explore-data Skill
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 data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze. Published by anthropics in knowledge-work-plugins.
Decision snapshot
Is this a fit?
Data analysis, Includes SKILL.md, Reusable instructions
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Copy skill directory
22 days ago · Apache-2.0 license
No specific cautions were detected. Review the source and requested permissions before installing.
What is explore-data Skill?
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 data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze. Published by anthropics in knowledge-work-plugins. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Source file: data/skills/explore-data/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use explore-data Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: anthropics
- Repository: anthropics/knowledge-work-plugins
- Skill file: data/skills/explore-data/SKILL.md
What it does
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 data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Skill instructions
/explore-data - Profile and Explore a Dataset If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md. Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis. Usage /explore-data <tablename or file Workflow 1. Access the Data If a data warehouse MCP server is connected: 1. Resolve the table name (handle schema prefixes, suggest matches if ambiguous) 2. Query table metadata: column names, types, descriptions if available 3. Run profiling queries against the live data If a file is provided (CSV, Excel, Parquet, JSON): 1. Read the file and load into a working dataset 2. Infer column types from the data If neither: 1. Ask the user to provide a table name (with their warehouse connected) or upload a file 2. If they describe a table schema, provide guidance on what profiling queries to run 2. Understand Structure Before analyzing any data, understand its structure: Ta
Verified compatibility and discovery
Frequently asked questions
What is explore-data?
explore-data is a open-source AI agent skill with Copy skill directory. 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 data quality issues like.
Who is explore-data best for?
explore-data is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install explore-data?
Install or run explore-data using Copy skill directory. Check explore-data for the latest setup command.
Is explore-data actively maintained?
explore-data may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
