# Curation Standard

This repository is intended to be a GitHub-native research radar for agentic AI. The best entries should be easy to verify, cite, and reuse.

## Resource quality bar

Add resources that are useful to builders, researchers, or technical decision makers:

- papers, preprints, benchmark reports, and surveys
- code repositories, frameworks, SDKs, and tools
- official docs, project pages, demos, or leaderboards
- real-world implementations with enough technical detail to learn from

Avoid entries that are only promotional, duplicate an existing item, or lack enough source detail to evaluate.

## Link priority

Use this priority order:

1. Canonical paper/project/benchmark/code link.
2. Official docs or project page.
3. Commentary post that explains the resource.

LinkedIn and X links can be useful commentary, but they should not be the only link when an original source exists.

## Metadata to collect

When upgrading entries, try to capture:

- title
- domain
- type: paper, benchmark, framework, tool, survey, case study, dataset, or implementation
- canonical URL
- code URL, when available
- commentary URL, when available
- tags such as memory, planning, MCP, evaluation, security, robotics, healthcare, or deep research

## Domain placement

Choose the domain where the resource is most useful to the reader. If a resource fits multiple domains, prefer the most specific one and mention secondary themes in tags or notes.

## Thin domains

These domains currently need the most help:

- Education
- Robotics
- Supply Chain
- Planning
- Design
- Blockchain
- Transportation

