A comprehensive diagnostic tool for reviewing, testing, and optimizing the structural integrity of complex agentic architectures and multi-agent systems.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-architecture-evaluator
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-architecture-evaluator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Architecture Evaluator?
The Agent Architecture Evaluator is designed to go beyond simple prompt engineering by analyzing the systemic structure of agentic workflows. It provides a deep-dive assessment into how planners, executors, routers, and memory systems interact within a larger framework. By leveraging this tool within the ecosystem of Openclaw Skills, developers can pinpoint architectural bottlenecks that lead to high latency, excessive costs, or reliability failures in production environments.
This skill is essential for teams moving from single-agent experiments to sophisticated multi-agent coordination. It evaluates critical dimensions like component clarity, routing correctness, and observability signals. Whether you are managing human-in-the-loop gates or complex tool-use layers, this skill helps ensure your system architecture is robust, scalable, and easy to debug.
Agent Architecture Evaluator Use Cases
- Assessing the structural health of an existing multi-agent system architecture.
- Troubleshooting reliability or latency issues that stem from routing and coordination logic.
- Generating a structured optimization roadmap and measurement plan for agentic workflows.
- Designing system-level test scenarios to validate recovery behavior and tool unavailability handling.
How Agent Architecture Evaluator Works
- Build an architecture inventory by capturing all components, responsibilities, and state boundaries.
- Map failure modes to identify where routers send work to the wrong specialists or where memory pollutes decisions.
- Design system tests that cover happy paths, degraded inputs, and tool-layer unavailability.
- Prioritize architectural changes by focusing on clarifying responsibilities and removing weak indirection.
- Define a measurement plan with concrete metrics such as task success rate, latency by stage, and human intervention frequency.
Agent Architecture Evaluator Setup
To get started with the Agent Architecture Evaluator, ensure you have the required reference frameworks and scripts available in your local environment. This skill integrates seamlessly with other Openclaw Skills to provide a full-stack evaluation.
# Verify required review templates and frameworks
ls assets/architecture-review-template.md
ls references/architecture-review-framework-v1.0.0.md
# Use the normalization script to process your architecture input
python scripts/render_architecture_review.py
Agent Architecture Evaluator Data Schema & Taxonomy
The skill produces a structured set of outputs to organize architectural data and insights:
| Artifact | Purpose |
|---|---|
architecture_inventory |
A detailed map of inputs, outputs, and memory boundaries. |
failure_mode_map |
A visualization of critical paths and high-risk handoff points. |
architecture_test_plan |
Defined scenarios for testing high-latency or partial failures. |
optimization_roadmap |
A prioritized list of structural changes for highest leverage. |
measurement_plan |
Metrics for tracking cost per task, retry rates, and efficiency. |
Agent Architecture Evaluator Advanced Features
- System-level failure mode mapping for multi-agent handoffs.
- Automated rendering of structured architecture reviews into developer-friendly Markdown.
- Detailed audit of memory usefulness and state isolation to prevent cross-contamination.
- Observability and debuggability analysis to improve system-level transparency.
- Integration with optimization patterns for tightening interface contracts and removing indirection.
SKILL.md
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