Results for “statistical-validity”

Guía para elegir y aplicar tests de hipótesis con SciPy, verificando supuestos, interpretando p-values y tamaño del efecto, y evitando falsos positivos.

Define and enforce data schemas and quality checks using pandera, Great Expectations, or manual assertions, with clear error handling and documentation.

Verifica la reproducibilidad y consistencia de un análisis de datos antes de presentarlo, validando contra la pregunta original y documentando limitaciones.

Compute the Kruskal-Wallis H-test using scipy.stats.kruskal for independent samples, returning the H statistic and p-value.

Guides statistical hypothesis testing with assumption checks, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting for research data.

Validates Claude Ads contracts, bundles, scores, runs, capabilities, repositories, and releases with deterministic checks and recovery steps.

Temporal Data

Find sources for every numerical claim in the draft, add inline hyperlinks, and add internal links to brand-reference articles. Triggered after /draft.

SNLI-VE: Visual Entailment Dataset

A validation *boundary* is any point where data crosses from a system you do not

Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Use when the user requests fact checking or provides relevant inputs for this workflow.

Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files. Use when working with .xlsx, .xlsm, .xls, .ods, .csv, or .tsv files; answering questions from a workbook; auditing formulas or data quality; comparing sheets or versions; producing pivots, charts, forecasts, or summary workbooks; repairing malformed tables; or validating that spreadsheet edits and calculations are accurate.

Validates generated SRS documents against IEEE 830-1998, fixing errors and reporting warnings.

Applies quantitative and qualitative analysis techniques, interprets experimental data, validates procedures, and selects appropriate methods with uncertainty quantification.

Centralized JSON validation for AGENT_SUCCESS_CRITERIA with defensive parsing and injection attack prevention (CVSS 8.2)

QA a completed analysis for methodology, accuracy, and bias before it is shared, shipped, or acted on.

Use when incorporating information from research, references, or external sources. This skill provides verification procedures to ensure information used in deliverables is accurate, current, and from authoritative sources, preventing hallucination and misinformation.

Botany Based Validation Skill

Algebra Based Validation Skill

Econometrics skill for descriptive statistics and summary tables. Activates when the user asks about: "descriptive statistics", "summary statistics", "summary table", "Table 1", "balance table", "means and standard deviations", "correlation matrix", "data summary", "sample characteristics", "variable distributions", "描述性统计", "描述统计", "汇总统计", "统计表", "均值标准差", "平衡性检验", "相关矩阵", "样本特征", "变量分布"

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.

Validate process JS files for correct SDK patterns, task definitions, syntax, and quality gate implementation.

Analyzes test suites to measure assertion diversity, detect shallow or trivial assertions, and identify tests that lack meaningful verification.

Validates data quality with completeness, consistency, accuracy checks and generates quality reports

Cross-validation only estimates generalization if the split mimics the gap between

Validates generated SRS documents against IEEE 830-1998, fixes errors, and reports compliance verdict.

E1-Quantitative Analysis Guide with Code Generation & Sensitivity Analysis VS-Enhanced with Full 5-Phase process: Avoids obvious analyses, explores innovative methodologies Expanded to include qualitative analysis (thematic, grounded theory, content, narrative) Absorbed E4 (Analysis Code Generator) and E5 (Sensitivity Analysis - Primary Study) capabilities Use when: selecting statistical/qualitative methods, interpreting results, checking assumptions, generating code, sensitivity analysis Triggers: statistical analysis, ANOVA, regression, t-test, power analysis, assumption checking, effect size, thematic analysis, grounded theory, content analysis, narrative analysis, NVivo, ATLAS.ti, coding, qualitative data, R code, Python code, SPSS syntax, sensitivity analysis, robustness check

Validate user claims against codebase and git history, trace to root cause, and propose a fix

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.

Verifies data structures, files, YAML blocks, or agent output against their defined schema, producing a pass/fail report with actionable fix instructions.

Validate financial data quality in market analysis documents before publication, checking price scales, instrument notation, date accuracy, allocation totals, and unit usage.

Validate backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to ensure backups are reliable for disaster recovery and ransomware response scenarios.

Verify the article’s factual, product, citation, and link integrity before visual production.

NLVR2: A Visual Reasoning Benchmark for Natural Language