Skill UI

A comprehensive toolset for the full backend development lifecycle. This suite enables developers to scaffold API routes from OpenAPI specifications, manage complex database migrations and optimization (PostgreSQL), and perform rigorous load/performance testing. It supports building robust microservices, securing REST APIs, and ensuring system scalability.

This skill provides expertise in building production-grade, scalable data systems and pipelines. It covers the full spectrum of data engineering, including designing robust batch ETL/ELT workflows, implementing real-time streaming architectures (Kafka/Flink), and utilizing modern tools like Airflow, Spark, and dbt. Essential for data architecture design, data lakehouse implementation, and data governance.

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

Toolkit for prompt engineering experts to optimize prompts, evaluate LLM outputs, design agentic workflows, assess RAG quality, and curate few-shot examples across evaluation and orchestration tasks.

A comprehensive toolkit for professional security engineers. It covers the entire security lifecycle, including systematic threat modeling using STRIDE, designing secure architectures based on Zero Trust and Defense-in-Depth, conducting detailed vulnerability assessments (SAST/DAST), and implementing secure coding practices. Ideal for security audits, compliance reviews, and building resilient systems.

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

A specialized reverse-engineering skill designed to extract comprehensive functional and non-functional specifications from existing, undocumented, or legacy codebases. It operates by analyzing code structure, tracing data flows, and identifying observable behaviors. The output is structured using EARS (Easy Approach to Requirements Syntax), providing clearer requirements documentation, architectural diagrams, and dependency maps for onboarding or system modernization.

Scans codebases for debt, scores severity, tracks trends, produces prioritized remediation plans, and reports progress for engineering teams balancing refactoring with feature delivery.

This skill provides expertise in building robust, scalable, and intelligent test automation ecosystems. It covers AI-powered test generation, self-healing tests, Test-Driven Development (TDD), cross-platform automation (Web/Mobile/API), advanced CI/CD integration, and comprehensive quality engineering practices, ensuring high quality across the entire software development lifecycle.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.