Agents

This Skill addresses the most challenging stability and performance issues in AI Agent development. In practice, Agents often fall into infinite loops, repeatedly call the same tool, drift from original instructions after multiple turns, fabricate tool parameters, halt prematurely, or silently swallow tool errors. It also assists in making decisions between single-Agent and multi-Agent architectures, as well as selecting among different frameworks. Furthermore, it covers Token cost optimization, p95 latency reduction, tool schema design, retry and timeout strategies, checkpoint configuration, human approval workflows, the creation of evaluation sets and regression test suites, and risk mitigation for prompt injection, tool abuse, and excessive permissions.

Usage is straightforward: simply place the necessary configuration files (e.g., ~/Clawic/data/agents/config.yaml and memory.md) in your project directory according to the convention, then describe your Agent's issue to the AI using natural language instructions. For example, tell the AI, "My Agent is repeatedly calling the same tool, please diagnose." It will automatically read the configuration and memory files, analyze loops, tool calls, context budgets, and costs, then provide optimization suggestions, modify the system prompt, or adjust tool schemas. At the end of the session, it writes any persistent outcomes (such as newly defined Agents, system prompts, evaluation results, cost measurements, etc.) to the designated memory file, ensuring continuity for the next session.

It is ideal for teams or individuals requiring deep customization of AI Agent behavior, especially developers already using multiple Agents but lacking unified debugging and governance tools. Whether you're an independent developer debugging a single Agent or a team managing the collaboration of dozens of Agents, this Skill provides end-to-end support from memory design and multi-Agent handoffs to traceability and production deployment. It is also suitable for projects that need strict control over Token costs and response latency, or require writing regression tests and evaluation sets for Agents.

It is recommended to introduce this Skill at the start of a new Agent project to establish robust memory management and cost control mechanisms from the outset. Note that it primarily operates in a local filesystem environment, relying on the directory structure under ~/Clawic/data/, and is therefore not suitable for purely cloud-based or database storage scenarios. It does not cover LangChain APIs, RAG pipelines, pure prompt engineering, or Agent role voiceovers. If your needs are limited to these areas, you may seek other specialized Skills.