Skills for managing LLM context windows, attention optimization, and token budgets for effective agent systems
9 skills available
muratcankoylan•Context Engineering
Methods for evaluating agent performance including LLM-as-Judge patterns, metrics design, and benchmarking
muratcankoylan•Context Engineering
Designing memory systems for agents including working memory, long-term storage, and retrieval strategies
muratcankoylan•Context Engineering
Architectural patterns for multi-agent systems including orchestration, delegation, and context sharing strategies
muratcankoylan•Context Engineering
Understanding and mitigating context degradation, including lost-in-the-middle phenomenon and attention curve analysis
muratcankoylan•Context Engineering
Foundational understanding of context window management, attention mechanics, and the 'lost-in-the-middle' phenomenon
muratcankoylan•Context Engineering
Belief-Desire-Intention cognitive architecture for building rational agent systems with formal mental state modeling
muratcankoylan•Context Engineering
Ongoing optimization of agent contexts including pruning, prioritization, and dynamic context management
muratcankoylan•Context Engineering
Best practices for designing agent tools including schema optimization, error handling, and output formatting
muratcankoylan•Context Engineering
Techniques for compressing context while preserving essential information including summarization and semantic deduplication
