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Getting Started: Loading Your First Agent Pack

Welcome to AgentHub! In this quick tutorial, you’ll learn how to find, load, and benefit from your first AgentHub pack—all in just a few minutes. No plugins, no custom tools—just copy, paste, and give your LLM sessions better guidance.

Step 1: Browse the AgentHub Registry​

Go to the AgentHub packs directory on GitHub.

  • Packs are organized by tool or API (for example, react or github-api).
  • Click on a tool you want to work with.

Step 2: Open and Copy the Canonical Markdown Pack​

  • Inside each tool’s folder, you’ll find a versioned Markdown file (for example, 0.4.0.md).
  • Click on the Markdown file to view its contents.
  • Copy the full contents to your clipboard.

Step 3: Load the Pack Into Your LLM​

How you do this depends on your LLM stack:

  • ChatGPT, Claude, Perplexity, etc.:

    • Start a new chat.
    • Paste the canonical Markdown pack as your first message or system prompt.
    • Continue with your development questions and requests.

  • Ollama, LM Studio, LocalAI, etc.:

    • Use the “system prompt” or “context” field in your interface.
    • Paste the canonical Markdown pack before your instructions.

  • Custom LLM/agentic tools:

    • Supply the pack as part of your context window or prompt assembly step.

Step 4: Start Your Session!​

With the pack loaded, your LLM will now:

  • Apply best practices for the tool or API
  • Avoid common pitfalls and anti-patterns
  • Produce code, explanations, or integrations that reflect real community expertise

Example Prompt:

(system prompt): [paste canonical Markdown pack here]
(user prompt): Generate a React component for a signup form with email validation.

Pro Tips​

  • Stack packs: You can load multiple packs if you’re using more than one tool (e.g., React + Redux).
  • Customize: Feel free to edit or annotate the Markdown pack for your specific needs.
  • Check for updates: The AgentHub registry is growing—come back for new and improved packs.

FAQ​

Q: Do I need any special software? A: No! If your LLM supports “system prompts” or context injection, you’re good to go.

Q: Will this work with proprietary models as well as open-source? A: Yes—any LLM that can accept pasted instructions or context can benefit from AgentHub files.

Q: How do I know what’s in the Markdown file? A: Each file is human-readable and reviewed. Read the comments, rationale, and best practices inside!

Q: What if I find a mistake or want to improve a pack? A: Contribute back via Pull Request—community improvements are welcome!

Next Steps​

  • Try your first pack now!
  • Browse for packs covering other tools or APIs you use.
  • Want to help others? Write or improve a pack for your favorite stack.

AgentHub makes every LLM session smarter. Welcome aboard, and happy pack-crafting! 🌱