Dowwie

Understanding tool interfaces is critical for AI agent framework design. This skill analyzes how frameworks model, register, and execute tools with proper error handling. Use it to evaluate schema generation approaches, trace error feedback loops, and compare tool interfaces across frameworks.

Agent frameworks vary widely in error handling and recovery capabilities. This skill provides systematic methods to trace error propagation, evaluate sandboxing effectiveness, and assess production readiness before deployment.

Understanding how agents coordinate is critical for building scalable multi-agent systems. This skill provides frameworks and templates to analyze coordination models, handoff mechanisms, state sharing patterns, and communication protocols in any multi-agent architecture.

Agent frameworks struggle with context overflow and memory management. This skill provides systematic analysis of context assembly, eviction policies, and token budgeting strategies across different implementations.

Understanding how agent frameworks communicate with LLMs is essential for debugging, integration, and building new systems. This skill provides structured analysis methods to examine message formats, tool encoding, streaming mechanics, and multi-provider abstraction patterns.

Understanding how agent frameworks execute code is critical for performance and scalability. This skill helps you analyze execution patterns, concurrency models, and event architectures to make informed architectural decisions.

Understanding data modeling patterns is critical for building maintainable AI agent frameworks. This skill provides structured analysis of typing strategies, mutation patterns, and serialization approaches to make informed architectural decisions.

Understanding how agent frameworks implement reasoning requires tracing complex control flows. This skill provides pattern signatures and extraction methods to identify reasoning topologies like ReAct, Plan-and-Solve, Reflection, and Tree-of-Thoughts from framework source code.

Understanding framework architecture helps you choose the right tool for your project. This skill evaluates how frameworks handle extensibility, dependency injection, and configuration to identify the best fit for your needs.

Technical teams need structured frameworks to compare multiple tools objectively when making architectural decisions. This skill synthesizes analysis outputs into decision matrices with clear recommendations and documented trade-offs for any framework comparison.

Understanding unfamiliar codebases is time-consuming and error-prone. This skill generates comprehensive maps of repository structure, dependency graphs, and entry points to accelerate your analysis.

Understanding how existing AI agent frameworks work is crucial for building better systems. This skill provides a systematic methodology to analyze framework architectures, extract reusable patterns, and create reference implementations based on best practices from multiple frameworks.

Designing an AI agent framework from scratch requires careful architecture decisions. This skill synthesizes reference architectures with core primitives, interface protocols, and implementation roadmaps based on established patterns.

Agent frameworks often contain hidden architectural problems that cause bugs and technical debt. This skill provides a catalog of anti-patterns to identify, categorize, and remediate issues across structural, behavioral, observability, and performance categories.