# agent-evaluation

A skill for comparing agent, prompt or tool changes against versioned cases with explicit verifiers. Useful for reproducing a failure or running a regression pass.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 971 installs · ★ 48508
- Author: sickn33
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/skills/agent-evaluation/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/sickn33-agent-evaluation

## What it does

The skill lays out a procedure for judging observable agent behaviour: freeze the contract, validate the harness, run the cases, investigate disagreement, compare baseline with candidate, then fix and verify. Cases carry expected outcomes and permission boundaries, while model, prompt, tool and runtime versions are recorded separately.

You need a case set with a revision, a known baseline and a candidate, synthetic or redacted inputs, isolated targets, and a budget for tokens, time and cost. The verifier has to tell a wrong outcome apart from an expected safe rejection, a verifier fault and an infrastructure outage. Provider access matters only where the declared evaluation calls that provider.

A JavaScript helper `wilson95(passes, trials)` ships with the skill: it returns an approximate 95% Wilson interval and throws on invalid counts. Ten passes out of ten do not prove full reliability, and repeated runs of a single case are not independent samples. Safety and authorization failures stay separate from average quality, because a higher score cannot offset them.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/agent-evaluation/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/agent-evaluation/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/agent-evaluation/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/agent-evaluation/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install sickn33/agentic-awesome-skills agent-evaluation
```

2. **Manually: put the files in place**
- File: `.github/skills/agent-evaluation/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/sickn33/agentic-awesome-skills --path skills/agent-evaluation
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/agent-evaluation/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/skills/agent-evaluation/SKILL.md

## When to use it
- compare agent behaviour before and after a prompt change
- reproduce an observed failure on a frozen case
- separate agent errors from verifier or infrastructure faults
- estimate reliability on a declared task distribution with uncertainty
- catch an unauthorized tool call in a negative case

## Example prompts
- Run the frozen case set for baseline and candidate and report per-case disagreement
- Validate the harness with a known-pass case, a known-fail case and a deliberate verifier failure
- Compute the Wilson interval for 10 passes out of 10 trials with `wilson95`

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE 1c5d37056771cf27
```text
---
name: agent-evaluation
description: "Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests."
risk: safe
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---

# Agent Evaluation

Evaluate observable agent behavior against task-specific cases. Modified by AAS maintainers on 2026-09-05 to remove unsupported benchmark claims, correct uncertainty/error reporting and separate optional architecture sketches from the operating procedure.

## When to Use

Use when comparing a changed agent, prompt or tool configuration, reproducing an observed failure, or estimating reliability on a declared task distribution. Do not infer product readiness from a public benchmark percentage or a generic score threshold.

## Prerequisites

- A versioned case set with expected observable outcomes and permission boundaries.
- A known baseline and candidate revision, including model, prompt, tools, configuration and runtime versions.
- Authorized synthetic or redacted inputs, isolated targets and a bounded token, time and cost budget.
- A verifier that distinguishes wrong outcomes, expected safe rejections, evaluator failures and infrastructure outages. Provider access is needed only if the declared evaluation calls that provider.

## Evaluation procedure

1. **Freeze the contract.** Record case IDs and dataset revision, baseline/candidate identities, target environment, repeat plan, budgets, stopping rule and decision criteria before execution. Keep critical safety and authorization failures separate from average quality; they cannot be compensated by a higher score.
2. **Validate the harness.** Run a known-pass case, a known-fail case and a deliberate verifier/infrastructure failure. Confirm that each is classified correctly and that trace retention excludes credentials and private input bodies. If classification is wrong, fix the harness and repeat these checks before measuring the agent.
3. **Run the frozen cases.** Use the same case definitions and budgets for baseline and candidate, with independent fixture state and recorded execution order. Retain every attempt and its run ID, outcome, reason, latency and resource totals. An exception is not evidence that an unsafe request was safely rejected.
```
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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md