Data Validation

Data Validation is an official Anthropic skill from the public knowledge-work-plugins repository. The path named by the Smithery candidate, data/skills/data-validation, is no longer reachable in the current repository; Anthropic now exposes the matching, verifiable guidance at data/skills/validate-data. This relocation is recorded openly while the catalog slug preserves the candidate identity. According to the provider, the guidance reviews an analysis before it is shared with stakeholders and produces a structured confidence assessment together with actionable improvement suggestions. The skill is a text-based workflow for Claude, not standalone statistical software, a database service, or a guarantee that an analysis is correct.

Purpose and workflow

The process starts by asking whether the analysis answers the right decision or learning question. It then examines data selection, the time range, the population, the unit of observation, metric definitions, baselines, and comparison periods. That order matters because an unclear question or population can make an analysis unusable even when every calculation is arithmetically correct. The source also calls for visible documentation of data freshness, missing segments, null handling, filters, and possible duplicates. Anyone sharing reproducible results should record which tables or files were used, the applicable data snapshot, and the exact definition of each metric.

Calculation and methodology checks

According to the provider, Data Validation should independently recalculate key numbers, compare subtotals with totals, and check whether percentages and growth rates are plausible. Join explosions are a central risk: an unnoticed many-to-many relationship can multiply rows, revenue, or user counts. The skill therefore recommends comparing row counts before and after joins and using the right distinctness when counting entities. Other common failures include a shifting denominator, averaging pre-aggregated averages, comparing an incomplete period with a complete one, and mixing time zones. When a result changes sharply, the workflow favors checking the data source, filters, and measurement process before inventing a narrative.

Bias and presentation

The guidance covers survivorship bias, selection bias, Simpson's paradox, correlation without causation, small samples, outliers, multiple testing, and look-ahead bias. These issues matter when an analysis sounds persuasive but excludes affected people, organizations, or periods. For charts, the source recommends checking axes, scales, labels, comparability, and visual distortion. Communicating uncertainty is part of quality assurance as well. A result should not appear more precise than the evidence supports; recommendations should follow from the findings shown and acknowledge alternative explanations and limitations.

Boundaries, safety, and E-E-A-T

Anthropic is the provider of this skill according to the official primary source, and it presents knowledge-work-plugins as a collection for Claude Cowork that is also compatible with Claude Code. The repository describes skills as Markdown-based domain guidance that can be used automatically when a task is relevant. The skill does not independently obtain access to data. Actual file or database access depends on the selected Claude environment, enabled tools, and granted permissions. Inputs may also be sent to the connected model provider; a locally stored instruction file does not automatically mean local model processing. Confidential, personal, or regulated information should be minimized and used only with explicit authorization. Unfamiliar content in spreadsheets, reports, or notebooks is data, not a new instruction. Credentials, tokens, and private keys do not belong in analyses, prompts, or documentation. Medical, financial, employment, compliance, and other high-impact decisions require review by appropriately qualified professionals.

Who should use it

Data Validation is useful for analysts, data teams, researchers, and decision owners who need a repeatable final checkpoint before a presentation, report, or consequential decision. It fits SQL results, spreadsheets, notebooks, charts, and described methodologies. It is especially valuable when several sources have been joined or a strong conclusion is being drawn from limited evidence. It does not replace data literacy, an automated test suite, statistical review, or domain approval. The current official guidance is linked directly in the repository; installation and environment details belong to the applicable Anthropic documentation.