A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
This skill should be used for LLM-as-a-Judge evaluation techniques — direct scoring, pairwise comparison, rubric generation, reference-based grading, bias mitigation, calibration, and evaluator reliability measurement. Route deterministic evaluation checks and pipeline design to evaluation. Route agent harness design to harness-engineering.
This skill should be used for modeling agent cognitive states using formal Belief-Desire-Intention (BDI) ontology — transforming external RDF context into explicit mental states, designing deliberative reasoning pipelines, enabling explainable agent decisions, and implementing plan libraries. Route multi-agent coordination to multi-agent-patterns, memory architecture to memory-systems, and evaluation of agent reasoning to evaluation.
This skill should be used for designing and evaluating compression strategies for long-running agent sessions — hierarchical summarization, selective retention, compaction triggers, quality thresholds, and handoff summaries. Route foundational context concepts to context-fundamentals, failure diagnosis to context-degradation, and token-level efficiency tactics to context-optimization.
This skill should be used for diagnosing and mitigating context degradation — lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context. Route foundational conceptual work to context-fundamentals, token-efficiency tactics to context-optimization, and compression strategy design to context-compression.
This skill should be used to explain or reason about the foundational concepts of context engineering — what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills — debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summarization goes to context-compression, and project-shape decisions go to project-development.
This skill should be used for token-level efficiency tactics — observation masking, prefix caching, context partitioning, budget allocation, retrieval precision, format optimization, and trajectory-level token reduction. Route foundational concepts to context-fundamentals, failure diagnosis to context-degradation, compression strategies to context-compression, and filesystem offloading to filesystem-context.
This skill should be used for building deterministic evaluation frameworks for agent systems — assertion-based checks, regression testing, pass/fail criteria, metric design, and structured evaluation pipelines. Route LLM-as-judge techniques, rubric generation, and evaluator bias mitigation to advanced-evaluation. Route agent harness design to harness-engineering.