evaluation
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 with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "evaluation" from https://skillstore.io/skills/muratcankoylan-evaluation.md and its manifest at https://skillstore.io/api/skills/muratcankoylan-evaluation/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "evaluation". Evaluate a customer support agent that answers account and billing questions.
Expected outcome:
- Dimensions: factual accuracy 35%, completeness 25%, policy compliance 25%, clarity 10%, tool efficiency 5%.
- Deterministic gates: account identifiers are valid, required disclosures appear, and prohibited actions are absent.
- Release threshold: overall score at least 0.85, with no dimension below 0.75.
Using "evaluation". Create a regression suite for a research agent.
Expected outcome:
- Use 50 cases across simple lookup, comparison, multi-source synthesis, and ambiguous research tasks.
- Score citation accuracy, source quality, completeness, factual accuracy, and tool efficiency separately.
- Compare each release with the established baseline and review all significant dimension regressions.
Using "evaluation". Plan production monitoring for an agent with variable traffic.
Expected outcome:
- Sample one percent of interactions, plus all failures and unusual tool sequences.
- Alert when pass rate falls below 0.85 and escalate when it falls below 0.70.
- Review trends by task type, complexity, model version, and evaluation dimension.
Security Audit
SafeAll 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.
Risk Factors
โ๏ธ External commands (18)
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
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https://skillstore.io/skills/muratcankoylan-evaluation/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/muratcankoylan-evaluation?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/muratcankoylan-evaluation/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/muratcankoylan-evaluation.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA ยท BibTeX ยท CFF)
APA citation
muratcankoylan. (2026). evaluation security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/muratcankoylan-evaluation/audits/8BibTeX citation
@techreport{muratcankoylan-muratcankoylan-evaluation-2026,
author = {muratcankoylan},
title = {evaluation security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/muratcankoylan-evaluation/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "evaluation security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "muratcankoylan"
date-released: "2026-08-09"
url: "https://skillstore.io/skills/muratcankoylan-evaluation/audits/8"
identifiers:
- type: other
value: "skillstore:muratcankoylan-evaluation:audit:8"
description: "Skillstore immutable audit report identifier"
Compare variants
4 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
muratcankoylan-evaluation
2026-08-21
chakshugautam-evaluation
2026-08-21
sickn33-evaluation
2026-08-21
asmayaseen-evaluation
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
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.
Try These Prompts
Create an evaluation plan for [agent task]. Define success outcomes, five quality dimensions, measurable criteria, and appropriate pass thresholds.
Design 50 evaluation cases for [agent workflow]. Stratify them by complexity, include edge cases, and describe expected outcomes without requiring fixed execution paths.
Design a release gate for [agent system]. Separate deterministic checks from rubric scores, define dimension minimums, and specify baseline regression tolerances.
Create a production evaluation strategy for [agent system]. Include sampling, human review, alert thresholds, confidence reporting, drift detection, and baseline comparisons.
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.
Avoid
- 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.
Frequently Asked Questions
What types of agents can this skill evaluate?
Does the skill call an LLM judge?
Can I use a custom rubric?
How large should an evaluation set be?
How should I handle non-deterministic outputs?
Does this replace human review?
Developer Details
Author
muratcankoylanLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/main/skills/evaluationRef
02be9409c79ca1183f7844009c14d9df684d0cf9
Maintenance freshness
8/11/2026
Usage
10 downloads ยท 254 views
File structure