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Multi Llm Consult

Listed
nickcrew·vlatest·ai-ml·MIT

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 nickcrew/claude-cortex --skill multi-llm-consult -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

nickcrew

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: dashscope.aliyuncs.com.

    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.

    Hosts contacted

    api.openai.commodel providerdashscope.aliyuncs.comundeclaredgenerativelanguage.googleapis.commodel provider

    What it found

    • skills/multi-llm-consult/scripts/consult_llm.py:45contacts dashscope.aliyuncs.com
      "qwen": "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",

  • 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 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 nickcrew/claude-ctx-plugin at bb47af79ad.

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

Commit
bb47af79ad
Manifest
skills/multi-llm-consult/SKILL.md
Scanned
3 files, 435 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

The multi-llm-consult skill allows an AI agent to leverage multiple Large Language Models (LLMs) for a single task, aggregating their responses and providing a more comprehensive or nuanced answer. It facilitates querying different LLMs based on the specific needs of the prompt and intelligently combines their outputs. This can lead to improved accuracy, creativity, or coverage compared to relying on a single model.

When to use it

  • Complex Research: When gathering information from diverse sources or perspectives is crucial for understanding a topic.
  • Creative Writing Assistance: To generate varied ideas and overcome writer's block by soliciting input from multiple LLMs with different writing styles.
  • Code Generation & Debugging: To get alternative solutions or identify potential errors using models trained on different codebases or debugging techniques.
  • Comparative Analysis: When needing to compare the strengths and weaknesses of different approaches or arguments presented by various LLMs.

Key capabilities

  • Querying multiple LLMs simultaneously.
  • Aggregating responses from different LLMs.
  • Intelligent combination of outputs from diverse models.
  • Selection of appropriate LLMs based on prompt requirements.

Example prompts

  • "Compare and contrast the economic policies of Reagan and Thatcher, using GPT-4 and Claude 3 Opus."
  • "Generate three different outlines for a science fiction novel about space exploration; use Gemini Pro, Llama 3, and Mistral Large."
  • “Debug this Python code snippet: [code]. Use both ChatGPT and Bard to identify potential issues.”

Tips & gotchas

  • The effectiveness of this skill depends on the quality and diversity of the LLMs being used. Consider models with different strengths and training data.