manager-review

Validate user responses against the original query and arsenal skills.

6|1|Updated Mar 5, 2026

Search Tags:#automation #workflow #verification #ai-assistant #checks #quality-review #response-guard

Ready to run

npx skills add https://github.com/pwv-vc/agentcribs-community --skill manager-review-pwv-vc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: manager-review
Source: https://github.com/pwv-vc/agentcribs-community/tree/main/resources/tearsheets/arsenal/dot-claude/skills/manager-review
Command: npx skills add https://github.com/pwv-vc/agentcribs-community --skill manager-review-pwv-vc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a mandatory quality gate that validates the proposed user response against the original query, checks proper use of arsenal skills, and promotes iterative improvement to reduce errors.

Core Features & Use Cases

Pre-response validation against the user query to ensure alignment
Checks for correct usage of arsenal skills and evidence quality
Iterative refinement workflow when confidence is low
Enforces a final approval before delivering the user response

Quick Start

Before replying to a user, run the manager-review workflow to validate your draft and iterate until approved.

Frequently Asked Questions about manager-review

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

FAQPage Schema

How do I enforce a mandatory quality review before an AI assistant replies?▼

You enforce a mandatory quality review by implementing a response validation gate that checks draft replies against the original query and deployed skills. This workflow enforces iterative refinement and explicit approval before delivering final output.

How does automated response verification against original queries work?▼

Automated response verification applies a quality gate that validates proposed replies against the original user query and checks proper use of arsenal skills. It enforces iterative refinement and requires evidence-backed validation before final output delivery.

Can I use iterative refinement to improve AI response accuracy when confidence is low?▼

Yes, you can use iterative refinement to improve response accuracy when confidence is low. The quality review workflow triggers refinement cycles, enforcing evidence-backed validation and explicit approval tokens before delivering the final response to the user.

What is the best way to automate quality checks for AI assistant workflows?▼

The best way to automate quality checks is deploying a mandatory validation gate across the entire response workflow. This enforces adherence to common mistake tables, requires evidence-backed validation, and mandates explicit approval tokens before final output.

Do I need deployed arsenal skills to run a pre-response quality gate?▼

Yes, deployed arsenal skills are required. The quality gate validates proposed user responses by checking proper use of these arsenal skills, ensuring evidence quality, and enforcing adherence to the common mistakes table before explicit approval.

Why does my AI response workflow need an approval token before final output?▼

Your AI response workflow needs an approval token to enforce a mandatory quality gate. This ensures the draft response has passed evidence-backed validation against the original query and arsenal skills, preventing unverified or low-confidence outputs from being delivered.