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Llm Council

Listed
oimiragieo·vlatest·ai-ml

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 oimiragieo/agent-studio --skill llm-council -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

oimiragieo

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.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 73 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as oimiragieo/agent-studio.
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About this skill

What it does

The oimiragieo-llm-council skill provides a mechanism to evaluate and rate Large Language Model (LLM) responses based on predefined criteria. It allows users to establish guidelines for LLM behavior, ensuring outputs adhere to specific standards or policies. This promotes consistency and quality in AI agent interactions.

When to use it

  • Content Moderation: Assess LLM-generated content for safety, appropriateness, and adherence to community guidelines before publication.
  • Policy Compliance: Verify that an LLM's responses align with internal company policies or legal regulations.
  • Quality Assurance: Evaluate the accuracy and helpfulness of LLM answers in a customer service chatbot environment.
  • Training Data Validation: Rate generated training data to ensure it meets quality standards for fine-tuning models.

Key capabilities

  • LLM response evaluation
  • Rating based on predefined criteria
  • Guideline enforcement
  • Policy alignment verification

Example prompts

  • "Evaluate this LLM response: '[Response text]' against the following guidelines: [Guidelines]."
  • "Rate this answer for helpfulness and accuracy: '[Response text]'"
  • "Does this output comply with our company's content policy? [Response Text]"

Tips & gotchas

The effectiveness of this skill relies on clearly defined evaluation criteria. Ambiguous or poorly structured guidelines will lead to inconsistent ratings.