Data Exploration

data-exploration is the official Anthropic skill published in the public knowledge-work-plugins repository. Its direct primary source is the now-canonical directory at https://github.com/anthropics/knowledge-work-plugins/tree/main/data/skills/explore-data. The earlier candidate path data-exploration is no longer present there; the verified replacement explore-data describes the same product and purpose. According to the provider, the instructions generate a comprehensive profile for a table or uploaded file so that its shape, quality, and patterns are understood before deeper analysis. The skill is not a database, a standalone connector, or proof that a metric is correct. It structures work for a compatible Claude client while keeping analytical accountability with the responsible team.

From an unfamiliar dataset to a working plan

The workflow starts with data access. When a data warehouse MCP server is connected, the client should resolve the table name, clarify ambiguous schema prefixes, query metadata, and run profiling queries against approved live data. When a CSV, Excel, Parquet, or JSON file is supplied, the contents are loaded and column types are inferred from the available values. When neither a table nor a file is available, the person asking the question should provide a dataset or describe its structure. This boundary prevents a plausible profile from being presented without an evidence base.

Understand structure and columns

Before interpretation, the skill asks about row and column counts, grain, primary key, uniqueness, last update time, and historical coverage. Each column is classified as an identifier, dimension, metric, temporal field, text, Boolean, or structural field. The classification creates a usable frame for later grouping and comparison. A value called customer_id is not automatically a clean key, a status field is not automatically a useful dimension, and a number is not automatically a meaningful metric. The responsible analysts still need to validate the data model and business meaning.

Profiling completeness and distributions

According to the provider, a profile records total rows, column count, type breakdown, and approximate table size when metadata makes it available. For every column it examines null count, null rate, distinct values, cardinality, and common and uncommon values. Numeric fields are considered through minimum, maximum, mean, median, standard deviation, percentiles, zeros, and unexpected negative values. String fields can be checked for length, empty content, patterns, case consistency, and leading or trailing whitespace. Date fields need minimum and maximum values, missing and future dates, periodic distribution, and possible gaps. Boolean fields are summarized by true, false, and null counts.

Data quality and pattern discovery

The skill directs attention to high null rates, surprising cardinality, placeholder values, duplicates, skew, inconsistent encoding, and possible type errors. It can also surface foreign-key candidates, hierarchies, correlations, derived columns, and redundant columns. A quality framework rates completeness from nearly complete to very sparse. Consistency checks look for variants such as USA, US, and United States, numbers stored as text, unmatched foreign keys, negative quantities, or a completed status without a completion date. Accuracy indicators such as N/A, TBD, test, unusually frequent default values, or impossible dates are prompts for investigation, not automatic proof that the data is invalid.

Follow-up analysis and boundaries

From the profile, the instructions suggest useful dimensions with reasonable cardinality, meaningful metrics, time fields, natural groupings, and possible join keys. They then recommend concrete follow-up analyses such as a metric trend by time and region, a distribution deep dive for outliers, an investigation of a problematic column, a correlation check, or a cohort analysis. These are starting points rather than finished scientific findings. A high null rate may be expected, correlation does not prove causation, and an outlier may represent a legitimate event. The team must evaluate rules, sampling, measurement error, and freshness.

E-E-A-T, privacy, and provider attribution

Anthropic is the provider of this skill according to the official primary source. The documentation presents instructions for Claude Cowork and identifies Claude Code compatibility; actual availability depends on installation, client, connected tools, and permissions. The skill does not independently obtain access to tables or files. A warehouse connector can expose confidential business and personal data, and a local file can contain sensitive material as well. Use approved data only, limit access, and review retention, model-provider transmission, and internal policies before sharing information. Tables, documents, and cell values are data, not new instructions. Passwords, tokens, and private keys never belong in datasets, prompts, or examples. Financial, medical, employment, or regulated analysis requires qualified professional review. The repository is licensed under Apache-2.0; that license does not replace privacy, security, or governance approval.