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Llm Application Dev Ai Assistant

Checked
rmyndharis·vlatest·ai-ml·MIT

Helps with LLMs, AI 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 rmyndharis/antigravity-skills --skill llm-application-dev-ai-assistant -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

rmyndharis

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 rmyndharis/antigravity-skills at 3eff4af253.

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

Commit
3eff4af253
Manifest
skills/llm-application-dev-ai-assistant/SKILL.md
Scanned
2 files, 1,253 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.
What each check looks for →

About this skill

What it does

This skill assists in the development of applications leveraging Large Language Models (LLMs). It provides support for tasks such as generating code snippets, debugging existing LLM application code, and suggesting improvements to application architecture. The AI assistant can also help with understanding complex LLM concepts and best practices.

When to use it

  • You're building a new application that uses an LLM and need assistance writing the initial code.
  • You’ve encountered errors in your existing LLM application and require debugging support.
  • You want to refactor or improve the design of your current LLM-powered application.
  • You are learning about LLMs and their applications and need explanations or examples.

Key capabilities

  • Code generation for LLM applications
  • Debugging assistance for existing code
  • Architectural suggestions for LLM applications
  • Explanation of LLM concepts

Example prompts

  • "Generate a Python script using Langchain to summarize this article."
  • "I'm getting an error 'Tokenization failed'. Can you help me debug my code?"
  • "How can I improve the efficiency of my prompt engineering workflow?"

Tips & gotchas

The skill’s effectiveness depends on providing clear and specific prompts. It is best suited for developers with some existing familiarity with LLMs and programming concepts.