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Evaluation Harness

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patricio0312rev·vlatest·agents·MIT

This Evaluation Harness assesses model outputs against defined criteria, ensuring consistent and reliable performance evaluations for improved results.

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 patricio0312rev/skills --skill evaluation-harness -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

patricio0312rev

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Install target

Live repository

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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

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    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned patricio0312rev/skills at 79ea6af485.

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

Commit
79ea6af485
Manifest
ai-engineering/evaluation-harness/SKILL.md
Scanned
1 file, 208 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.
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About this skill

What it does

The Evaluation Harness skill enables AI agents to systematically evaluate model outputs against predefined criteria using a structured dataset and scoring methods. It allows for consistent performance assessments of LLM applications, providing detailed scores based on metrics like exact match, semantic similarity, and rubric-based evaluations judged by another LLM. This facilitates improved results through data-driven feedback and iterative refinement.

When to use it

  • Evaluating the quality of code generated by a model against expected solutions.
  • Assessing the accuracy and style of text generation tasks.
  • Benchmarking different models on a standardized dataset with defined scoring rubrics.
  • Identifying areas where a model struggles based on category or specific criteria.
  • Automating the evaluation process for LLM applications to ensure consistent performance.

Key capabilities

  • Golden Dataset Format: Uses a structured JSON format for test cases, including input prompts, expected outputs, rubrics (criteria and weights), and metadata.
  • Scoring Methods: Provides multiple scoring functions: exact match, semantic similarity (using embeddings), keyword presence, and LLM-based rubric evaluation.
  • Test Runner: Automates the process of running evaluations against a model function, generating scores for each test case.
  • Thresholds & Pass Criteria: Allows defining minimum score thresholds per category to determine if a test case "passed."
  • Reporting: Generates reports summarizing evaluation results, including individual scores and overall pass/fail status.

Example prompts

  • "Run the Evaluation Harness on this model function using the dataset located at /path/to/dataset.json."
  • "Evaluate the code generation model's performance against the 'code_generation' category in the evaluation dataset, focusing on correctness and style."
  • "Generate a report summarizing the results of running the Evaluation Harness with the provided model function and dataset."

Tips & gotchas

  • Requires a properly formatted "golden dataset" (JSON file) defining test cases, expected outputs, and rubrics.
  • The score_semantic_similarity method depends on an external embedding generation function (get_embedding). This needs to be defined or available for the skill to work correctly.
  • For LLM-based rubric evaluation (score_with_llm), access to a functional LLM is required, as it utilizes an LLM to judge outputs against criteria.