cncf-landscape

Query the CNCF Landscape API to filter cloud-native projects by maturity and license.

40|6|Updated Jul 11, 2026

Search Tags:#architecture #discovery #evaluation #kubernetes #landscape #cncf

Ready to run

npx skills add https://github.com/magnus919/agent-skills --skill cncf-landscape

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cncf-landscape
Source: https://github.com/magnus919/agent-skills/tree/main/cncf-landscape
Command: npx skills add https://github.com/magnus919/agent-skills --skill cncf-landscape

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of navigating the vast CNCF ecosystem by providing a disciplined, evidence-based approach to discovering and comparing cloud-native technologies for architecture decisions.

Core Features & Use Cases

Live API Discovery: Query the official CNCF Landscape API to find projects based on category, maturity, license, and repository signals.
Evidence-Based Comparison: Use a structured framework to distinguish between observed facts, inferred ecosystem reach, and unknown risks.
Use Case: When tasked with selecting a distributed tracing tool, use this skill to filter candidates by maturity and license, then generate a shortlist with trade-offs and a validation plan for a proof of concept.

Quick Start

Use the cncf-landscape skill to search for graduated observability projects that support tracing and have an open source license.

Frequently Asked Questions about cncf-landscape

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

FAQPage Schema

How do I evaluate cloud-native technologies for architecture decisions?▼

To evaluate cloud-native technologies, query the CNCF Landscape API to filter projects by maturity, license, and repository signals. This provides an evidence-based framework to compare operational fit and distinguish observed facts from inferred ecosystem risks.

What is the best way to filter CNCF landscape projects by maturity and license?▼

Filtering CNCF landscape projects by maturity and license is done by querying the official CNCF Landscape API endpoints. This structured discovery process narrows down candidates to generate a shortlist of viable cloud-native technologies for architecture comparison.

How do I compare open source observability and tracing tools for Kubernetes?▼

Comparing open source observability and tracing tools for Kubernetes involves querying the CNCF Landscape API to filter graduated projects. You then apply a structured framework to evaluate repository evidence, maturity levels, and operational fit for your specific architecture.

Do I need network access and Python to discover cloud-native projects via the CNCF Landscape API?▼

Yes, discovering cloud-native projects via the CNCF Landscape API requires network access to the public endpoints and standard Python 3.8 or higher. These prerequisites enable live API queries to filter projects by technical and governance criteria.

Can I distinguish between observed facts and unknown risks when selecting cloud-native technologies?▼

You can distinguish between observed facts and unknown risks when selecting cloud-native technologies by using an evidence-based comparison framework. This structured approach separates verified repository signals from inferred ecosystem reach during architecture evaluation.

When should I use a structured framework for cloud-native project discovery?▼

Use a structured framework for cloud-native project discovery when navigating the vast CNCF ecosystem for architecture decisions. It enforces an evidence-based approach to filter candidates, compare trade-offs, and generate a validation plan for a proof of concept.