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Agent Evaluation

Checked
eyadsibai·vlatest·agents

Evaluates agent performance against defined metrics, providing actionable insights to improve effectiveness and efficiency.

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 eyadsibai/ltk --skill agent-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

eyadsibai

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 eyadsibai/ltk at f8e85697b9.

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

Commit
f8e85697b9
Manifest
plugins/ltk-core/skills/agent-evaluation/SKILL.md
Scanned
1 file, 119 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, Agent Evaluation, assesses the performance of AI agents using a structured approach that accounts for their non-deterministic nature and lack of single correct answers. It identifies key factors influencing agent effectiveness – primarily token usage, tool calls, and model choice – to provide actionable insights for improvement. The skill uses an LLM-as-Judge system to evaluate agent outputs against ground truth data based on a multi-dimensional rubric.

When to use it

  • To understand the primary drivers of agent performance (token usage, tool calls, model selection).
  • When comparing different agent strategies or models for a given task.
  • To identify and address potential pitfalls like path overfitting or ignoring edge cases in agent behavior.
  • For continuous monitoring of agent quality following changes or updates.
  • To evaluate the impact of context engineering on agent performance.

Key capabilities

  • Multi-Dimensional Rubric: Evaluates agents based on factual accuracy, completeness, citation accuracy, and tool efficiency.
  • LLM-as-Judge: Uses an LLM to score agent outputs against ground truth data with reasoning provided.
  • Test Set Design: Employs a test set stratified by complexity (simple, medium, complex, very complex) to assess performance across different scenarios.
  • Complexity Stratification: Categorizes tasks based on the number of tool calls and level of reasoning required.
  • Context Engineering Evaluation: Tests and compares agent performance with different context strategies.
  • Continuous Evaluation Pipeline: Automates evaluation runs, tracks metrics over time, and sets alerts for quality drops.

Example prompts

  • "Evaluate this agent's response to the prompt: 'What is the capital of France?'"
  • "Compare the performance of Agent A and Agent B on the task: 'Analyze Q1-Q4 sales trends.'"
  • "Run an evaluation pipeline with a test set including tasks of varying complexity."

Tips & gotchas

  • Agent Evaluation focuses on outcomes, not the specific steps taken by the agent.
  • Ensure your test sets include diverse scenarios to avoid overlooking edge cases.
  • The skill uses token usage as a key performance driver; be mindful of cost implications when increasing token limits.