What problem does it solve? AI agents often fail because of poorly designed tool interfaces, opaque outputs, and uncontrolled context growth. This Skill provides a structured methodology for designing agent harnesses—action spaces, tool definitions, observation formats, and error recovery contracts—so agents complete tasks more reliably. ## Core Features & Use Cases - Action Space Design: Rules for stable tool naming, schema-first narrow inputs, deterministic outputs, and granularity guidance (micro, medium, macro tools). - Observation & Recovery Contracts: Standard response shapes with status, summary, next_actions, and artifacts, plus error contracts with root cause hints, safe retries, and stop conditions. - Architecture & Benchmarking Guidance: Compares ReAct, function-calling, and hybrid patterns, and defines metrics like completion rate, retries per task, pass@1/pass@3, and cost per successful task. - Use Case: When building a coding agent whose tool calls frequently fail or loop, apply this Skill to restructure tool schemas, add recovery hints to every error path, and set up completion-rate benchmarks. ## Quick Start Ask the agent to review your current tool definitions and redesign the action space and error recovery contract using the agent harness construction guidelines.