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Data Analyst
Not "write my SQL" but "know my definitions and show your work": skills for the intake, the metric, the query, the check, the readout and the memo, built from how analysts describe their week with agents.
Workflow, stage by stage
10 stages. What you do, what the agent does, and what stays with you.
Intake: from a vague ask to a briefshared with BA, PM
What you do
- Turn the request into a decision question
- Pin the metric, grain, filters and time window
- Name the definition source before any SQL
DeliverableOne-paragraph analysis brief the stakeholder signs off
Stays with youThe ambiguity itself. "Active customer revenue last month" hides three undefined terms; resolving them is the job.
“The system can write perfect SQL and still miss the business meaning entirely.”Jose Parreño Garcia, senior data science lead ↗
Skills that do it
Structured requirements elicitation for analysis requests. Use when scoping new analysis projects, clarifying ambiguous business questions, or documenting analysis acceptance cr...
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
Gather business context from connected or provided sources so downstream analysis starts with the right framing. Use when an analytical question depends on missing context, such...
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data a...
Pressure-test an idea's premise before helping build it. Use whenever a user proposes building, shipping, or starting something — an app, product, feature, startup, side project...
Metric definitions and the semantic layershared with PM, BA
What you do
- Draft metric definitions people can agree on, with lineage
- Reconcile two numbers that disagree
- Keep the semantic layer the single source for BI and agents
DeliverableMetrics dictionary, semantic model (dbt or YAML), reconciliation notes
Stays with youAgreeing the definition with the business. Anything outside the semantic layer is unsupported by design.
“Skills protect against plausible but incorrect output.”dbt Labs (vendor) ↗
Industry notes for SaaS and B2B software, E-commerce and retail, Fintech and payments, Marketplaces and platforms, Healthcare, Games and live ops. Pick an industry above to read them.
Skills that do it
Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML de...
Trace and resolve discrepancies when the same metric shows different values in two or more sources. Use before reporting, after pipeline changes, or when stakeholders question a...
Standard business metric calculation with industry benchmarks. Use when calculating SaaS metrics (MRR, churn, LTV, CAC), e-commerce KPIs, or product analytics metrics with prope...
Use when creating or modifying dbt Semantic Layer components — semantic models, metrics, dimensions, entities, measures, or time spines. Covers MetricFlow configuration, metric ...
Design KPI frameworks, metric definitions, targets, guardrails, and measurement plans for product or business decisions. Use when success metrics, drivers, guardrails, targets, ...
Compare two metric definitions that should produce the same number and find exactly where they disagree. Use when the user says "these numbers don't match", "why do two dashboar...
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics d...
Acquire, clean and check the data
What you do
- Pull from warehouse, API, CSV or Excel
- Run the quality audit: nulls, duplicates, ranges, row counts
- Log every cleaning step and every column touched
DeliverableClean table with a cleaning log and a row-count and column diff
Stays with youWatching for silent drops. Agents have quietly lost columns and fetched a third of the rows because of pagination; the audit catches what the chat did not mention.
“Claude Code isn't going to replace data engineers (yet).”Robin Moffatt, after a dbt run silently dropped columns ↗
Industry notes for Healthcare, Fintech and payments. Pick an industry above to read them.
Skills that do it
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production us...
Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields b...
Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflictin...
Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, ...
QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggrega...
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user...
SQL writing and reviewshared with PM
What you do
- Draft the query from the brief, with the SQL shown
- Review joins for fan-out, grain, nulls and date boundaries
- Explain the query in English and reconcile row counts
DeliverableReviewed query saved as a reusable view, with an English explanation
Stays with youThe review. Text-to-SQL fails silently and confidently; most wrong queries are schema-level mistakes.
“I know the relevant libraries well enough to read and verify the code, which is an important distinction from blindly using a black box AI.”Data analyst at a geology firm, Hacker News ↗
“The real danger is that text-to-SQL fails silently and confidently.”Omni (vendor) ↗
Industry notes for Fintech and payments, Healthcare. Pick an industry above to read them.
Skills that do it
Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, opt...
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, transl...
SQL query review for correctness, performance, and best practices. Activate when a query needs review before production use, shows unexpected results, or runs too slowly.
Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or bui...
Explain complex SQL queries in plain English with Mermaid data flow diagrams, performance annotations, and anti-pattern detection. Use when the user pastes a SQL query and asks ...
Analyze and optimize slow SQL queries. Use when the user says a query is slow, asks to optimize or speed up SQL, wants to find anti-patterns, needs index recommendations, or ask...
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation...
Optimize SQL query performance using EXPLAIN analysis, indexing strategies, and common anti-pattern fixes. Use this skill when the user needs to speed up slow queries, design in...
Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, ...
