Agents

This Skill addresses common pain points in AI Agent development: infinite loops, repeated calls to the same tool, deviation from original instructions after multi-turn conversations, hallucinated tool parameters, premature task termination, or silent swallowing of tool errors. It is independent of any framework, focusing on debugging, evaluating, and hardening the Agent's core loop, helping developers quickly pinpoint the root cause of behavioral anomalies.

Usage is straightforward. Simply configure the config.yaml and memory.md files in your local ~/Clawic/data/agents/ directory, then describe the issue in natural language. For example, you can say, "My Agent keeps repeatedly calling the same search tool, help me check it." It will automatically analyze tool call history, context budget, memory state, and provide specific remediation suggestions. It can also be used for designing tool schemas, retry mechanisms, timeout settings, checkpoints, human-in-the-loop approval workflows, and for writing evaluation sets and regression test suites.

It is ideal for teams or individuals developing complex AI Agents, especially those who have used frameworks like LangChain or AutoGPT but encountered difficult-to-debug issues. It is also suitable for project managers to evaluate per-task costs and p95 latency for Agents, establishing upgrade rules and cost ceilings. For scenarios requiring multi-Agent coordination, memory design, tracing, and canary releases, this Skill provides systematic methodological support.

We recommend introducing this tool early in the development cycle to centrally manage Agent behavior specifications and cost control. Note that it primarily handles the Agent's core loop logic and does not cover LangChain APIs, RAG retrieval pipelines, pure prompt engineering, or Agent personality settings. Before use, ensure the corresponding configuration files exist locally under ~/Clawic/data/ and adhere to the file's rules regarding local data storage and read/write permissions for shared boxes.