zelda-model-manager

Plan, train, evaluate, and deploy Zelda/ASM models with a unified lifecycle tool.

Updated Dec 30, 2025

Search Tags:#deployment #training #dataset #model-management #asm #zelda

Ready to run

npx skills add https://github.com/scawful/afs_scawful --skill zelda-model-manager

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: zelda-model-manager
Source: https://github.com/scawful/afs_scawful/tree/main/skills/zelda-model-manager
Command: npx skills add https://github.com/scawful/afs_scawful --skill zelda-model-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Zelda model management orchestrates datasets, training runs, evals, registry updates, and deployment artifacts for Zelda/ASM projects, reducing coordination friction across teams.

Core Features & Use Cases

Lifecycle orchestration: manage dataset inventory, training runs, evals, registry entries, and deployment artifacts for Zelda and ASM models.
QA and tooling integration: ties into model-mgr CLI, registry configs, and evaluation packs to ensure reproducibility and traceability.
Use Case: When planning a Zelda/ASM project, coordinate data, scripts, and deployments from data prep to model serving.

Quick Start

Plan, train, and deploy a Zelda/ASM model lifecycle from dataset assembly to deployment using the provided registry and conversion tools.

Frequently Asked Questions about zelda-model-manager

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

FAQPage Schema

How do I manage the full lifecycle of Zelda and ASM models from training to deployment?▼

To manage the Zelda and ASM model lifecycle, orchestrate dataset inventory, training runs, evaluations, registry updates, and deployment artifacts using a unified tool that integrates QA checks and model-mgr tooling.

How do I convert and deploy GGUF or MLX artifacts for local model serving?▼

You can convert and deploy GGUF or MLX artifacts using a local registry and model-mgr tooling, which handles the conversion process and serves the artifacts for Zelda and ASM model deployment.

What is the best way to coordinate datasets, training scripts, and deployment for ASM projects?▼

The best way to coordinate ASM projects is through lifecycle orchestration that manages data prep, training runs, evaluations, and model serving, reducing coordination friction across teams.

Do I need a base model selection workflow before training Zelda models?▼

Yes, a base model selection workflow is required. The lifecycle process mandates selecting a base model, running training, and performing QA checks before updating the registry or deploying artifacts.

Can I use model-mgr CLI to ensure reproducibility for ASM training runs and evaluations?▼

Yes, the model-mgr CLI ties into registry configs and evaluation packs to ensure reproducibility and traceability throughout the ASM model training and evaluation lifecycle.

Why does Zelda model deployment require QA checks and registry updates?▼

Zelda model deployment requires QA checks and registry updates to maintain reproducibility and traceability, ensuring that only evaluated and verified models are served as deployment artifacts.