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Evaluation

Listed
sickn33·vlatest·other·MIT

This "Evaluation" skill analyzes text to assess its quality, clarity, and potential biases, aiding in refining content and 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 sickn33/agentic-awesome-skills --skill 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

sickn33

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 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/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

The Evaluation skill analyzes agent systems to assess their performance and quality. It moves beyond traditional software evaluation by accounting for the non-deterministic nature of agents, which can take multiple valid paths to achieve goals. The skill helps build robust evaluation frameworks that provide actionable feedback for continuous improvement, regression detection, and validation of context engineering choices.

When to use it

  • Testing agent performance systematically.
  • Validating context engineering choices made when designing an agent system.
  • Measuring improvements in agent performance over time.
  • Catching regressions (undesirable changes) before deploying an agent.
  • Building quality gates for automated agent pipelines.

Key capabilities

  • Supports outcome-focused evaluation, judging agents based on results rather than specific steps taken.
  • Utilizes multi-dimensional rubrics to assess aspects like factual accuracy, completeness, citation accuracy, source quality, and tool efficiency.
  • Can leverage LLM-as-judge for scalable evaluation while incorporating human review for edge cases.
  • Considers token usage, number of tool calls, and model choice as key performance drivers in agent evaluation.

Example prompts

  • "Evaluate this agent's response to the query 'Find the capital of France' focusing on factual accuracy."
  • "Assess the efficiency of this agent’s process for summarizing a news article."
  • "Compare the performance of these two agent configurations when completing task X, using the provided rubric."

Tips & gotchas

  • Agent systems are non-deterministic; evaluations should account for multiple valid paths to achieve goals.
  • Token budgets significantly impact agent performance – evaluate agents within realistic resource constraints.
  • Upgrading the underlying model often yields greater improvements than simply increasing token limits.