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Llm Advisor

Checked
ckorhonen·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 ckorhonen/claude-skills --skill llm-advisor -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

ckorhonen

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 ckorhonen/claude-skills at 947001b038.

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

Commit
947001b038
Manifest
skills/llm-advisor/SKILL.md
Scanned
1 file, 318 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 llm-advisor skill provides guidance and recommendations when working with Large Language Models (LLMs). It helps users refine prompts, troubleshoot issues, and understand LLM behavior. Specifically, it can analyze existing prompts for clarity and effectiveness, suggest alternative phrasing to improve results, and explain why an LLM might be producing unexpected output.

When to use it

  • Prompt Refinement: You're not getting the desired response from your LLM prompt and need help improving its structure or content.
  • Troubleshooting Unexpected Output: The LLM is generating responses that are nonsensical, irrelevant, or factually incorrect, and you want to understand why.
  • Understanding LLM Behavior: You're curious about how different phrasing or instructions impact the LLM’s output and want insights into its decision-making process.
  • Learning Prompt Engineering Techniques: You are new to prompt engineering and would like assistance in crafting more effective prompts.

Key capabilities

  • Prompt analysis for clarity and effectiveness
  • Suggestion of alternative phrasing for improved results
  • Explanation of LLM behavior and potential causes of unexpected output

Example prompts

  • "Analyze this prompt: 'Write a short story about a cat.' How can I make it more specific?"
  • "Why did the LLM generate an incorrect answer when I asked, 'What is 2 + 2?'"
  • "Suggest improvements to this prompt: 'Summarize this article.'"

Tips & gotchas

The llm-advisor skill works best with clear and concise prompts. While it can analyze complex prompts, simpler examples often yield more actionable feedback.