Using Data Validation safely for analysis

What the Data Validation skill is for

The skill, listed in Anthropic's official repository under the name validate-data, is designed to QA a data analysis before it is shared with stakeholders. Per the provider, it covers methodology, accuracy, and bias checks and is typically used before presenting a report, validating a SQL query, or checking whether conclusions are actually supported by the data. In plain terms: before numbers go out to executives or clients, something needs to double-check whether the calculation is correct, whether the question was properly understood, and whether the charts don't accidentally paint a misleading picture. The skill can process documents, notebooks, spreadsheets, SQL queries with their results, or even just a description of the methodology as input.

How the review runs

The review starts with a check of methodology and assumptions: is the right question being answered, are the appropriate data sources and time ranges chosen, is the analyzed population correctly defined, and are comparison periods chosen fairly. It then works through a pre-delivery checklist covering data quality, calculation logic, reasonableness, and presentation. On data quality, it checks whether the correct tables were used, whether the data is fresh enough, whether there are unexpected gaps in time series, and how missing values are handled. On calculation logic, it checks whether groupings are correct, whether percentages sum to one hundred where expected, and whether denominators in rate calculations are non-zero.

Common error sources it catches

An important part of the skill is a catalog of analytical pitfalls it systematically checks against. These include row-count explosion from faulty table joins, survivorship bias, where only surviving or successful cases are considered, incomplete period comparisons, shifting denominators in metrics, mistakenly averaging averages, as well as timezone mismatches and selection bias. These errors happen often in practice because they look plausible at first glance but distort the strength of an analysis on closer inspection. The skill also reviews visualizations: do bar charts start at zero, are scales consistent across comparison charts, do titles actually describe what is shown, and are there distorting effects such as truncated axes or misleading three-dimensional representations.

Output and confidence rating

At the end, the skill delivers structured feedback with an overall assessment on a three-level scale: ready to share, when the analysis is methodologically sound; share with noted caveats, when specific assumptions must be communicated; or needs revision, when concrete errors or missing analyses were found. It also lists concrete suggested improvements, additional analyses that would strengthen the conclusion, and wording for caveats that should be communicated to stakeholders. This format ensures the feedback is not just a vague impression but concretely actionable.

Prerequisites and safe use

To use the skill effectively, the analysis under review should be as complete as possible, including the underlying data sources or at least a traceable description of the calculation steps. Because analyses frequently contain sensitive business figures, the review should take place in an environment that complies with the organization's policy for handling confidential data. It's also worth noting: the skill does not replace human subject-matter review, especially for high-stakes analyses such as investment decisions or regulatory reports. It serves as a structured first line of defense that reliably catches many common errors, but it offers no guarantee of complete correctness.

Limitations in practice

The skill can only check what it has been given. If important context is missing, such as how a dataset was assembled or undocumented filter logic, it cannot identify the corresponding problems. Substantive expert judgments, such as whether a chosen statistical method is truly appropriate for the specific question, still require human expertise. The biggest practical value therefore lies in the combination: the skill handles the systematic, repeatable review work, while people make the final substantive call.