An automated system for generating standardized, professional evaluation reports for AI agents and intelligent systems based on raw test data.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-evaluation-report
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-evaluation-report 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 Evaluation Report?
The Agent Evaluation Report skill is a specialized tool within the Openclaw Skills ecosystem designed to streamline the quality assurance lifecycle for AI agents. It transforms raw testing metrics into comprehensive, high-level documentation suitable for stakeholders and technical teams alike. By automating the report generation process, it ensures consistency across project evaluations and saves developers significant time manual formatting.
This skill is particularly powerful for teams building complex AI middleware or LLM-based applications where performance, security, and functional accuracy must be rigorously documented. It integrates directly with document management systems to provide a centralized repository for all AI system assessments.
Agent Evaluation Report Use Cases
- Generating a standardized test report after a new AI agent deployment.
- Creating a comprehensive project evaluation for stakeholder review.
- Documenting performance benchmarks and latency metrics for AI systems.
- Automating risk assessment and defect analysis reports during the QA phase.
How Agent Evaluation Report Works
- The user triggers the skill using natural language commands like generate intelligent system evaluation report.
- The agent prompts for or extracts key project data, including project name, test cycles, and versioning info.
- Raw metrics regarding functional pass rates, performance response times (e.g., average first-token latency), and security audits are processed.
- The skill organizes this data into a structured nine-section template covering everything from executive summaries to technical appendices.
- The final report is formatted as Markdown or Word and can be automatically synced to Feishu/Lark document nodes or local output directories.
Agent Evaluation Report Setup
To utilize this skill within your environment, ensure you have the necessary Feishu API permissions and local directory structure.
# Create the local output directory for reports
mkdir -p output/effect-reports/
# Ensure Openclaw Skills have access to your Feishu Knowledge Base
# Knowledge Base ID: 7616288931050507220
Agent Evaluation Report Data Schema & Taxonomy
The skill generates reports using a standardized schema to ensure data integrity across different Openclaw Skills evaluations.
| Section | Content Details |
|---|---|
| Overview | Project name, test cycle dates, and executive summary. |
| Environment | Hardware specs, software versions, and port configurations. |
| Metrics | Functional pass rates (%), response times (ms), and concurrency data. |
| Defects | Categorized list of bugs with severity, status, and fix plans. |
| Risk | Technical risk items, impact levels, and mitigation strategies. |
Agent Evaluation Report Advanced Features
- Multi-platform export support for both Markdown and Word formats.
- Integrated Feishu/Lark automation for document creation, fetching, and updating.
- Support for granular AI-specific metrics like first-token response time and system availability percentages.
- Automatic file naming conventions using {ProjectName}Evaluation{Date} for better version control.
SKILL.md
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