Exploratory analysis and root causeshared with PM
What you do
- Run the EDA and the quarter-over-quarter comparison
- Decompose a miss into drivers with a waterfall
- Split claims the data supports from hypotheses
DeliverableFindings doc with charts and an explicit supported-versus-hypothesis split
Stays with youSign-off on every claim. Agents have confidently attributed a gap to sales effectiveness with nothing in the data to support it.
“Every number, every claim, every conclusion still needs a human sign-off.”Data analyst, after matching three days of work in an hour ↗
Industry notes for E-commerce and retail, SaaS and B2B software. Pick an industry above to read them.
Skills that do it
Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.
Systematic investigation of metric changes and anomalies. Use when a metric unexpectedly changes, investigating business metric drops, explaining performance variations, or dril...
Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testin...
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting d...
Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or...
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand...
Diagnose why a metric changed or differs from expectation. Use when the task is to identify likely drivers of a metric movement, anomaly, gap, or discrepancy.
Perform comprehensive Exploratory Data Analysis on any dataset. Use when the user mentions a new dataset, says "explore this data", "profile this table", "what does this data lo...
Experiments and statisticsshared with PM
What you do
- Size the test from baseline, effect and traffic
- Check sample ratio mismatch and segment effects
- Compute effects with intervals; use CUPED or sequential tests where they apply
- Write the go, no-go or partial decision
DeliverableExperiment readout with primary and guardrail metrics and a decision
Stays with youChoosing the primary metric and the exposure unit, and refusing to peek. The skills run the arithmetic and the checks.
Industry notes for E-commerce and retail, Consumer apps, Games and live ops. Pick an industry above to read them.
Skills that do it
Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test desig...
How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metr...
A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segm...
Design and analyze A/B tests with proper statistical methodology including sample size calculation, randomization, frequentist and Bayesian approaches, and sequential testing. U...
Conduct statistical hypothesis testing including null/alternative hypothesis formulation, p-values, Type I/II errors, and test statistic selection. Use this skill when the user ...
Apply causal inference methods — counterfactual framework, instrumental variables, propensity score matching, and difference-in-differences — to estimate causal effects from obs...
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, det...
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment result...
Perform rigorous A/B test analysis with statistical significance testing, sample size validation, and ship/no-ship recommendations. Use when the user mentions A/B tests, experim...
Cohorts, funnels and forecastsshared with PM
What you do
- Build cohort and retention views
- Map and measure the funnel
- Forecast with a stated method and a stated error
DeliverableCohort tables, funnel report, forecast with intervals
Stays with youPicking the cohort key and the funnel definition; both encode a business decision.
Industry notes for E-commerce and retail, Marketplaces and platforms, SaaS and B2B software, Games and live ops. Pick an industry above to read them.
Skills that do it
Time-based cohort analysis with retention and behaviour tracking. Activate when you need to measure how groups of users/customers behave over time — retention rates, revenue by ...
Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or opti...
Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting mode...
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying fe...
Conduct cohort analysis to track user behavior over time, build retention matrices, and compare cohort performance. Use this skill when the user needs to measure retention, unde...
Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, h...
Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential ...
Perform RFM (Recency, Frequency, Monetary) customer segmentation from transaction data. Use this skill when the user needs to segment customers by purchase behavior, identify hi...
Architecting cross-tool conversion flows that match audience and stage. Landing page to lead magnet to nurture sequence to offer to advanced funnels. Honest about silo-funnels (...
Dashboards and recurring reportsshared with BA
What you do
- Write the dashboard spec: metric, grain, filters, owner, cadence
- Build it in the BI tool or as an HTML page
- Produce the weekly report pack
DeliverableDashboard spec, the dashboard, the weekly pack
Stays with youChart choice and layout judgment; publication figures still need hand tweaks.
Industry notes for E-commerce and retail, Games and live ops. Pick an industry above to read them.
Skills that do it
Design specifications for effective dashboards. Use when planning new dashboards, improving existing ones, or documenting dashboard requirements before development starts.
Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best...
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics d...
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contain...
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, gene...
Build source-backed dashboards for monitoring performance, exploring drivers, or acting on product and business metrics. Use when the task needs a dashboard, scorecard, or monit...
Prepare KPI readouts, scorecards, WBR/MBR/QBR updates, and executive summaries from quantitative business or product metrics; use when the task is to report status, compare agai...
Generate recurring weekly or monthly analytics reports with period-over-period comparison, anomaly detection, and executive summaries. Use when the user asks for a weekly report...
Looker Developer Onboarding: Step 7 (Final Step). Creates a LookML dashboard in the project, imports it as a user-defined dashboard (UDD) in Looker, and iteratively refines it b...
Design effective data dashboards with proper KPI hierarchy, chart type selection, and interactive features. Use this skill when the user needs to create a dashboard, choose the ...
