Agents

This Skill addresses the most challenging stability and efficiency issues in AI Agent development—behaviors such as an Agent repeatedly calling the same tool, deviating from original instructions, fabricating tool parameters, getting stuck mid-process, or silently swallowing errors. In practice, teams often spend significant time locating these 'phantom' issues without a systematic diagnostic approach. This tool covers all dimensions of an Agent, including loops, tools, memory, context budget, cost, and escalation, helping developers harden Agent behavior from the ground up.

Usage is intuitive: simply install this Skill on the Clawic platform, then describe your Agent's abnormal behavior in natural language (e.g., 'My Agent is repeatedly calling the search tool with increasing frequency'). It will automatically read local Agent configuration and memory files, analyze loop logs, tool invocation sequences, and context window usage, outputting root cause analysis and repair suggestions. You can also use it to design tool schemas, retry strategies, timeout mechanisms, checkpoints, and even write regression test suites.

It is ideal for teams or individuals building stable, controllable AI Agents, especially those who have experienced runtime Agent失控, exploding Token consumption, or topic drift after multi-turn conversations. Whether in single-Agent or multi-Agent collaboration scenarios, and whether during framework selection or post-deployment continuous optimization, this tool provides a structured evaluation and improvement path.

We recommend introducing this tool early in Agent development for health checks to avoid rework later. Note that it focuses on Agent runtime behavior and does not cover API wrappers for frameworks like LangChain, RAG pipelines, pure prompt engineering, or Agent personality settings. It requires local filesystem access to read Agent configuration and memory files, with all data stored locally only.