data-analyst

Overview of data-analyst skill

The data-analyst skill is a lightweight, focused prompt layer for Data Analysis tasks that need SQL, pandas, and basic statistical reasoning. It is best for users who already have a dataset, table schema, query goal, or exploratory question and want more reliable analytical output than a generic chat prompt usually gives.

What data-analyst is designed to do

This data-analyst skill steers an agent toward:

• writing SQL for extraction and transformation
• using pandas for cleaning, grouping, reshaping, and time-based work
• applying descriptive statistics, correlation checks, and simple hypothesis-testing logic
• returning code plus interpretation, not just commentary

The real job-to-be-done is not “be analytical” in the abstract. It is to turn a vague request like “find churn drivers” or “help me explore this CSV” into executable analysis steps, code, and findings you can inspect.

Who should install the data-analyst skill

Best fit:

• analysts who want faster first-draft SQL or pandas workflows
• engineers who occasionally need data exploration help
• AI users who want code-backed answers instead of high-level advice
• teams using agents for ad hoc analysis, data cleaning, or exploratory diagnostics

Less ideal:

• users expecting automated chart rendering, notebook execution, or database connectivity from the skill alone
• advanced statisticians needing rigorous model selection, causal inference, or production-grade ML pipelines

What makes this data-analyst skill different from a generic prompt

The main advantage of data-analyst is scope clarity. The skill explicitly centers SQL, pandas, and statistics, so the agent is more likely to:

• choose the right analytical tool for the question
• produce structured code instead of hand-wavy explanation
• include comments, example outputs, performance notes, and interpretation
• stay anchored to common data analysis workflows

That makes it more useful for real work than a broad “analyze this data” prompt, especially when you need something you can run or adapt quickly.

What the repository includes

This skill is intentionally minimal. The repository evidence shows only a single SKILL.md file and no helper scripts, rules, references, or sample datasets. That matters for adoption:

• setup is simple
• behavior is easy to understand
• there is less hidden logic
• output quality depends heavily on the quality of your prompt and data context

If you want a deeply opinionated framework with test assets or decision trees, this is not that. If you want a clean data-analyst skill you can invoke quickly for SQL/pandas/statistics work, it is a good fit.

How to Use data-analyst skill

Install context for data-analyst skill

If your agent environment supports GitHub-hosted skills, install data-analyst from the repository that contains it:

npx skills add Shubhamsaboo/awesome-llm-apps --skill data-analyst

If your client uses a different skills loader, adapt the source path to:

awesome_agent_skills/data-analyst

Because this repo exposes only SKILL.md, there are no extra dependency files you need to inspect before deciding whether to try it.

Read this file first before using data-analyst

Start with:

• awesome_agent_skills/data-analyst/SKILL.md

There are no supporting README.md, metadata.json, rules/, or resources/ files in this skill directory, so nearly all of the usable guidance is in that one file. Read it to understand:

• when the skill should be applied
• its expected competency areas
• the preferred output style

What input the data-analyst skill needs

The data-analyst install step is easy; good results depend on the input you provide after installation. At minimum, give the agent some combination of:

• table schema or CSV column names
• data types and date fields
• business question
• sample rows
• desired grain, filters, or time range
• output preference: SQL, pandas, stats explanation, or all three

Weak input:

• “Analyze my sales data.”

Strong input:

• “Use the data-analyst skill. I have an orders table with order_id, customer_id, order_date, country, channel, revenue, and is_refunded. Write SQL to calculate monthly revenue, refund rate, and repeat-purchase rate for 2024 by country and channel. Then explain what patterns to look for.”

The stronger version reduces guesswork on metrics, dimensions, and time scope.

How to turn a rough goal into a usable prompt

A good data-analyst usage prompt usually contains five parts:

1. Context — what dataset or system you have
2. Question — what decision or insight you need
3. Structure — schema, columns, joins, date rules
4. Constraints — SQL dialect, pandas only, no plotting, etc.
5. Output format — query, code, interpretation, validation checks

Example prompt:

“Use the data-analyst skill for Data Analysis. I need pandas code to inspect a customer support CSV. Columns: ticket_id, created_at, resolved_at, priority, channel, csat_score, agent_id. Clean missing values, compute resolution time in hours, summarize by priority and channel, flag outliers, and explain what metrics might indicate process issues. Assume the file is already loaded into a DataFrame named df.”

Best workflow for SQL tasks

For SQL-heavy work, use this sequence:

1. provide schema and join keys
2. define the metric precisely
3. name the SQL dialect if it matters
4. ask for both query and explanation
5. ask for edge-case checks before running

Useful prompt addition:

• “State any assumptions about nulls, duplicate keys, and date boundaries before writing the final query.”

This improves output because SQL errors often come from unstated assumptions, not syntax.

Best workflow for pandas tasks

For pandas work, tell the skill:

• the DataFrame name
• whether dates are already parsed
• expected row count or memory constraints
• whether you need one-off analysis or reusable transformation code

A stronger pandas request:

• “Use pandas only. df has 4 million rows, so avoid unnecessary copies. Show memory-conscious cleaning steps, groupby summaries, and missing-value diagnostics.”

That helps the agent choose more practical code instead of toy examples.

