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This skill provides sophisticated project estimation by adapting to human-only, hybrid, or agent-first development modes. It uses research-backed PERT statistics and confidence bands (P50, P75, P90) to forecast development timelines for tasks, backlogs, and capacity planning. It continuously calibrates estimates based on actual completion data, ensuring accuracy in AI-assisted and hybrid development environments.

A guide to building robust projections and read models from continuous event streams. This pattern is essential for implementing CQRS read sides, creating materialized views, optimizing query performance, and building complex search indexes in event-sourced architectures. Use it when data needs to be transformed and aggregated from raw events into optimized query structures.

This skill enables authorized reverse engineering of custom binary protocols, Protobuf/gRPC schemas, and WebSocket frames using PCAP data. It helps recover undocumented wire protocols, decode structured traffic, and reconstruct state machines for security analysis and debugging purposes.

This skill provides comprehensive methodologies for capturing, analyzing, and documenting network protocols. It is crucial for security researchers, developers ensuring interoperability, and engineers debugging complex network communications by understanding the underlying data exchange mechanisms.

This skill generates Puppeteer scripts for browser automation, web scraping, and PDF creation. It supports headless Chrome, network interception, wait strategies, and cloud integration for testing. Ideal for automating browser interactions and extracting data efficiently.

A workflow for converting known binary vulnerabilities into working exploits. Covers stack/heap/kernel pwn techniques using pwntools, libc-database, and ROP. Suitable for porting CTF solutions to authorized remote targets or developing RCE payloads for Linux kernel drivers.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

A comprehensive guide for profiling, analyzing, and optimizing Python code. It addresses identifying performance bottlenecks related to CPU usage, excessive memory consumption, I/O operations, and slow data processing. Use this skill to improve application speed and efficiency in production environments.

Build financial models, backtest trading strategies, and analyze market data with risk metrics, portfolio optimization, and statistical arbitrage using pandas, numpy, and scipy.

Analyze weather radar and satellite observations as remote-sensing data: validate product metadata, geometry and calibration, derive storm and cloud structures, track their evolution, and quantify uncertainty for short-term nowcasting.

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.