Claude Skills by a5c-ai

Zero-knowledge circuit development using Circom and Noir languages. Supports constraint optimization, ZK-friendly cryptographic primitives, proof generation (Groth16, PLONK), and Merkle tree implementations.

Performs statistical analysis for A/B testing experiments

Analyzes, validates, and optimizes Apache Airflow DAGs for reliability, performance, and best practices adherence.

Analyzes and optimizes Apache Spark jobs for performance, cost, and resource utilization

Analyze and decide between batch and stream processing architectures — latency requirements, cost modeling, and hybrid Lambda/Kappa patterns.

Generates semantic layer definitions for BI tools from dimensional models

Implements Change Data Capture patterns for real-time data integration

Analyzes and optimizes costs for cloud data platforms

Enriches data catalog entries with automated metadata

Extracts and maps data lineage from various sources including SQL, dbt, Airflow, and Spark, generating comprehensive lineage graphs for impact analysis.

Profiles data assets to assess quality dimensions, detect anomalies, and generate comprehensive data quality reports with actionable recommendations.

Analyzes dbt projects for best practices, performance, maintainability, and generates actionable recommendations for improvement.

Validates dimensional models against Kimball methodology best practices

Test ETL pipelines for data completeness, transformation accuracy, schema validation, and end-to-end data flow integrity.

Optimizes feature engineering pipelines and feature store configurations

Generates Great Expectations suites from data profiles and business rules

Selects and configures optimal incremental model strategies

Designs and optimizes Apache Kafka topics and configurations

Designs and optimizes One Big Table (OBT) patterns

Generates Slowly Changing Dimension implementations across platforms

Manages schema evolution and compatibility across data systems

Develop, optimize, and deploy Apache Spark jobs for large-scale batch processing, streaming, and machine learning workloads.

Analyzes and optimizes SQL queries across different data warehouse platforms (Snowflake, BigQuery, Redshift, Databricks) with platform-specific recommendations.

Designs optimal windowing strategies for stream processing

Alibi explainability skill for counterfactual explanations, anchors, and trust scores.

Arize AI skill for production ML monitoring, embedding drift, and performance analysis.

Dataset versioning skill using DVC for tracking data changes, managing data pipelines, and ensuring reproducibility.

Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting.

Feature store management skill for online/offline feature serving, feature registration, and training-serving consistency.

Data quality validation skill using Great Expectations for schema validation, expectation suites, data documentation, and automated data quality checks in ML pipelines.

Benchmark and optimize ML model inference — latency, throughput, memory usage, and hardware-specific performance profiling.

Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.

Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.

LIME-based local explanation skill for individual predictions across tabular, text, and image data.

MLflow integration skill for experiment tracking, model registry, and artifact management. Enables LLMs to log experiments, compare runs, manage model lifecycle, and retrieve artifacts through the MLflow API.

Model documentation skill for generating model cards following Google's model card framework.

Optuna integration skill for automated hyperparameter optimization with advanced search strategies, pruning, multi-objective optimization, and visualization capabilities.

Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations.

ML-specific testing skill using pytest with fixtures for data, models, and predictions.

PyTorch model training skill with custom training loops, gradient management, and GPU optimization.

Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.

Design and train reinforcement learning agents — environment setup, reward shaping, policy gradient methods, and evaluation.

Ensure ML experiment reproducibility — seed management, environment pinning, artifact versioning, and deterministic training validation.

Human-feedback-driven model optimization — preference data collection, reward modeling, policy updates, and alignment evaluation.

Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.

SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

Scikit-learn model training skill with cross-validation, hyperparameter tuning, pipeline construction, and model serialization. Enables automated ML model development using scikit-learn's comprehensive toolkit.

Apply statistical hypothesis testing, significance analysis, A/B test evaluation, and distribution comparisons for data science workflows.

TensorFlow/Keras model training skill with callbacks, distributed strategies, and TensorBoard integration.

Analyze and model time-series data — forecasting, anomaly detection, trend decomposition, and temporal feature engineering.