Optimizes AI agent tool definitions, observation formats, and action spaces to maximize task completion rates.
This skill provides a comprehensive framework for architecting robust AI agents within the Claude environment. It focuses on the 'harness'—the interface between the LLM and its environment—offering specific guidance on designing stable tool schemas, managing context budgets, and implementing deterministic error recovery protocols. By standardizing how agents observe their surroundings and plan their next steps, it significantly reduces hallucination and improves success rates for complex, multi-step engineering tasks.
主要功能
01Optimized action space design with precise tool granularity rules.
02Architectural guidance for ReAct, Function Calling, and Hybrid patterns.
03Standardized observation formats for consistent environment feedback.
040 GitHub stars
05Deterministic error recovery contracts to prevent agent loops.
06Context budget management to minimize token overhead and noise.
使用场景
01Building high-reliability autonomous agents for code refactoring and migration.
02Improving the recovery rate of agents performing high-risk infrastructure tasks.
03Standardizing tool definitions across multi-agent developer ecosystems.
