Skill UI

Provides comprehensive monitoring for the Clay data enrichment pipeline. It tracks critical operational metrics such as credit consumption velocity, enrichment success rates (hit rates), data quality scores, and lead distribution. Essential for cost control and ensuring pipeline health in data-intensive CRM workflows.

A comprehensive system for implementing policy guardrails in data enrichment pipelines. It enforces critical controls including credit spending limits, blocks the enrichment of sensitive PII (e.g., SSN, bank details), and validates input data quality. Essential for maintaining compliance (GDPR/CCPA) and managing costs when processing large volumes of personal and business data.

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

A comprehensive Python command-line toolkit for processing and visualizing high-throughput sequencing data including ChIP-seq, RNA-seq, and ATAC-seq. Performs quality control, normalization, sample comparison, and generates publication-quality genomic visualizations with bigWig conversion and heatmap profiles.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analysis, covering quality control, normalization, dimensionality reduction, clustering, and visualization. It supports converting R formats to h5ad and includes ready-to-run CLI scripts for end-to-end workflows, making it ideal for exploratory scRNA-seq analysis and publication-quality plotting.

Provide qualitative, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with local quality controls. Not for ranking people or high-stakes decisions.

This skill guides authors on submitting original, high-quality, and reusable Earth System datasets (observations, reanalysis, model outputs) as primary publications. It assesses whether the work fits the criteria of a 'data paper,' requiring open deposition in a FAIR repository with a persistent DOI, detailed provenance documentation, and rigorous quality control. It serves as a venue-selection and manuscript re-framing tool.

This skill provides a comprehensive advisory tool for authors targeting the journal 'Economic Review Journal' (《经济纵横》). It helps users assess the fit of their academic manuscript—covering macroeconomics, policy reform, and digital transition—by analyzing the journal's scope, required academic rigor, preferred structure, and common submission pitfalls. It functions as a pre-submission quality control mechanism.

A comprehensive guide for conducting and reporting rigorous experimental research in social and behavioral sciences. It covers various design types (vignette, lab, field), statistical requirements (effect sizes, power analysis), quality control measures (manipulation checks, confound control), and advanced techniques for establishing causality, ensuring high internal validity for academic publication.

Scanpy is a powerful, scalable Python toolkit built on AnnData for comprehensive single-cell RNA-seq data analysis. It supports the complete biological workflow, including quality control, normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker gene identification, and sophisticated cell type annotation, enabling the generation of publication-quality figures.