Skills anthropics knowledge-work-plugins

analyze

Use it when:: You have a data question, from how many users signed up last week to what's driving a drop in conversion.
It does:: Sizes the question, writes and runs SQL on a connected warehouse or works from data you paste or upload, then sanity-checks the results.
You get:: A direct answer with its query, an analysis that leads with the key finding, or a formal report with methodology, caveats and recommendations.
Checks:: Row counts, unexpected nulls, magnitudes, gaps in time series and whether subtotals add up, before presenting anything.
Works with:: The data plugin's sql-queries skill for SQL dialects and its data-visualization skill for choosing charts.
Runs:: As /analyze with a question in plain language, or when you ask one.
Part of:: The data plugin of Anthropic's knowledge-work plugins.

TL;DR

A data-question command from the data plugin of Anthropic's knowledge-work plugins. It first sorts the question into a quick answer (one metric or lookup), a full analysis (trends, drivers, comparisons) or a formal report, and works out the tables, metrics, dimensions and time range it needs. With a data warehouse connected, it explores the schema, writes and runs SQL, debugs failed queries and sanity-checks unexpected results. Without one, it asks you to paste results, upload a CSV or Excel file, or describe the schema so it can write queries for you to run, using the plugin's sql-queries skill for the dialect. It calculates metrics, finds patterns, outliers and anomalies, compares segments and periods, and breaks complex questions into sub-questions. Before presenting, it checks row counts, nulls, magnitudes, time-series gaps and that subtotals sum to totals, and notes caveats. Quick answers come with the query used; reports add an executive summary, methodology, findings, limitations and next steps. When a chart helps, it picks one with the data-visualization skill and draws it in Python or builds it into an HTML dashboard.

Summarizes the skill at ab4cb1c.

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill analyze

skills.sh page Source on GitHub All skills from anthropics/knowledge-work-plugins

Total Downloads: 5,800 on skills.sh, Nov 9, 2026
Repo stars: 35,234 anthropics/knowledge-work-plugins
First seen: Mar 13, 2026 on skills.sh

Weekly Downloads

-28% over the 8 weeks to Nov 9

Weekly values

Week to	Installs
Sep 21	90
Sep 28	63
Oct 5	59
Oct 12	61
Oct 19	70
Oct 26	71
Nov 2	70
Nov 9	65

Category	category:science Research reproduction, financial data, charts and diagrams.
Type	type:workflow A multi-step process the agent follows: interview, debug, run a TDD loop, review.
Effect	effect:local Its own steps create or change files, git state or local tools.
Needs	needs:none Instructions only. Works in any agent that supports skills.
Publisher	by:official A verified publisher on skills.sh, or the company or maintainer behind the product the skill operates.
Topics	databases research charts

SKILL.md

From anthropics/knowledge-work-plugins at ab4cb1c. Checked daily, last changed Nov 6, 2026.

Frontmatter

name: analyze
description: 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 preparing a formal data report for stakeholders.
argument-hint: "<question>"

/analyze - Answer Data Questions

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Answer a data question, from a quick lookup to a full analysis to a formal report.

Usage

/analyze <natural language question>

Workflow

1. Understand the Question

Parse the user's question and determine:

Complexity level:
- Quick answer: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
- Full analysis: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
- Formal report: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
Data requirements: Which tables, metrics, dimensions, and time ranges are needed
Output format: Number, table, chart, narrative, or combination

2. Gather Data

If a data warehouse MCP server is connected:

Explore the schema to find relevant tables and columns
Write SQL query(ies) to extract the needed data
Execute the query and retrieve results
If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
If results look unexpected, run sanity checks before proceeding

If no data warehouse is connected:

Ask the user to provide data in one of these ways:
- Paste query results directly
- Upload a CSV or Excel file
- Describe the schema so you can write queries for them to run
If writing queries for manual execution, use the sql-queries skill for dialect-specific best practices
Once data is provided, proceed with analysis

3. Analyze

Calculate relevant metrics, aggregations, and comparisons
Identify patterns, trends, outliers, and anomalies
Compare across dimensions (time periods, segments, categories)
For complex analyses, break the problem into sub-questions and address each

4. Validate Before Presenting

Before sharing results, run through validation checks:

Row count sanity: Does the number of records make sense?
Null check: Are there unexpected nulls that could skew results?
Magnitude check: Are the numbers in a reasonable range?
Trend continuity: Do time series have unexpected gaps?
Aggregation logic: Do subtotals sum to totals correctly?

If any check raises concerns, investigate and note caveats.

5. Present Findings

For quick answers:

State the answer directly with relevant context
Include the query used (collapsed or in a code block) for reproducibility

For full analyses:

Lead with the key finding or insight
Support with data tables and/or visualizations
Note methodology and any caveats
Suggest follow-up questions

For formal reports:

Executive summary with key takeaways
Methodology section explaining approach and data sources
Detailed findings with supporting evidence
Caveats, limitations, and data quality notes
Recommendations and suggested next steps

6. Visualize Where Helpful

When a chart would communicate results more effectively than a table:

Use the data-visualization skill to select the right chart type
Generate a Python visualization or build it into an HTML dashboard
Follow visualization best practices for clarity and accuracy

Examples

Quick answer:

/analyze How many new users signed up in December?

Full analysis:

/analyze What's causing the increase in support ticket volume over the past 3 months? Break down by category and priority.

Formal report:

/analyze Prepare a data quality assessment of our customer table -- completeness, consistency, and any issues we should address.

Tips

Be specific about time ranges, segments, or metrics when possible
If you know the table names, mention them to speed up the process
For complex questions, Claude may break them into multiple queries
Results are always validated before presentation -- if something looks off, Claude will flag it