What problem does it solve? AI agents often fail tasks because of poorly designed tool definitions, opaque tool outputs, and missing error recovery guidance. This Skill provides a structured methodology for designing action spaces, observation formats, and context budgets so agents plan, call tools, and recover from errors more reliably. ## Core Features & Use Cases - Action Space Design: Rules for stable tool names, narrow schema-first inputs, deterministic output shapes, and micro/medium/macro tool granularity. - Observation & Recovery Contracts: Standard response fields (status, summary, next_actions, artifacts) plus root-cause hints, safe retries, and stop conditions for every error path. - Architecture & Benchmarking Guidance: When to use ReAct, function-calling, or hybrid patterns, and which metrics to track (completion rate, retries, pass@1/pass@3, cost per success). - Use Case: When building a coding agent whose tool calls keep failing, apply this Skill to restructure tool schemas and add recovery hints so the agent converges on completion instead of looping. ## Quick Start Use the agent-harness-construction skill to review my agent's tool definitions and improve its error recovery and observation formatting.