review

Audit experimental results by validating metric computations and conclusion correctness.

Updated Apr 29, 2026
One-click install
npx skills add https://github.com/smanist/a-exp --skill review-smanist
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review
Source: https://github.com/smanist/a-exp/tree/main/.agents/skills/review
Command: npx skills add https://github.com/smanist/a-exp --skill review-smanist

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validates experimental results by auditing metric computations and the validity of conclusions.

Core Features & Use Cases

  • Metrics validation: check metric definitions, calculations, and their applicability to the experimental setup.
  • Findings validation: verify that written conclusions are supported by the metrics and methodology.
  • Full-pipeline execution: run metrics first, then findings, or execute both in sequence with a single command.

Quick Start

Invoke the review workflow with a metric pass, a findings pass, or both by invoking /review.

Frequently Asked Questions about review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit experimental results to ensure metric calculations are correct?▼

Audit experimental results by running a metrics validation pass that checks metric definitions, calculations, and applicability to the experimental setup. This verifies metric accuracy before conclusions are drawn.

What is findings validation in experiment quality assurance?▼

Findings validation verifies that written conclusions are fully supported by the computed metrics and methodology. It enforces structured checks like constraint extraction, attribution tests, and cross-session citation verification.

How do I run full-pipeline validation for both metrics and findings?▼

Run full-pipeline validation by invoking the review workflow without specifying a mode. This executes metrics validation first, then findings validation sequentially in a single automated command.

Can I validate reproducibility across multiple experiment sessions?▼

Yes, you can validate reproducibility across experiment sessions using cross-session citation verification. This structured check ensures findings remain consistent and correctly attributed across different experiment runs.

What structured checks are applied during experiment validation?▼

Structured checks applied during experiment validation include constraint extraction, degeneracy tests, attribution tests, and cross-session citation verification. These enforce metric and finding correctness.

Does experiment validation work with existing experiment tooling?▼

Yes, experiment validation integrates with existing experiment tooling to support both metrics and findings modes. This allows you to audit results without replacing your current experimental workflow.