Narrative and stakeholder handoffshared with PM, BA
What you do
- Write the insight memo: findings, confidence, caveats, next steps
- Produce the three-bullet summary for leadership
- Translate method into business language
DeliverableInsight memo, executive summary, the answer to the follow-up question
Stays with youThe recommendation. Agents write a competent professional's memo; what to do about it is yours.
“Claude, on the other hand, sounds like a competent professional.”Jess Ramos ↗
Skills that do it
Build compelling data-driven narratives. Use when presenting analysis results, creating stakeholder reports, or transforming a set of findings into a story that drives a specifi...
Create concise executive summaries from detailed analysis. Use when preparing board decks, executive briefings, or condensing complex analysis into decision-ready formats for se...
Transform data findings into compelling insights. Use when converting analysis results into actionable insights, connecting findings to business impact, or preparing insights fo...
Translate technical analysis into business language. Use when explaining statistical concepts to non-analysts, simplifying technical findings, or bridging communication between ...
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or buildi...
Build polished analytical reports for executive, product, business, or technical audiences. Use when the task needs a durable answer-first narrative with evidence-backed finding...
Generate structured analytics reports with metrics, trends, and visualizations. Use when the user asks for a business review, monthly report, executive summary, deep dive, incid...
Documentation and QA
What you do
- Document the analysis, its assumptions and its data
- Run the QA checklist and a peer review
- Keep the catalog entry and the dbt docs current
DeliverableAnalysis doc, assumptions log, catalog entry, reviewed notebook
Stays with youDeciding what is reusable. Reproducible scripts beat chat state; sessions do not remember.
“With learning to use these tools comes learning WHAT to hand over to them and what to keep tight reins on yourself.”Analytics engineer ↗
Skills that do it
Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for...
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytic...
Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring cl...
Structured peer review for analytical work. Use when reviewing teammates' analysis, providing constructive feedback, or establishing analysis quality standards.
Audits dbt documentation coverage and drafts missing model/column descriptions in the project's own house style, one folder at a time, for human review. Use when documenting und...
Create, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can ins...
Packages for this role
Ranked by weekly runs, like the ladder. Install any of them into any supported client.
- 7Analytics with Claude CodeadityawrkGitHub ↗
Metric reconciliation and calculation, A/B readout, data quality, EDA, explain and optimize SQL, weekly report, systematic debugging.
Analytics0runs / wkno runs yet - 8Anti-Sycophantmachinesoul11GitHub ↗
Three skills that stop an AI assistant from agreeing with a bad product idea: prove the premise, hobby or business, one real conversation.
Validation0runs / wkno runs yet - 9Asgard Analytics Subsetasgard-ai-platformGitHub ↗
Statistics, forecasting and e-commerce analytics: EDA, hypothesis tests, A/B, causal inference, cohorts, dashboards, SQL optimization, ARIMA and Prophet forecasts, RFM.
Analytics0runs / wkno runs yet - 11Business AnalyticswshobsonGitHub ↗
KPI dashboard design and data storytelling: choose the metrics, lay out the dashboard, and turn results into a narrative stakeholders act on.
Analytics0runs / wkno runs yet - 12Data Analytics (role plugin)openaiGitHub ↗
Metric diagnostics, data quality analysis, KPI design and reporting, dashboards, reports, notebooks, market sizing, business context gathering.
Analytics0runs / wkno runs yet - 13Data Analytics SkillsnimrodfisherGitHub ↗
31 analyst skills with scripts: EDA, data quality audit, query validation, semantic model, metric reconciliation, A/B, cohort, funnel, time series, dashboard spec, narrative, executive summary, stakeholder intake.
Analytics0runs / wkno runs yet - 14dbt Skillsdbt-labsGitHub ↗
dbt Labs' official skills: answer questions through the semantic layer, build semantic models, write and document models, run and troubleshoot dbt, unit tests, state, mesh.
Analytics0runs / wkno runs yet - 15Experimentation SkillsrampstackcoGitHub ↗
Experiment design and analysis done properly: CUPED, sequential testing, ratio metrics, dashboard reconciliation, plus analytics strategy, product analytics setup and funnel architecture.
Analytics0runs / wkno runs yet - 18Data (Knowledge Work Plugins)anthropicsGitHub ↗
Anthropic's data plugin: write and review queries, explore data, statistical analysis, charts and dashboards, validate data, extract data context.
Analytics0runs / wkno runs yet - 19Looker Skillslooker-open-sourceGitHub ↗
Google's Looker skills: dashboards, LookML models, views, explores, fields, tests, refinements, performance, CLI, BigQuery connection.
Analytics0runs / wkno runs yet - 21pm-data-analyticsphurynGitHub ↗
3 analytics skills for product work: A/B test analysis, cohort analysis, and SQL queries written from a plain question.
Product0runs / wkno runs yet
Recommendations are curated in catalog/professions.json (updated 2026-09-24) and reviewed like code. Quotes link to the practitioner who said them, and vendors are marked as such. Nothing here is ranked by payment.