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Ab Test Analyzer

ab-test-analyzer

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for ab test analyzer workflows, covering SQL queries, visualization, statistical analysis, and BI validation so teams can run reliable experiments within data analytics contexts.

Chaos Engineering Playbook

Jeffallan/claude-skills

Designs and executes chaos experiments, failure-injection frameworks, and game days for distributed systems, providing runbooks, manifests, rollback plans, and continuous resilience automation in CI/CD.

Chaos Engineering Experimentation

conducting-chaos-engineering

jeremylongshore/claude-code-plugins-plus-skills

Guides chaos engineering experiments by defining objectives, choosing tools such as Chaos Mesh, Gremlin, Toxiproxy, or AWS FIS, injecting failures, monitoring metrics, and recommending fixes to harden services and APIs.

Databricks ML Workflow: Training and Serving

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

Systematic Debugging Strategies

debugging-strategies

sickn33/antigravity-awesome-skills

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

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.

Langfuse LLM Evaluation And Benchmarking

langfuse-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

Modular Fuzzing Library

trailofbits/skills

LibAFL is a modular fuzzing library in Rust that exposes advanced mutators, feedback, objectives, and schedulers so you can build bespoke fuzzers or use it as a drop-in replacement for libFuzzer when targeting non-standard architectures or research experiments.

MLflow Lifecycle Platform

Orchestra-Research/AI-Research-SKILLs

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Model Checkpoint Manager

model-checkpoint-manager

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for managing model checkpoints in ML training workflows, offering best practices, sample code, and validation to keep experiments stable and reproducible.

Model Explainability Assistant

model-explainability-tool

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for model explainability tooling within ML training workflows, giving step-by-step best practices, production-ready code, and validation for experiments whenever users mention explainability tool needs.

PostHog Feature Flags And A/B Testing

posthog-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Provides a comprehensive workflow for managing feature flags, running A/B/multivariate experiments, and analyzing user cohorts using PostHog. The guide covers implementing flag evaluation both client-side (browser) and server-side (Node.js), and demonstrates how to programmatically set up, track, and query experiment results via API calls (cURL/Bash).

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