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Found 95 Skills
Use when validating data with Standard Schema-compatible schemas or handling ValidationError results.
QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.
Parseur et explicateur complet du format HPK (format de message propriétaire santé). Supporte plus de 100 types de messages couvrant l'administration des patients (ID, MV, CV), la chaîne logistique (PR, FO, MA, CO, LI, RO, FA), les stocks (SO, IM), la structure organisationnelle (ST, UT) et les opérations financières (RD, DD). Utilise @erp-pas/hpk-dictionary comme source de vérité. Valide la structure, extrait les champs, explique le contexte métier, mappe vers HL7 v2.5/IHE PAM et aide au dépannage des problèmes d'intégration.
Execute read-only SQL queries against Databricks. Use when you need to run a specific SQL query, aggregate data, join tables, or answer analytical questions about Databricks data.
Review football data code and visualisations for correctness. Use after building a chart, data pipeline, or analysis. Dispatches specialised reviewers for data correctness, chart conventions, visual inspection, and interactive edge cases.
Verify whether the analysis units, replication levels, statistical methods, and result reports in the research are consistent, and do not treat report review as re-analysis. Use when the user asks for "check statistical reports", "verify n and replicate experiments", "review statistical methods and results", or requests the rw-statistics-audit workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Use this skill when users need to create, generate, or validate Salesforce Lightning Report metadata. Trigger when users mention reports, creating reports, report metadata, .report-meta.xml files, tabular reports, summary reports, matrix reports, joined reports, report columns, report groupings, report filters, report charts, cross-filters, bucket fields, report formulas, or report time frame filters. Also use when users say things like 'create a report', 'generate a report', 'build a report on Accounts', 'add a chart to my report', or when they encounter deployment errors for .report-meta.xml files. Do NOT trigger for: creating or modifying Custom Report Type metadata (.reportType-meta.xml — use platform-custom-report-type-generate), creating dashboards, creating list views, running or viewing existing reports in the UI, or SOQL queries.
Use when preparing ANY app for App Store submission - enforces pre-flight checklist, rejection prevention, privacy compliance, and metadata completeness to prevent common App Store rejections
Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says "审查论文数据", "check paper claims", "verify numbers", "论文数字核对", or before submission to ensure paper-to-evidence fidelity.
Debug Scikit-learn issues systematically. Use when encountering model errors like NotFittedError, shape mismatches between train and test data, NaN/infinity value errors, pipeline configuration issues, convergence warnings from optimizers, cross-validation failures due to class imbalance, data leakage causing suspiciously high scores, or preprocessing errors with ColumnTransformer and feature alignment.
Multi-layer validation pattern - validates data at EVERY layer it passes through to make bugs structurally impossible, not just caught.