Agents
A full-cycle tool for debugging and hardening AI agents
Install & Use
Copy this prompt and send it to your AI assistant (Claude / Cursor / TRAE / Codex / WorkBuddy etc.) to auto-install:
Help me install this AI Skill: Agents. It is used for: A full-cycle tool for debugging and hardening AI agents Full Skill content: https://321skill.com/skills/agents-x-9/raw/index.md Read that page and install it.
The prompt includes a link to the full Skill content. You can also view the full content.
This Skill addresses common issues in AI agent operations, such as loops, repeated calls, deviation from instructions, parameter hallucination, premature termination, or silent error swallowing. Traditional development often relies on framework-level APIs, which lack deep control over agent loops, tools, memory, context budgeting, costs, and upgrade rules. Independent of any framework, it is designed specifically for designing, debugging, evaluating, and hardening AI agents, enabling low-level intervention into agent behavior.
Usage is straightforward: simply configure the config.yaml and memory.md files locally under ~/Clawic/data/agents/. Upon startup, it automatically reads these configurations and memory indices, dynamically loading relevant files based on the ## Boxes index. You can then use natural language instructions to define new agents, rename or retire existing ones, add tools, record system prompts, run evaluations, measure cost and latency, and formulate upgrade strategies. At the end of each session, it automatically writes generated persistent results to the corresponding files, ensuring all decisions and observations are recorded.
It is ideal for teams and individuals building stable, reliable, and auditable AI agents—particularly agent developers, backend engineers, and DevOps professionals who have used frameworks like LangChain or LlamaIndex but found agent behavior difficult to control. Whether dealing with multi-agent coordination, tool-call optimization, context budget management, or cost and latency reduction, this Skill provides a standardized workflow.
We recommend introducing this tool early in a project to unify team development practices. Note that it primarily focuses on agent loop control and behavior debugging, and does not handle LangChain APIs, RAG retrieval, pure prompt engineering, or agent persona design. If your goal is to quickly build a chatbot, it may be better paired with other Skills. Before use, ensure the local directory structure is created according to configPaths.
Key Features
Unlike agents in frameworks such as LangChain or LlamaIndex, it does not rely on high-level APIs. Instead, it directly manipulates underlying elements like agent loops, tools, memory, and costs, enabling developers to debug and harden agent behavior at a fine-grained level, rather than merely providing pre-packaged Agent classes.
Limitations
Requires a local file system with configuration and memory files stored in a pre-defined directory structure per configPaths. It lacks cloud or stateless operation capabilities, depends on the fixed path under `~/Clawic/data/`, and cross-platform compatibility must be verified independently.
FAQ
Which AI frameworks does this Skill support?
It is framework-agnostic and works with agents built on any framework—such as LangChain, AutoGPT, or CrewAI—as well as purely custom agents.
How do I get started?
First, create `~/Clawic/data/agents/config.yaml` and `~/Clawic/data/agents/memory.md` locally. Then run `claw install agents` and follow the prompts.
Installation guide for AI assistants
If your AI coding assistant (Claude Code, Cursor, TRAE etc.) can see this page, send it this message to auto-install:
Visit https://321skill.com/skills/agents-x-9/raw/index.md to read the original Skill definition (Markdown format) for Agents, and install it according to the instructions.
Raw Markdown URL for AI: /skills/agents-x-9/raw/index.md