions
analyzer.likert_comparison(['quality', 'value', 'service'])
analyzer.plot_likert_comparison(['quality', 'value', 'service'],
'comparison.png')
Frequency Analysis
Single Choice
freq = analyzer.frequency_table('age_group')
# Count Percentage
# 18-24 45 22.5%
# 25-34 78 39.0%
# 35-44 52 26.0%
# 45+ 25 12.5%
# Plot
analyzer.plot_frequencies('age_group', 'age_distribution.png')
Multiple Choice
For questions allowing multiple selections:
# Data format: "Option A, Option B, Option C"
results = analyzer.multiple_choice('features_liked', delimiter=',')
# Count Percentage
# Price 120 60%
# Quality 95 47.5%
# Design 80 40%
# Durability 70 35%
analyzer.plot_frequencies('features_liked', 'features.png', top_n=10)
Cross-Tabulation
Basic Cross-Tab
crosstab = analyzer.crosstab('age_group', 'satisfaction')
# Satisfied Neutral Dissatisfied
# 18-24 30 10 5
# 25-34 60 15 3
# 35-44 40 8 4
# 45+ 18 5 2
# With percentages
crosstab_pct = analyzer.crosstab('age_group', 'satisfaction',
normalize='index') # Row percentages
Statistical Testing
result = analyzer.chi_square_test('age_group', 'satisfaction')
# {
# 'statistic': 12.45,
# 'p_value': 0.014,
# 'significant': True,
# 'interpretation': 'There is a significant relationship between
# age_group and satisfaction (p=0.014)'
# }
Visualization
# Heatmap
analyzer.plot_crosstab('age_group', 'satisfaction', 'crosstab_heatmap.png')
Sentiment Analysis
Analyze open-ended text responses:
# Analyze all comments
sentiment_df = analyzer.sentiment_analysis('comments')
# comment polarity sentiment
# 0 "Great product!" 0.8 Positive
# 1 "Could be better" 0.1 Neutral
# 2 "Very disappointed" -0.6 Negative
# Summary
summary = analyzer.sentiment_summary('comments')
# {
# 'positive': 65%,
# 'neutral': 20%,
# 'negative': 15%,
# 'avg_polarity': 0.35
# }
# Visualize
analyzer.plot_sentiment('comments', 'sentiment_distribution.png')
Open-Ended Analysis
Word Frequency
words = analyzer.word_frequency('comments', top_n=20)
# Word Frequency
# 0 great 45
# 1 quality 38
# 2 price 32
# ...
Word Cloud
analyzer.word_cloud('comments', 'wordcloud.png')
Theme Extraction
themes = analyzer.extract_themes('feedback', n_themes=5)
# ['product quality', 'customer service', 'pricing',
# 'delivery speed', 'user experience']
Satisfaction Metrics
Net Promoter Score (NPS)
nps = analyzer.nps_score('recommendation') # 0-10 scale
# {
# 'promoters': 65%, # 9-10
# 'passives': 25%, # 7-8
# 'detractors': 10%, # 0-6
# 'nps': 55
# }
Overall Satisfaction
satisfaction = analyzer.satisfaction_score([
'product_quality',
'customer_service',
'value_for_money',
'ease_of_use'
])
# {
# 'overall_score': 4.3,
# 'category_scores': {...},
# 'satisfaction_rate': 86% # % scoring 4-5
# }
Demographics Analysis
demographics = analyzer.demographics_summary([
'age_group',
'gender',
'location',
'income_range'
])
# Returns frequency tables for each demographic variable
Response Rate Analysis
response_rate = analyzer.response_rate()
# {
# 'total_respondents': 200,
# 'completion_rate': 85%,
# 'average_time': '5m 30s',
# 'dropout_points': {
# 'question_5': 8%,
# 'question_12': 5%
# }
# }
Report Generation
Comprehensive Report
analyzer.generate_report('survey_report.pdf', format='pdf')
Report includes:
- Executive summary
- Response rate and demographics
- Question-by-question analysis
- Likert scale visualizations
- Cross-tabulations
- Sentiment analysis
- Key findings and recommendations
Custom Report Sections
analyzer.set_report_sections([
'executive_summary',
'demographics',
'likert_questions',
'cross_tabs',
'sentiment',
'recommendations'
])
Advanced Features
Filter by Segment
# Analyze subset of responses
analyzer.filter('age_group', '25-34')
results = analyzer.likert_analysis('satisfaction')
analyzer.clear_filter()
Compare Segments
comparison = analyzer.compare_segments(
segment_col='age_group',
metric_col='satisfaction'
)
# Shows how different segments scored the metric
Trend Analysis
For longitudinal surveys:
trends = analyzer.trend_analysis(
metric='satisfaction',
time_col='survey_date',
period='month'
)
analyzer.plot_trends(trends, 'satisfaction_trend.png')
Dependencies
- pandas>=2.0.0
- numpy>=1.24.0
- scipy>=1.10.0
- textblob>=0.17.0
- matplotlib>=3.7.0
- seaborn>=0.12.0
- wordcloud>=1.9.0
- reportlab>=4.0.0
FAQ
What does the survey-analyzer skill do?
Analyze survey responses with Likert scale analysis, cross-tabulations, sentiment scoring, and frequency distributions with visualizations.
How do I install survey-analyzer?
Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill survey-analyzer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/survey-analyzer/ (personal) or .claude/skills/survey-analyzer/ (project).
Which AI agents can use survey-analyzer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is survey-analyzer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains survey-analyzer?
It is published by dkyazzentwatwa in dkyazzentwatwa/chatgpt-skills. It has 72 recorded installs.
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