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Artifact Registry Manager

Organization
oracle
Artifact Registry Manager

Registers generated skill outputs in the project artifact index and maps each artifact to the skill that produced it.

Overview

Publisheroracle
RepositoryFusion-Setup-Configuration-Registry
Skill nameArtifact Registry Manager
Stars
112
Forks
26
Bundled files
Instructions only
LicenseUPL-1.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by oracle on GitHub. Read the source before you install it.

Installation

Install the Artifact Registry Manager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/core/project-mgmt/artifact-registry-manager
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oracle/Fusion-Setup-Configuration-Registry.git /tmp/Fusion-Setup-Configuration-Registry
mkdir -p .claude/skills
cp -r /tmp/Fusion-Setup-Configuration-Registry/core/project-mgmt/artifact-registry-manager .claude/skills/oracle-artifact-registry-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Artifact Registry Manager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Artifact Registry Manager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Artifact Registry Manager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Artifact Registry Manager

Purpose

Registers generated skill outputs in the project artifact index and maps each artifact to the skill that produced it.

When to Use

Use this skill after any skill writes an output artifact under project/{project_id}/artifacts, project/{project_id}/baselines, project/{project_id}/exports, project/{project_id}/decisions, or project/{project_id}/blueprints.

Inputs

  • artifact_metadata
  • skill_id
  • artifact_location

Outputs

  • artifact_record
  • updated_artifact_index

Implementation Notes

Every artifact record must include artifact type, producing skill, location, version, status, timestamps, and checksum when available.

Frequently asked questions

What does the Artifact Registry Manager AI skill do?

Registers generated skill outputs in the project artifact index and maps each artifact to the skill that produced it.

Why use Artifact Registry Manager on TypingMind?

Because you install it once and use it with any model. Artifact Registry Manager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Artifact Registry Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/core/project-mgmt/artifact-registry-manager. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Artifact Registry Manager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Artifact Registry Manager?

As many as you like. As long as a model supports skills, you can use Artifact Registry Manager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Artifact Registry Manager AI skill free?

Yes. It is published on GitHub by oracle under the UPL-1.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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