Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
5.7
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0
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AI & LLM
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The skill addresses an important AI domain (autonomous agents) with solid conceptual structure and relevant patterns (ReAct, Plan-Execute, Reflection). However, it suffers from severe truncation issues throughout - the description cuts off mid-sentence ('60% b'), philosophical statements are incomplete ('logging befor'), and the Sharp Edges table has placeholder 'Issue' entries instead of actual problems. The structure is clear and appropriate for the domain, listing patterns and anti-patterns effectively. Novelty is moderate - while autonomous agents are complex, the skill provides only high-level guidance without actionable implementation details, tool scripts, or concrete examples that would enable a CLI agent to actually build or deploy an agent system. To score higher, it needs: complete content, specific guardrail implementations, executable code examples, and detailed step-by-step procedures for implementing each pattern.
davila7
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