How to ask for statistical analysis well

The data-analyst guide is most useful when the statistical question is concrete. Ask for:

• the hypothesis
• the variables involved
• whether comparison groups exist
• what level of rigor you need

Better:

• “Compare average order value between paid search and organic traffic. Recommend an appropriate significance test, explain assumptions, and show pandas code to run it.”

Worse:

• “Do some stats on this data.”

The skill covers descriptive statistics, correlation analysis, and basic testing logic, but it is not a substitute for a specialized statistical review when decisions are high stakes.

Output to expect from data-analyst usage

According to the skill definition, good outputs should include:

• SQL queries or pandas code
• clear comments
• example results
• performance considerations
• interpretation of findings

That output shape is valuable in practice because it gives you something to run plus enough explanation to sanity-check the logic before execution.

Practical tips that improve output quality

Small prompt upgrades materially improve data-analyst for Data Analysis workflows:

• Specify whether you want exploration or a final metric.
• Tell it if the data is messy, sparse, or wide.
• Mention suspected issues like duplicates, missing timestamps, or inconsistent categories.
• Ask for validation queries, not just the main query.
• Request alternative approaches when there are tradeoffs.

Example:

• “After the main SQL, add a validation query to check duplicate customer_id + order_date combinations and null rates in revenue columns.”

What this skill does not do for you

Because the skill is only a prompt file, it does not itself:

• connect to databases
• execute SQL
• load files
• profile your environment
• enforce statistical correctness

You still need your own runtime, database access, and judgment. The skill improves the agent’s analytical framing; it does not replace tools or domain review.

data-analyst skill FAQ

Is data-analyst skill worth installing if I already use normal prompts?

Usually yes, if you often ask for SQL, pandas, or exploratory analysis. The value is not hidden automation; it is a better default analytical posture. A generic prompt may answer broadly. data-analyst is more likely to give code, assumptions, and interpretation aligned to common analyst work.

Is the data-analyst skill beginner-friendly?

Yes, with one caveat: beginners still need to provide schema and business context. The skill can help you structure an analysis, but it will not rescue an underspecified request. If you are new to SQL or pandas, ask it to explain each step and comment the code heavily.

When should I not use data-analyst?

Skip data-analyst when your task is mainly:

• dashboard design
• advanced machine learning
• causal inference
• data engineering orchestration
• visualization-specific work

It is strongest in exploratory analysis, transformation logic, querying, and straightforward statistical reasoning.

Does data-analyst support a specific database or library stack?

The skill mentions SQL, Python with pandas, and statistical analysis, but it does not lock you to one SQL engine or one data platform. That flexibility is helpful, but it means you should state your dialect explicitly when needed, such as PostgreSQL, BigQuery, Snowflake, or SQLite.

Is this skill enough for production analytics work?

It can accelerate production work, but it is not production assurance by itself. Review generated SQL for performance, confirm metric definitions with stakeholders, and validate outputs on real data. The skill is a drafting and reasoning aid, not an execution guarantee.

How to Improve data-analyst skill

Give the data-analyst skill better analytical context

The biggest quality lever is context density. Include:

• schema
• business definitions
• sample records
• known data quality issues
• success criteria

Without those, the skill may still respond fluently, but the analysis can drift from your actual metric logic.

Ask for assumptions before final code

One of the most effective ways to improve data-analyst skill output is to force assumptions into the open.

Try:

• “Before writing the final SQL, list assumptions about joins, null handling, duplicate events, and time windows.”

This catches common failure modes early:

• inflated counts from one-to-many joins
• wrong date grain
• misread categorical values
• invalid statistical comparisons

Request validation steps, not just answers

A high-quality data-analyst guide prompt asks the model to verify its own work.

Useful additions:

• “Provide one validation query.”
• “Show sanity checks for row counts before and after filtering.”
• “Point out which result would be suspicious and why.”
• “List possible confounders before interpreting the correlation.”

This is often more valuable than asking for longer explanations.

Narrow the task when the first answer is too broad

If the initial response mixes SQL, pandas, and stats all at once, split the workflow:

1. schema understanding
2. extraction query
3. cleaning/transformation
4. statistical interpretation
5. summary for stakeholders

The data-analyst skill performs better when each pass has a single analytical objective.

Improve pandas results with runtime constraints

Pandas output gets better when you tell the model what matters operationally:

• memory sensitivity
• notebook vs script style
• vectorized operations preferred
• readability vs speed tradeoff

Example:

• “Optimize for readable notebook code, but avoid row-wise apply unless necessary.”

That kind of instruction changes code quality in a way that generic prompts often miss.

Iterate on interpretation, not only on code

After the first answer, ask follow-ups like:

• “Which conclusion is strongest, and what evidence supports it?”
• “What could make this result misleading?”
• “What segment cut would you check next?”
• “What additional column would most improve confidence?”

This is where data-analyst for Data Analysis becomes more than code generation. It helps move from extraction to decision support.

Common failure modes to watch for

Even with the data-analyst skill, review outputs for:

• incorrect joins
• unspoken metric assumptions
• null handling mistakes
• overconfident statistical claims
• example outputs that do not match your schema
• inefficient SQL on large tables

The skill is compact and useful, but not deeply constrained by rules or test fixtures, so your review process matters.