swamp-report

Generate markdown and JSON reports from model and workflow executions.

Updated Apr 29, 2026

Search Tags:#data visualization #model analysis #report generation #reporting #workflow monitoring

Ready to run

npx skills add https://github.com/alvagante/agentspulse --skill swamp-report

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swamp-report
Source: https://github.com/alvagante/agentspulse/tree/main/.agents/skills/swamp-report
Command: npx skills add https://github.com/alvagante/agentspulse --skill swamp-report

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the creation, configuration, and viewing of reports for model and workflow executions, eliminating manual report generation and providing insightful data analysis.

Core Features & Use Cases

Automated Reporting: Generate markdown and JSON reports automatically from model and workflow executions.
Custom Report Extensions: Create custom report extensions for specific model or workflow requirements.
Data Persistence: Store and retrieve report outputs for future reference and analysis.
Use Case: For a machine learning engineer, automatically generate cost and performance reports for model executions to monitor and optimize model performance.

Quick Start

Run the report for the model 'my-model' with the command 'swamp model report my-model'.

Frequently Asked Questions about swamp-report

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate report generation for model and workflow executions?▼

Automate report generation by parsing model and workflow execution data to produce markdown and JSON outputs without manual input. This eliminates manual configuration and provides insightful data analysis for monitoring executions.

Can I generate machine learning model evaluation reports without manual input?▼

Yes, machine learning model evaluation reports are generated automatically by parsing execution data. This provides markdown and JSON outputs for cost and performance analysis to help monitor and optimize model performance.

What output formats are supported for workflow analysis and data visualization reports?▼

Workflow analysis and data visualization reports are outputted in both markdown and JSON formats. These outputs are stored and retrieved for future reference, ensuring data persistence throughout the reporting workflow.

Does automated reporting allow custom extensions for specific model requirements?▼

Yes, automated reporting supports custom report extensions tailored for specific model or workflow requirements. This allows you to configure and view specialized execution reports beyond the default generated outputs.

What is the best way to monitor model performance and cost over multiple executions?▼

The best way to monitor model performance and cost is to generate automated execution reports. By parsing workflow and model data, the system outputs structured markdown and JSON files for continuous analysis and optimization.