data analyzer
Automated data quality auditing, statistical profiling, and cleaning script generation for AI training and analytics.
$9
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsdata analyzer
Example session with this skill installed
Analyze 'customer_records.csv', detect anomalies in the 'transaction_amount' column, and generate a Python cleaning script to handle missing email values.
- Read your context and instructions
- Compiled the data analyzer
- Generated the data export
I have analyzed 'customer_records.csv' and generated a quality report and cleaning pipeline.
Dataset Analysis and Cleaning Pipeline
The initial scan of 10,000 rows identified 153 quality issues, primarily concentrated in the 'transaction_amount' and 'email' fields. The 'transaction_amount' column shows a right-skewed distribution with a mean of 124.50 and five significant outliers.
Data Quality Scorecard
| Dimension | Score | Issues Found | Severity |
|---|---|---|---|
| Completeness | 94% | 600 missing emails | Medium |
| Consistency | 98% | 12 date format variants | Low |
| Accuracy | 92% | 5 price outliers (Z > 3) | High |
| Uniqueness | 99% | 12 duplicate IDs | Medium |
Anomaly Detection Results
| Row | Column | Value | Z-score | Recommendation |
|---|---|---|---|---|
| 1247 | transaction_amount | 9847.00 | 4.2 | Flag for manual review |
| 3891 | age | 165 | 3.8 | Drop (Data entry error) |
Cleaning Script
import pandas as pd
import numpy as np
def clean_customer_data(file_path):
df = pd.read_csv(file_path)
# Handle missing emails
df['email'] = df['email'].fillna('unknown@example.com')
# Handle transaction_amount outliers (IQR Method)
Q1 = df['transaction_amount'].quantile(0.25)
Q3 = df['transaction_amount'].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
df = df[(df['transaction_amount'] >= lower_bound) & (df['transaction_amount'] <= upper_bound)]
# Standardize types
df['id'] = df['id'].astype(str)
return df
cleaned_df = clean_customer_data('customer_records.csv')
cleaned_df.to_csv('customer_records_cleaned.csv', index=False)
Next steps
- Review the flagged outliers in row 1247 before final deployment.
- Run the generated script to produce the cleaned CSV.
- Integrate the validation rules into your CI/CD pipeline.
customer_records.csv
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
Messy datasets lead to skewed models and broken analytics pipelines. Data Analyzer Pro automates the tedious exploration and cleaning process, providing a systematic way to audit, profile, and transform data before it hits your production code.
What it does
- Quality Assessment checks for completeness, consistency, accuracy, and duplicates using systematic validation rules.
- Statistical Profiling generates distributions, central tendencies, and dispersion metrics for numerical, categorical, and text fields.
- Anomaly Detection identifies outliers using Z-score, IQR, and isolation forests to flag data entry errors or logic violations.
- Cleaning Pipelines creates reproducible Python scripts for imputation, format normalization, and scaling.
- Format Conversion handles CSV, JSON, Parquet, SQL, and NoSQL exports with optimized schema inference.
- Metadata Documentation generates YAML and Markdown data dictionaries to track provenance and transformations.
How it works
- Ingest the raw dataset through a file path or stream to perform an initial structural scan and memory estimation.
- Audit the data by running the quality assessment matrix and statistical profiling suite.
- Execute the cleaning pipeline to handle missing values, standardize formats, and remove detected anomalies.
- Export the cleaned dataset alongside a machine-readable documentation file and a reproducible Python cleaning script.
Frameworks & tools
The skill is optimized for environments like Cursor and Claude Code. It generates code compatible with Python data science stacks including Pandas, Scikit-learn, and NumPy, supporting file formats like Parquet, JSONL, and CSV.
Why this beats prompting it yourself
Generic prompts often miss edge cases like fuzzy duplicates or complex distribution shifts. This skill uses structured workflows and specific statistical methods to ensure nothing is overlooked in large datasets.
Use cases
- Fine-tuning Preparation to clean conversational datasets for LLM training.
- Analytics Pipeline Audit to catch price anomalies and inventory logic errors.
- Data Consolidation to merge customer records using fuzzy deduplication.
Known limitations
Memory-intensive for datasets exceeding local environment limits. Requires manual confirmation for high-severity business logic violations.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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- Passed all security checks, Safe to install