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Llm App Patterns

Checked
sebas-aikon-intelligence·vlatest·ai-ml·MIT

Helps with LLMs, patterns as part of building AI and machine learning applications workflows.

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 sebas-aikon-intelligence/antigravity-awesome-skills --skill llm-app-patterns -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

sebas-aikon-intelligence

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Install target

Live repository

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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

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  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • No pipe-to-shell installers: passed

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  • Pinned to a commit: passed

    Scanned sebas-aikon-intelligence/antigravity-awesome-skills at 0f4a1b2fd7.

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Commit
0f4a1b2fd7
Manifest
skills/llm-app-patterns/SKILL.md
Scanned
1 file, 723 lines
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About this skill

What it does

This skill provides patterns for structuring and interacting with Large Language Model (LLM) applications. It helps developers create more robust, predictable, and maintainable AI agents by offering pre-defined application architectures and interaction flows. The patterns cover common LLM app use cases, promoting best practices in design and implementation.

When to use it

  • Building a complex chatbot: Use the skill to structure conversations with clear turns and defined roles for the agent and user.
  • Creating an automated content generator: Apply patterns to ensure consistent output format and style across multiple generations.
  • Developing a data extraction tool: Leverage predefined flows to reliably extract specific information from unstructured text.
  • Designing a personalized recommendation engine: Utilize patterns to manage user context and tailor recommendations effectively.

Key capabilities

  • Pre-defined application architectures for LLM apps
  • Structured interaction flows
  • Best practice implementation guidance
  • Common use case coverage

Example prompts

  • "Generate a chatbot pattern with a greeting, question prompt, and farewell."
  • "Show me the data extraction pattern for extracting product names from online reviews."
  • "What's the recommended architecture for building an LLM-powered content summarization tool?"

Tips & gotchas

This skill is most effective when used by developers with some familiarity with LLMs and application design principles. While it provides a solid foundation, customization may be required to fit specific project needs.