Skill UI

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This skill simulates a world-class Senior System Design Engineer with 20+ years of experience at top tech companies. It helps users design, review, and scale complex, highly available, and resilient distributed systems. It covers every aspect from database sharding and microservices decomposition to caching strategies, observability stacks, and CI/CD pipelines, providing detailed blueprints and mathematical scaling estimates.

Execute arbitrary PySpark or pure Python code directly on Fabric Spark compute using the Livy API. This method creates ephemeral sessions without generating persistent notebook artifacts. It provides full read/write access to lakehouse Delta tables, making it ideal for agent-driven ETL pipelines, quick data transformations, or analyses where session cleanup is critical. Users must manage the session lifecycle (create, run, and delete).

A comprehensive tool for identifying poisoned training data, backdoored ML models, and supply-chain compromises. It leverages IBM's Adversarial Robustness Toolbox (ART) for activation clustering and spectral signature analysis, uses Cleanlab for label quality checks, and enforces integrity via weight-hash verification and safetensors. Essential for securing ML pipelines before training, deployment, or during supply chain audits.

This skill provides comprehensive techniques for security-testing Retrieval-Augmented Generation (RAG) pipelines. It probes for indirect prompt injection, which occurs when malicious instructions are embedded in retrieved documents, and tests for embedding-space manipulation. Using advanced tools like NVIDIA garak, Promptfoo, and PyRIT against vector stores (FAISS, Chroma, etc.), users can validate retrieval guardrails and secure CI/CD gates against adversarial inputs and data leakage.

This skill provides comprehensive guidance on designing complex system integrations, including event-driven architectures (EDA), webhook flows, API chaining, and robust ETL pipelines. It covers advanced patterns like Saga, Outbox, and resilience mechanisms (Idempotency, Circuit Breaker), making it ideal for connecting multiple microservices and managing asynchronous data flows.

A comprehensive utility for building reusable, command-line scripts and utilities to interact with the Hugging Face API. It enables complex data workflows by allowing users to chain, pipe, and process data fetched from models, datasets, and various endpoints. Ideal for automation, enrichment, and advanced data pipelines.

This skill automates the process of running Postman API collections using Newman CLI. It generates comprehensive scripts, including basic CLI commands, robust shell scripts, and multi-stage Jenkins pipelines. Ideal for integrating API tests into CI/CD workflows, executing data-driven tests, and ensuring reliable automation in any environment.

A sophisticated and print-friendly presentation template designed for explaining complex system architectures and data pipelines. Featuring a blueprint grid, cream background, and rust red accents, this deck is ideal for technical deep dives in engineering and system design.

Assemble scored competitor profiles into a decision-grade report featuring landscape maps, benchmarking matrices, and strategic recommendations. Ideal for presenting findings to leadership or boards, this skill structures competitive analysis pipelines to identify white-space opportunities and drive actionable business decisions based on auditable data.

Diagnose and accelerate large data movement pipelines by isolating bottlenecks, benchmarking variants, and codifying the fastest path with proof of correctness. Use when ingestion, backfill, ETL, or warehouse loading is too slow and must get faster without losing data integrity.

A comprehensive, multi-phase checklist for ensuring the quality, security, and deployment readiness of Django applications. It covers linting (ruff, mypy), unit testing (pytest), database migrations, security audits (bandit, pip-audit), and performance checks, making it an essential quality gate for CI/CD pipelines before merging or deploying to production.