Reviewing Agent Output

Validate AI agent deliverables against acceptance criteria and integration contracts.

1|2|Updated Feb 5, 2026

Search Tags:#quality assurance #code review #integration testing #validation #acceptance criteria #agent output

Ready to run

npx skills add https://github.com/7a336e6e/skills --skill reviewing-agent-output

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Reviewing Agent Output
Source: https://github.com/7a336e6e/skills/tree/main/agent-manager/reviewing-agent-output
Command: npx skills add https://github.com/7a336e6e/skills --skill reviewing-agent-output

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that the output generated by AI agents meets predefined quality standards, adheres to project requirements, and integrates seamlessly with other agents' work.

Core Features & Use Cases

Quality Gates: Enforces checks for completeness, correctness, consistency, and test coverage.
Integration Verification: Validates cross-agent contracts (e.g., API endpoints, data models).
Use Case: After the backend engineer implements a new API endpoint, this skill reviews the code to ensure it matches the frontend's expected request/response format and includes necessary error handling.

Quick Start

Use the reviewing agent output skill to approve the task 'T3 — Build ProfilePage component' if all acceptance criteria are met.

Frequently Asked Questions about Reviewing Agent Output

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

FAQPage Schema

How do I validate AI agent output against acceptance criteria?▼

Cross-agent integration testing verifies contracts between frontend, backend, storage, and auth systems to ensure API endpoints and data models match expected request and response formats.

What is the best way to verify cross-agent contracts between frontend and backend systems?▼

Cross-agent integration testing verifies contracts between frontend, backend, storage, and auth systems to ensure API endpoints and data models match expected request and response formats.

How do I review generated code for consistency and test coverage?▼

Reviewing generated code for consistency involves validating deliverables against predefined quality standards, enforcing integration checks, and verifying that all task outputs include necessary error handling.

Can I use automated quality gates to check AI generated API endpoints?▼

No additional dependencies or external testing frameworks are required to validate agent deliverables, as the skill operates independently to verify correctness, consistency, and test coverage.

Do I need external testing frameworks to validate agent deliverables?▼

No additional dependencies or external testing frameworks are required to validate agent deliverables, as the skill operates independently to verify correctness, consistency, and test coverage.

When should I enforce integration checks on AI agent task outputs?▼

You should enforce integration checks on AI agent task outputs when verifying cross-agent contracts across systems like frontend, backend, storage, and auth to ensure seamless integration before final completion.