ai-eval-review
Evaluates AI performance evidence to confirm accuracy, grounding, and valid tool usage.
Install
mkdir -p .claude/skills/ai-eval-review && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12879" && unzip -o skill.zip -d .claude/skills/ai-eval-review && rm skill.zipInstalls to .claude/skills/ai-eval-review
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Review AI evaluation evidence for grounding, hallucination risk, tool assumptions, and whether results actually support the claimed behavior.Key capabilities
- →Compare evaluation results to stated behavior
- →Check for hallucination risk in AI output
- →Verify retrieval and tool assumptions
- →Confirm eval exercises claimed behavior
- →Identify gaps missed by automated scores
How it works
This skill reviews AI evaluation evidence by comparing results to stated behavior, checking for risks, and confirming the evaluation's scope.
Inputs & outputs
When to use ai-eval-review
- →Reviewing AI evaluation metrics
- →Checking for model hallucinations
- →Validating test evidence
- →Grounding AI output claims
About this skill
AI Eval Review
Purpose
Decide whether evaluation evidence is strong enough to support the claimed AI behavior.
Read
.codex/project-context.md- relevant design docs
- relevant tests and validation output
- the prompt or eval scenario under review
Do
- Compare eval results to the stated behavior and grounding.
- Check hallucination risk, retrieval/tool assumptions, and prompt boundaries.
- Confirm the eval exercises the behavior it claims to prove.
- Identify gaps that automated scores may miss.
- Recommend prompt, design, test, or validation updates as separate findings.
Outputs
- Pass/fail/partial status per evaluation scenario.
- Notes on hallucination checks, grounding, and tool usage.
- Follow-up recommendations.
Rules
- Do not approve plausible model output without grounding evidence.
- Do not conflate eval coverage with complete behavior validation.
- Do not change requirements while reviewing eval evidence.
When not to use it
- →When plausible model output is acceptable without grounding evidence
- →When eval coverage is considered complete behavior validation
- →When requirements should be changed during review
Limitations
- →Does not approve plausible model output without grounding evidence
- →Does not conflate eval coverage with complete behavior validation
- →Does not change requirements while reviewing eval evidence
How it compares
This workflow provides a structured review of AI evaluation evidence, focusing on grounding and hallucination risks, which goes beyond automated scoring alone.
Compared to similar skills
ai-eval-review side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| ai-eval-review (this skill) | 0 | 2mo | No flags | Intermediate |
| mlops-validation | 2 | 6mo | No flags | Intermediate |
| python-testing-patterns | 77 | 2mo | Review | Intermediate |
| dependency-upgrade | 26 | 5mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by chethanp11
View all by chethanp11 →You might also like
mlops-validation
fmind
Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
python-testing-patterns
wshobson
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
dependency-upgrade
wshobson
Manage major dependency version upgrades with compatibility analysis, staged rollout, and comprehensive testing. Use when upgrading framework versions, updating major dependencies, or managing breaking changes in libraries.
test-cases
cexll
This skill should be used when generating comprehensive test cases from PRD documents or user requirements. Triggers when users request test case generation, QA planning, test scenario creation, or need structured test documentation. Produces detailed test cases covering functional, edge case, error handling, and state transition scenarios.
reviewing-code
CaptainCrouton89
Systematically evaluate code changes for security, correctness, performance, and spec alignment. Use when reviewing PRs, assessing code quality, or verifying implementation against requirements.
wcag-audit-patterns
wshobson
Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing accessible design patterns.