claude

pm-data

Data & analytics skills: Metrics Framework, SQL Query Explainer, Dashboard Brief, Chart Data Extractor, Cohort Analysis, Data Pipeline Spec, A/B Test Readout, Metric Tree Builder, Data Quality Audit. Build metric trees, explain SQL, spec dashboards, run cohort retention with LTV modelling, design ETL/ELT pipelines, read out A/B tests with significance and guardrails, and audit datasets for quality before you trust them.

1,544 on GitHubby Mohit Aggarwalv1.3.0MITupdated yesterdayproductivity
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Plugin pm-data from marketplace pm-claude-skills in mohitagw15856/pm-claude-skills. Commands read from the repo's manifest — yesterday.

What it installs

Skills (9)

  • ab-test-readout

    Analyse a finished A/B test and write the readout — the result, whether it's statistically and practically significant, what it means, and the ship/no-ship call. Use when asked to analyse experiment results, write an A/B test readout, interpret test data, or decide whether to ship a variant. Produce

  • chart-data-extractor

    Extract pixel-level data from an image of a chart or graph and produce a structured data table. Use when asked to extract data from a chart image, transcribe numbers from a graph, digitise a chart, or turn a screenshot of data into a table. Produces a structured table with extracted values, confiden

  • cohort-analysis

    Structure a cohort analysis for retention, LTV, or behavioural patterns. Use when asked to run a cohort analysis, analyse retention by cohort, segment users by behaviour over time, or calculate lifetime value by acquisition period. Produces a complete cohort analysis framework with methodology, coho

  • dashboard-brief

    Convert a business question into a complete dashboard specification. Use when asked to design a dashboard, create a dashboard spec or brief, plan a BI report, or define what charts and metrics a dashboard should include. Produces a structured spec with metrics, dimensions, chart types, filters, and

  • data-pipeline-spec

    Design an ETL/ELT data pipeline specification. Use when asked to design a data pipeline, spec an ETL or ELT process, document a data ingestion workflow, or plan a data integration. Produces a complete pipeline spec with sources, transforms, destinations, SLAs, error handling, and data quality rules.

  • data-quality-audit

    Audit a dataset for the quality problems that silently break analysis — missingness, duplicates, outliers, type and range errors, consistency, and freshness — and produce a prioritised fix list. Use when asked to assess data quality, audit a dataset, check data before analysis, or explain why number

  • metric-tree-builder

    Decompose a north-star metric into a driver tree — the inputs and sub-inputs that actually move it — so a team knows which levers to pull. Use when asked to build a metric tree, break down a north-star metric, map metric drivers, or find the inputs behind an output metric. Produces a hierarchical tr

  • metrics-framework

    Build a metrics framework for any product, team, or business. Use when asked for a metrics tree, KPI framework, North Star metric, AARRR funnel, HEART framework, or OKR metrics. Produces a structured metrics hierarchy from North Star down to leading indicators, with measurement guidance.

  • sql-query-explainer

    Explains, optimises, writes, and documents SQL queries. Use when asked to explain a SQL query, optimise slow SQL, translate SQL to plain English for non-technical stakeholders, write a query from a natural language description, or produce query documentation. Produces plain-English explanations, ann

View source on GitHub mohitagw15856.github.io

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