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ir:agent-landscape

Track and rank coding agents by GitHub stars and momentum.

93|10|Updated Sep 5, 2025
Search Tags:#open-source#ranking#ai-agents#landscape-report#agent-landscape#github-stars
One-click install
npx skills add https://github.com/ingo-eichhorst/Irrlicht --skill ir-agent-landscape
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ir:agent-landscape
Source: https://github.com/ingo-eichhorst/Irrlicht/tree/main/.claude/skills/ir%3Aagent-landscape
Command: npx skills add https://github.com/ingo-eichhorst/Irrlicht --skill ir-agent-landscape

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Identify and monitor the competitive landscape of AI coding agents and orchestrators by aggregating data, tracking popularity, and publishing up-to-date landscape reports.

Core Features & Use Cases

  • Tracks GitHub stars and momentum for coding agents and orchestrators.
  • Generates a published landscape report and a detailed comparison index for site integration.
  • Useful for engineering teams evaluating tooling options and for product teams benchmarking market players.

Quick Start

Run the landscape generator to produce the latest HTML report for Irrlicht's agent landscape.

Frequently Asked Questions about ir:agent-landscape

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

FAQPage Schema
How do I track GitHub stars and momentum for AI coding agents?▼

You track AI coding agents by maintaining data in references/agent-data.json and running the generator to rank agents by popularity and momentum. This allows you to monitor competitive ecosystems and compare tooling options.

What is an AI agent landscape report and how does it work?▼

An AI agent landscape report audits agent ecosystems by aggregating popularity data and comparing live versus planned integrations. It uses a Python generator to process agent data and output a detailed HTML comparison index for site integration.

How do I generate an HTML landscape report for coding agents and orchestrators?▼

You generate an HTML landscape report by running the landscape generator located in assets/generate.py. The generator reads your tracked agent data and publishes the HTML output directly to the site/landscape/ directory.

Can I benchmark coding agent market players using GitHub stars?▼

Yes, you can benchmark coding agent market players by tracking their GitHub stars and momentum. The generator ranks these coding agents and orchestrators to help product teams evaluate open-source tooling options.

Do I need to manually maintain agent data to compare live vs planned integrations?▼

Yes, you must manually maintain your agent data in the references/agent-data.json file. The landscape generator relies on this data source to audit agent ecosystems and compare live versus planned integrations accurately.

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