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ACTIVE WORKSPACEHome

Catalog purpose, outputs, trust boundaries, and discovery workflow.

LOCAL-FIRST EXPERIENCE | CLOUDFLARE PAGES READYREVIEW AUDIO READY

Organize datasets and models for trustworthy discovery. dataset metadata|

A searchable research catalog for comparing datasets, models, notebooks, APIs, and templates by purpose, quality, license, and readiness.

Original template
user@metricforge: ~/commerce-ai-dataset-catalog
12catalog assets4license classes3export formatsLocalshortlist
01 | CLEAR PRODUCT DEFINITION

Useful by design, honest about scope.

Create a searchable metadata layer that helps humans compare research assets and generates concise machine-readable discovery exports.

Primary use

University data catalogs, model registries, research directories, and AI-readable product documentation.

A searchable research catalog for comparing datasets, models, notebooks, APIs, and templates by purpose, quality, license, and readiness.

GitHub repository ↗
Produces

Concrete, reusable outputs

Dataset and model cards, quality and license filters, shortlists, CSV/JSON exports, and llms.txt.

Boundary

No exaggerated capability claims

The catalog organizes metadata and links. It does not verify legal rights, process payments, host large artifacts, or guarantee model suitability.

02 | WORKFLOW

One focused path from input to output.

Every stage is separated so the interface stays readable, reviewable, and useful for AI and Data Science learning.

01

Describe

Record asset type, task, provenance, license, quality, and usage notes.

02

Filter

Search by purpose, type, quality, and licensing constraints.

03

Shortlist

Create a focused research set for evaluation or documentation.

04

Export

Generate JSON, CSV, and llms.txt summaries for downstream use.