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Artifact Management

Community
aj-geddes
artifact-management

Manage build artifacts, Docker images, and package registries. Configure artifact repositories, versioning, and distribution strategies.

Overview

Publisheraj-geddes
Repositoryuseful-ai-prompts
Skill nameartifact-management
Stars
340
Forks
55
Bundled files
5
LicenseMIT
Links
  • 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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aj-geddes on GitHub. Read the source before you install it.

Installation

Install the Artifact Management 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.
  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/aj-geddes/useful-ai-prompts.git /tmp/useful-ai-prompts
mkdir -p .claude/skills
cp -r /tmp/useful-ai-prompts/skills/artifact-management .claude/skills/artifact-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Artifact Management 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 Management 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 Management 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 Management

Table of Contents

Overview

Implement comprehensive artifact management strategies for storing, versioning, and distributing built binaries, Docker images, and packages across environments.

When to Use

  • Docker image registry management
  • Package publication and versioning
  • Build artifact storage and retrieval
  • Container image optimization
  • Artifact retention policies
  • Multi-registry distribution
  • Dependency caching

Quick Start

Minimal working example:

dockerfile
# Dockerfile with multi-stage build for optimization
FROM node:18-alpine AS dependencies
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:18-alpine AS runtime
WORKDIR /app
COPY --from=dependencies /app/node_modules ./node_modules
COPY --from=builder /app/dist ./dist
COPY package*.json ./

EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s --start-period=40s --retries=3 \
  CMD node healthcheck.js

CMD ["node", "dist/server.js"]

// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
Docker Registry ConfigurationDocker Registry Configuration
GitHub Container Registry (GHCR) PushGitHub Container Registry (GHCR) Push
npm Package Publishingnpm Package Publishing, Artifact Retention Policy, Artifact Versioning, GitLab Package Registry

Best Practices

✅ DO

  • Use semantic versioning for artifacts
  • Implement image scanning before deployment
  • Set retention policies for old artifacts
  • Use multi-stage builds for Docker images
  • Sign and verify artifacts
  • Implement artifact immutability
  • Document artifact metadata
  • Use specific base image versions
  • Implement vulnerability scanning
  • Cache layers aggressively
  • Tag images with commit SHA
  • Compress artifacts for storage

❌ DON'T

  • Use latest tag as sole identifier
  • Store secrets in artifacts
  • Push artifacts without scanning
  • Use untrusted base images
  • Skip artifact verification
  • Overwrite published artifacts
  • Mix binary and source artifacts
  • Ignore image layer optimization
  • Store build logs with sensitive data

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Artifact Management AI skill do?

Manage build artifacts, Docker images, and package registries. Configure artifact repositories, versioning, and distribution strategies.

Why use Artifact Management on TypingMind?

Because you install it once and use it with any model. Artifact Management 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 Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aj-geddes/useful-ai-prompts/tree/main/skills/artifact-management. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Artifact Management?

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 Management?

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

Is the Artifact Management AI skill free?

Yes. It is published on GitHub by aj-geddes under the MIT 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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