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Eval Accuracy

Checked
whitespectre·vlatest·ai-ml

Evaluates the accuracy of generated content by comparing it to a reference source, ensuring reliable and trustworthy outputs.

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 whitespectre/ai-assistant-evals --skill eval-accuracy -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

whitespectre

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

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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 whitespectre/ai-assistant-evals at 8d48470982.

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

Commit
8d48470982
Manifest
skills/eval-accuracy/SKILL.md
Scanned
2 files, 62 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 eval-accuracy skill assesses the accuracy of AI assistant responses against a provided ground truth. It calculates and reports metrics like precision, recall, and F1-score to quantify performance. This allows for objective evaluation and comparison of different AI models or prompt strategies.

When to use it

  • Evaluating chatbot performance: Measure how accurately a chatbot answers questions compared to expected responses.
  • Comparing model outputs: Determine which language model produces more accurate results for a given task.
  • Prompt engineering optimization: Assess the impact of different prompts on the accuracy of AI-generated content.
  • Benchmarking AI systems: Establish baseline performance metrics for ongoing monitoring and improvement.

Key capabilities

  • Accuracy assessment
  • Precision calculation
  • Recall calculation
  • F1-score calculation

Example prompts

  • "Evaluate the following assistant response: '[Assistant Response]' against this ground truth: '[Ground Truth]'."
  • "Calculate the F1 score for these two texts: '[Assistant Response]' and '[Ground Truth]'."
  • "Assess the accuracy of this AI output: '[Assistant Output]' compared to the expected answer: '[Expected Answer]'."

Tips & gotchas

The quality of the ground truth data is crucial for accurate evaluation. Ensure your ground truths are comprehensive, correct, and representative of the desired response style.