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Advanced Evaluation

Checked
guanyang·vlatest·ai-ml·MIT

This skill deeply analyzes text for nuanced sentiment, bias, and factual accuracy, providing insightful evaluations to refine content & decision-making.

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 guanyang/open-agent-hub --skill advanced-evaluation -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

guanyang

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 guanyang/open-agent-hub at 0d6f1e7d3f.

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

Commit
0d6f1e7d3f
Manifest
skills/advanced-evaluation/SKILL.md
Scanned
6 files, 1,658 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 provides techniques for evaluating LLM outputs using other LLMs as judges (LLM-as-a-Judge). It synthesizes research and practical experience to build reliable evaluation systems, recognizing that this approach encompasses various methods suited to different contexts. The skill focuses on choosing the appropriate method and mitigating biases inherent in these evaluations to improve content refinement and decision-making.

When to use it

  • Building automated evaluation pipelines for LLM outputs.
  • Comparing multiple model responses to select the best one.
  • Establishing consistent quality standards across evaluation teams.
  • Designing A/B tests for prompt or model changes.
  • Analyzing correlation between automated and human judgments.

Key capabilities

  • Evaluation Taxonomy: Offers two primary approaches: Direct Scoring (for objective criteria like factual accuracy) and Pairwise Comparison (for subjective preferences).
  • Bias Mitigation: Provides strategies to address Position Bias, Length Bias, Self-Enhancement Bias, Verbosity Bias, and Authority Bias.
  • Metric Selection Framework: Suggests appropriate primary and secondary metrics based on task type (binary classification, ordinal scale, pairwise preference, multi-label).

Example prompts

  • "Evaluate these two responses using pairwise comparison to determine which is more persuasive."
  • "Score this response for factual accuracy on a scale of 1 to 5, providing justification for your score."
  • "Compare the tone of these two outputs and select the one that best aligns with a professional style."

Tips & gotchas

  • LLM-as-a-Judge is not a single technique; consider the evaluation context when selecting an approach.
  • Be aware of potential biases (position, length, self-enhancement) and implement mitigation strategies.
  • For Direct Scoring, monitor for score calibration drift and inconsistent scale interpretation.