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Llm Architect

Checked
omer-metin·vlatest·ai-ml·Apache-2.0

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add omer-metin/skills-for-antigravity --skill llm-architect -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

omer-metin

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned omer-metin/skills-for-antigravity at e8dcf4e873.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
e8dcf4e873
Manifest
skills/llm-architect/SKILL.md
Scanned
4 files, 1,255 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

The omer-metin-llm-architect skill assists in designing and implementing Large Language Model (LLM) architectures. It helps users define the components of an LLM system, including model selection, prompt engineering strategies, and integration with external tools or data sources. This skill facilitates building more complex AI agent workflows that leverage LLMs effectively.

When to use it

  • Designing a new AI Agent: Use this when you're starting from scratch and need help structuring the LLM components of your agent.
  • Optimizing Existing Workflows: If an existing workflow is underperforming, this skill can assist in re-architecting the LLM usage for better results.
  • Integrating External Data: When needing to connect an LLM with specific databases or APIs, this skill helps define the integration points and data flow.
  • Experimenting with Models: To quickly prototype different LLM configurations and compare their performance on a given task.

Key capabilities

  • Model Selection Guidance
  • Prompt Engineering Strategy Definition
  • Integration Design (External Tools/Data)
  • LLM System Architecture Planning

Example prompts

  • "I need to build an agent that summarizes legal documents. Suggest an LLM architecture."
  • "How can I integrate a vector database with my LLM workflow for semantic search?"
  • "What are some prompt engineering techniques I could use to improve the accuracy of my chatbot's responses?"

Tips & gotchas

The skill’s effectiveness depends on providing clear context about the desired agent functionality and any existing constraints. It is best used in conjunction with other skills focused on specific LLM tasks (e.g., prompt generation, data extraction).