The Data Analyst's Guide to Claude AI Skills

Data analysts live in a strange in-between space. The technical work, writing queries, building models, cleaning data, is rigorous and requires real skill. The communication work, turning those results into something a VP or product manager can act on, is equally important and often underestimated. Most analysts are better at one than the other. Claude skills help with the communication layer regardless of which side is your stronger one.

Turning Raw Data Into Narrative

This is the core challenge. You have a table of numbers, a chart that shows something interesting, a query result that answers the question that was asked. Now you need to write the paragraph that tells the reader what it means, what to do about it, and what they should look at next. This translation work requires both analytical understanding and communication skill, and it's the part that slows most analysts down.

A data narrative skill takes structured data inputs (key metrics, comparison period, notable trends, anomalies) and produces a written analysis with appropriate framing. It knows how to lead with the finding rather than the methodology. It knows how to caveat without undermining the conclusion. It knows when to say "this warrants further investigation" versus "this is clear."

SQL Explanation

Two situations call for SQL explanation. First: you've written a complex query and need to explain it to a non-technical stakeholder in language they can follow. Second: you've inherited someone else's query and need to understand what it actually does before you modify it. A SQL explanation skill handles both: give it a query and get back a plain-English explanation of what it's doing, why each join is there, and what the output represents.

-- Example: a query that looks opaque to non-technical readers
SELECT
  u.user_id,
  u.signup_date,
  COUNT(DISTINCT o.order_id) AS total_orders,
  SUM(o.amount) AS lifetime_value,
  DATEDIFF(MAX(o.created_at), u.signup_date) AS days_to_last_order
FROM users u
LEFT JOIN orders o ON u.user_id = o.user_id
WHERE u.signup_date >= '2025-01-01'
GROUP BY u.user_id, u.signup_date
HAVING total_orders > 0
ORDER BY lifetime_value DESC;

A skill takes this and explains: "This query finds all users who signed up in 2025 or later and have placed at least one order. For each user, it calculates how many orders they've placed, their total spend, and how many days passed between their signup and their most recent order. Results are sorted by highest spenders first." Instantly useful for documentation and stakeholder communication.

Dashboard Commentary

Executive dashboards need accompanying commentary: what changed, why it matters, what's driving the variance from last period, what to watch in the coming weeks. Writing this commentary from scratch for every dashboard delivery is time-consuming. A dashboard commentary skill takes the key metrics and their period-over-period changes and produces a structured commentary that reads as analytical rather than mechanical.

The skill handles the structure. The analyst handles the context only they have: the campaign that launched mid-quarter, the data quality issue that skewed last month's numbers, the product change that explains the user behavior shift.

Anomaly Identification Prompts

Anomaly detection is often a manual eyeballing exercise. A well-built anomaly identification skill takes summary statistics and asks the right questions: what's outside two standard deviations, what's changed directionally when it should be stable, what's correlated when it shouldn't be. The skill prompts systematic thinking about the data rather than relying on the analyst to notice everything intuitively.

Building a Skill That Generates Insights From CSV Summaries

One of the more powerful things you can build is a skill that takes a structured CSV summary (exported from your data tool) and generates a full insights report. The input format is consistent, the analysis framework is encoded in the skill, and the output is a report ready for stakeholder consumption. This kind of skill takes 3-4 hours to build well, but it pays for that investment in the first month of regular use.

Browse data analysis skills on SkillsLib to see what practitioners have built. If you have an insight generation or reporting workflow that works well for your domain, the skills marketplace is a good place to share it.