Data Analyst
Get SQL, pandas, and statistics help to explore datasets, clean data, and summarize findings for product decisions.
Overview
data-analyst is an agent skill most often used in Grow (also Validate and Build) that produces SQL, pandas, and statistical analysis with interpreted insights.
Install
npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill data-analystWhat is this skill?
- SQL: JOINs, CTEs, window functions, and optimization notes
- pandas: grouping, pivoting, time series, and missing-data handling
- Statistics: descriptive stats, hypothesis tests, correlation, basic predictive modeling
- Outputs include commented code, example results, and interpretation
- Triggers on data cleaning, pattern finding, and transformation tasks
- MIT-licensed skill metadata version 1.0.0 from awesome-llm-apps
Adoption & trust: 3.4k installs on skills.sh; 114k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have raw tables or exports but lack confident SQL, pandas, or stats steps to explore, clean, and explain what the data shows.
Who is it for?
Solo founders analyzing product, marketing, or ops data in SQL warehouses or CSVs before committing to a dashboard or feature bet.
Skip if: Production ML platform setup, real-time streaming pipelines, or teams that need certified BI tooling instead of ad-hoc analysis in chat.
When should I use this skill?
Analyzing data, writing SQL queries, using pandas, performing statistical analysis, or when the user mentions data analysis, SQL, pandas, or statistics.
What do I get? / Deliverables
You receive commented queries and pandas code with example outputs, performance notes, and a clear read on patterns relevant to your decision.
- Commented SQL queries or pandas scripts
- Example result tables and performance considerations
- Short interpretation of findings and patterns
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Grow analytics because the skill optimizes for insight extraction and interpretation after you have data to analyze. SQL extraction, pandas transforms, and hypothesis-style stats are analytics workflows—not frontend polish or launch SEO.
Where it fits
Pull early signup and activation counts with SQL to decide whether the MVP hypothesis holds before a full build.
Run pandas aggregations on weekly revenue and churn exports to explain a dip to yourself or investors.
Draft optimized SQL and transformation logic you will embed in an internal admin reporting endpoint.
How it compares
Analysis playbook skill—not a replacement for Metabase, BigQuery console expertise, or automated ETL orchestration products.
Common Questions / FAQ
Who is data-analyst for?
Indie builders and small teams who want agent help writing SQL, pandas, and stats for exploration without a full-time data hire.
When should I use data-analyst?
In Validate to sanity-check traction data, in Grow for recurring metric deep-dives, or in Build when implementing transforms behind a reporting feature.
Is data-analyst safe to install?
It does not inherently exfiltrate data, but never paste production secrets into prompts—check the Security Audits panel on this Prism page.
SKILL.md
READMESKILL.md - Data Analyst
# Data Analyst You are an expert data analyst with expertise in SQL, Python (pandas), and statistical analysis. ## When to Apply Use this skill when: - Writing SQL queries for data extraction - Analyzing datasets with pandas - Performing statistical analysis - Creating data transformations - Identifying data patterns and insights - Data cleaning and preparation ## Core Competencies ### SQL - Complex queries with JOINs, subqueries, CTEs - Window functions and aggregations - Query optimization - Database design understanding ### pandas - Data manipulation and transformation - Grouping, filtering, pivoting - Time series analysis - Handling missing data ### Statistics - Descriptive statistics - Hypothesis testing - Correlation analysis - Basic predictive modeling ## Output Format Provide SQL queries and pandas code with: - Clear comments - Example results - Performance considerations - Interpretation of findings --- *Created for data analysis and SQL/pandas workflows*