Agents

This Skill addresses common loss-of-control issues in AI agent development and operation: infinite loops, repeated tool calls, deviation from instructions, hallucinated parameters, premature termination, or silent error swallowing. In practice, developers often spend significant time troubleshooting these behavioral anomalies without systematic diagnostic tools. It covers key dimensions such as loops, tools, memory, context budget, cost, and escalation, and is framework-agnostic, allowing you to focus on agent behavior itself rather than underlying implementations.

Usage is straightforward: simply describe the agent's abnormal behavior in natural language, e.g., "The agent starts fabricating parameters after 5 rounds of dialogue" or "Every tool call times out." You can then use commands to initiate diagnostics, read configuration files, analyze memory files, inspect tool definitions, and generate optimization suggestions. It also automatically reads project documentation, server inventories, and contact information to provide more precise context. All read/write operations are confined to the local filesystem, ensuring no data leakage.

It is ideal for teams or individuals building stable, controllable AI agents, especially developers who have used frameworks like LangChain or AutoGPT but encounter deep behavioral issues beyond the framework's scope. Test engineers can use it to write regression test suites, operations engineers can monitor agent token consumption and latency, and project managers can define agent responsibilities, escalation rules, and cost ceilings.

We recommend integrating this tool at the start of a new agent project to establish behavioral norms from the outset. Note that it primarily focuses on the agent behavior layer and is not suitable for LangChain API calls, RAG retrieval pipelines, pure prompt optimization, or agent role-setting. It relies on local filesystem configuration—ensure configuration paths are correct and have read/write permissions.