Llm Evaluation

Name: Llm Evaluation
Author: sickn33

Listed

sickn33·vlatest·ai-ml·MIT

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

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Run this in your project

terminal

$ npx skills add sickn33/agentic-awesome-skills --skill llm-evaluation -a claude-code

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What we know about this skill

Publisher

sickn33

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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 28 Sept 2026

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.

Declares what it does: failed
No SKILL.md was found in the skill's directory.
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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 5a4cdd9df8
Manifest: skills/llm-evaluation/SKILL.md
Scanned: 0 files
Copies in repo: 3 — all of them scanned

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 enables AI agents to perform structured evaluations of Large Language Models (LLMs) using predefined criteria and scoring mechanisms. It automates the assessment process to provide quantitative feedback on model performance across specific tasks or domains.

When to use it

Validating the accuracy and relevance of an LLM's responses during development cycles.
Comparing multiple model versions against a standardized rubric to identify improvements.
Ensuring compliance with safety guidelines by scoring outputs for harmful content.
Generating detailed performance reports for stakeholder review or deployment readiness.

Key capabilities

Executes automated evaluation workflows based on user-defined parameters.
Assigns numerical scores to LLM outputs based on specific quality metrics.
Provides structured feedback highlighting strengths and weaknesses in model responses.

Example prompts

"Evaluate this LLM response for factual accuracy and tone consistency using the standard rubric."
"Run a comparative evaluation of three different model outputs against our safety guidelines."
"Score the following generated text based on creativity, coherence, and adherence to instructions."

Tips & gotchas

Ensure you have clear, well-defined evaluation criteria before running assessments to avoid ambiguous scoring. This skill relies on structured input; vague prompts may result in less actionable evaluation data.