Guide · Agent skill

How to use the Agent Evaluation agent skill

Updated 2026-10-29 · written with AI from the project's own documentation and checked against our catalogue data

The Agent Evaluation skill is a set of instructions that tells an AI agent how to test whether a changed agent, prompt or tool setup works better or worse than before. It is aimed at engineers comparing a baseline against a candidate, teams reproducing an observed failure, and anyone who needs an honest account of uncertainty. After this guide you will know how to install the skill, how to ask for it in plain language, and what the agent will and will not do with your results.

Agent Evaluationsickn33/agentic-awesome-skills · MIT Download the skill

Quick start

1Download and unzip. Download the skill ZIP from this page and unzip it. You should end up with a folder containing SKILL.md and a references folder.
2Place the folder. Copy the unzipped folder into ~/.claude/skills/ for personal use, or into the project's .claude/skills/ folder to limit it to one project.
3Or upload it in the apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the skill there instead.
4Prepare your inputs. Gather a versioned case set and the baseline and candidate configurations you control, using synthetic or authorised redacted data only.
5Ask in plain language. Describe the comparison, the failure to reproduce or the regression suite you need, and the agent will follow the frozen-contract procedure.

What the skill makes the agent do

Once the skill is installed, the agent follows a fixed procedure when you ask it to evaluate agent behaviour. It freezes the test cases and pass criteria before running anything, then runs the same cases against the baseline and the candidate with the same budgets.

The procedure also separates real agent failures from harness bugs and infrastructure outages, keeps a record of every attempt, and avoids claiming reliability from too few runs. The skill includes a small helper for computing a Wilson confidence interval on pass rates.

Freezes case IDs, pass criteria, repeat plan and budgets before execution
Checks the test harness with a known-pass case, a known-fail case and a deliberate infrastructure failure
Keeps every attempt, including failures, rather than retrying until the result is green
Reports per-case results, uncertainty, regressions and critical violations

How to install the Agent Evaluation skill

Download the ZIP for this skill from this page and unzip it. To make it available across your work, place the unzipped skill folder in ~/.claude/skills/. To limit it to one project, place it in that project's .claude/skills/ folder instead.

You can also upload the skill in the Claude apps under Settings → Capabilities → Skills.

Personal install: ~/.claude/skills/
Project install: the .claude/skills/ folder inside your project
App install: upload under Settings → Capabilities → Skills

How to trigger the skill with natural requests

You do not need a special command. Describe the task in plain language. The skill's description covers three situations: comparing agent or prompt changes, reproducing failures, and running agent regression tests. Mention one of these and say what you have available, such as a versioned case set or a baseline and candidate configuration.

Compare my changed prompt against the baseline using our frozen case set and tell me if anything regressed.
Reproduce this failure where the agent called a tool with the wrong tenant ID, and build a case that captures it.
Set up a regression suite for this agent and explain how many repeats we need before drawing conclusions.
Estimate the pass rate of the candidate with an honest confidence interval.

Worked example: a changed tool argument

Suppose a synthetic agent changes how it chooses a tenant identifier for a read-only lookup. The skill would freeze three cases: an authorised lookup must return the seeded fixture, an unauthorised tenant must be rejected without a tool call, and a simulated tool outage must be classified as an infrastructure failure. No real customer records or production credentials are used.

Suppose the candidate passes the authorised lookup in all five runs but makes one unauthorised call in the second case. The skill treats the candidate as failing the permission contract even if its overall success rate improves. The run is kept, the argument authorisation is fixed, the negative case is verified, and the frozen suite is rerun from clean state. These outcomes are illustrative, not measured results.

Check uncertainty with the Wilson helper

Ten successes in ten independent trials do not prove 100% reliability. The skill's dependency-free JavaScript helper returns an approximate 95% Wilson interval. For 10 out of 10 it returns about [0.7225, 1]. For zero trials it rejects the input.

When repeated runs share cases or state, use case-level or clustered uncertainty instead, because pooling correlated runs as independent observations overstates confidence. The NIST interval guidance linked from the skill explains the method.

0 out of 10 gives a positive upper bound
10 out of 10 gives a lower bound below 1
0 out of 0is rejected as invalid input

function wilson95(passes, trials) {
  if (!Number.isSafeInteger(passes) || !Number.isSafeInteger(trials)
      || trials <= 0 || passes < 0 || passes > trials) throw new Error('Invalid counts');
  const z = 1.959963984540054;
  const p = passes / trials;
  const denominator = 1 + z * z / trials;
  const center = (p + z * z / (2 * trials)) / denominator;
  const margin = z * Math.sqrt(p * (1 - p) / trials + z * z / (4 * trials * trials)) / denominator;
  return [Math.max(0, center - margin), Math.min(1, center + margin)];
}

What the skill needs and its safety notes

The skill requires a versioned case set, a baseline and candidate configuration you control, and isolated test targets. A Python or JavaScript runtime is needed to run the example helper. Provider API access is needed only if your own evaluation calls a provider.

The skill's listing states that it does not run scripts and does not use the network. It says to use synthetic or authorised, redacted inputs with bounded retention, and to avoid real customer data or production credentials. The skill is not required to work with any other tool or skill.

Requires: versioned case set, baseline and candidate, isolated targets
Requires: Python or JavaScript runtime for the example helper
Runs scripts: no
Uses network: no
Avoid real customer data and production credentials

Tips and limitations

The architecture sketches in the references folder are optional design notes, not a runnable framework. Read them only when you are designing a custom harness, and expect to write application-specific adapters.

The example thresholds and score weights in those sketches are not universal merge or deploy rules. Define your own criteria and keep critical safety failures separate from average quality. A result that is not statistically significant does not prove two versions are equivalent.

Freeze the contract before running anything
Keep failed attempts; never change an expected outcome to fit the candidate
Treat an exception as an error, not as a safe rejection
Do not treat LLM judges as a substitute for real user feedback

What people use it for

Comparing a baseline agent with a candidate after a prompt, model or tool change
Reproducing an observed agent failure and turning it into a stable test case
Building a regression suite for an agent that must keep refusing unauthorised tool calls
Reporting pass rates with an honest confidence interval instead of a single headline number

Frequently asked questions

How do I install the Agent Evaluation agent skill?

Download the ZIP from this page and unzip it. Place the folder in ~/.claude/skills/ for personal use or in a project's .claude/skills/ folder, or upload it in the Claude apps under Settings → Capabilities → Skills.

Is the agent-evaluation skill free to use?

The skill is listed under the MIT licence. Check the repository's licence file for the full terms before reusing it.

What do I need to run the Agent Evaluation skill?

You need a versioned case set, a baseline and candidate configuration you control, and isolated test targets. A Python or JavaScript runtime runs the example helper, and provider API access is needed only if your evaluation calls a provider.

Does the skill run scripts or use the internet?

According to its listing, the skill does not run scripts and does not use the network. Its helper is dependency-free JavaScript.

Can I use this skill for a quick check of a chatbot answer?

The listing says this is not the right tool for that. The procedure is heavier than needed for a one-off check of a single answer.

How is this different from a generic benchmark score?

The skill says not to infer readiness from a public benchmark percentage or a generic score threshold. It evaluates your declared task cases and reports per-case results, regressions and uncertainty.

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Our verdict

This skill suits engineers and teams who already have a versioned case set and isolated test targets, and who want a disciplined way to compare agent versions. It is probably too heavy for quick one-off checks, and teams without a case set or isolated targets should build those first.

Download the skill