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Clay Pitfalls: Avoiding Data Enrichment Mistakes

clay-known-pitfalls

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide identifies and explains common anti-patterns, gotchas, and integration mistakes when using Clay's data enrichment platform. It details the root causes of credit waste, workflow failures, and data loss, providing actionable fixes for auditing configurations, onboarding new team members, or optimizing existing pipelines.

Compression Pipeline Optimizer

compression-optimizer

jeremylongshore/claude-code-plugins-plus-skills

Guides users through compression optimizer scenarios within data pipelines, offering best practices, code templates, and validation steps for ETL, transformation, and streaming workflows.

Cross Validation Setup

cross-validation-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates cross validation setup for ML training requests, offering best-practice guidance, production-ready code samples, and validation steps across data preparation, model tuning, and experiment tracking.

Data Augmentation Pipeline

data-augmentation-pipeline

jeremylongshore/claude-code-plugins-plus-skills

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

Data-Driven Feature Development

data-engineering-data-driven-feature

sickn33/antigravity-awesome-skills

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

Data Lineage Tracker

data-lineage-tracker

jeremylongshore/claude-code-plugins-plus-skills

Offers automated guidance for implementing data lineage trackers across ETL and streaming pipelines, including best practices on transformations, orchestration, and validation for production environments.

Data Quality Validation Frameworks

data-quality-frameworks

sickn33/antigravity-awesome-skills

This skill provides production patterns for implementing robust data quality validation using Great Expectations, dbt tests, and formal data contracts. It is essential for building reliable, automated, and verifiable data pipelines, ensuring data integrity throughout the entire ETL/ELT process, and establishing clear data governance rules.

Databricks Incident Response Runbook

databricks-incident-runbook

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

Data Transformation Patterns Using dbt

dbt-transformation-patterns

sickn33/antigravity-awesome-skills

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

Early Stopping Callback

early-stopping-callback

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Feature Importance Analyzer

feature-importance-analyzer

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

Website Scraping and Crawling Pipeline

firecrawl-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

This workflow provides the primary mechanism for converting complex, live websites into clean, LLM-ready markdown content. It supports single-page scraping using `scrapeUrl`, deep multi-page site crawling with `crawlUrl`, and scalable async crawling for large datasets. Ideal for building robust content ingestion pipelines and reliably extracting structured information from the web.

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