# Build Reliable Agent Evaluations

Agent quality is difficult to measure because outputs vary and may have several valid forms. This skill builds repeatable rubrics, tests, gates, and monitoring.

## Install

```bash
npx skillstore add muratcankoylan/evaluation
```

## Metadata

- Status: approved
- Slug: muratcankoylan-evaluation
- Skillstore revision: r2
- Version status: missing
- Tree hash: a991217c21d5419965a3681b1c5cbd95e32883cc2bc166f81a8f950ca7fbd671
- Author: muratcankoylan
- GitHub username: muratcankoylan
- License: MIT
- Repository: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/main/skills/evaluation
- Ref: 02be9409c79ca1183f7844009c14d9df684d0cf9
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 81
- Quality tier: silver
- Public page: https://skillstore.pages.dev/skills/muratcankoylan-evaluation
- Manifest: https://skillstore.pages.dev/api/skills/muratcankoylan-evaluation/manifest

## Capabilities

- Design weighted, multi-dimensional rubrics for accuracy, completeness, citations, source quality, and tool efficiency.
- Create representative test sets stratified by task complexity and known edge cases.
- Run deterministic checks before subjective model or human evaluation.
- Compare configurations against baselines and identify regressions by quality dimension.
- Define production sampling, pass-rate thresholds, trend metrics, and quality alerts.

## Use Cases

- Gate Agent Releases: Build regression suites and quality thresholds that detect performance drops before deployment.
- Compare Agent Configurations: Measure quality, token use, and tool efficiency across models, prompts, or context strategies.
- Monitor Production Quality: Define sampling, alerts, and trend reports for changing agent behavior in live systems.

## Prompt Templates

### Define Evaluation Goals

```
Create an evaluation plan for [agent task]. Define success outcomes, five quality dimensions, measurable criteria, and appropriate pass thresholds.
```

### Build a Test Set

```
Design 50 evaluation cases for [agent workflow]. Stratify them by complexity, include edge cases, and describe expected outcomes without requiring fixed execution paths.
```

### Create a Quality Gate

```
Design a release gate for [agent system]. Separate deterministic checks from rubric scores, define dimension minimums, and specify baseline regression tolerances.
```

### Design Production Monitoring

```
Create a production evaluation strategy for [agent system]. Include sampling, human review, alert thresholds, confidence reporting, drift detection, and baseline comparisons.
```

## Limitations

- The included evaluator uses simple heuristics and simulated agent output, so production integrations require custom logic.
- It does not design or calibrate advanced LLM judge prompts.
- It does not run agents, call external models, or connect to monitoring services.
- Reliable thresholds still require representative data and periodic human review.

## Best Practices

- Measure final outcomes and quality dimensions instead of requiring one execution path.
- Run deterministic validation before model judgment and preserve dimension-level results.
- Use representative cases, production-realistic budgets, independent judges, and regular human review.

## Anti Patterns

- Do not rely on one aggregate score that can hide critical dimension failures.
- Do not build test sets only from easy, clean, or synthetic examples.
- Do not treat evaluation as a one-time release activity without baseline tracking.

## Security Audit

- Audited at: 2026-08-09T09:44:25.256\+00:00
- Summary: All 29 static findings are false positives caused by Python mapping methods, documentation markup, ordinary prose, comments, or YAML examples. The reviewed files contain no command execution, sensitive file access, system reconnaissance, prompt injection, or other semantic security concern.

## Stats

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- Downloads: 11
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