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galatanovidiu
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team

Delegate code and design reviews to AI teammates with verified findings.

2|1|Updated Apr 20, 2026
Search Tags:#verification#provenance#code-review#prompting#team#ai-teammate#provider-coordination
One-click install
npx skills add https://github.com/galatanovidiu/hyper7 --skill team-galatanovidiu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: team
Source: https://github.com/galatanovidiu/hyper7/tree/main/skills/team
Command: npx skills add https://github.com/galatanovidiu/hyper7 --skill team-galatanovidiu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Delegates AI-assisted teammate reviews and ensures verified outcomes by cross-checking findings against the source code before presenting results to the user.

Core Features & Use Cases

  • On-demand AI teammate reviews: Initiates adversarial review workflows for code or design changes, with the lead orchestrating providers and validating results.
  • Verification-first outputs: Every teammate finding is checked against the repository; no automatic fixes are applied without explicit user approval.
  • Provider-agnostic coordination: Works across multiple providers, preserving provenance and enabling parallel assessments when needed.

Quick Start

Provide a goal and a provider to trigger a teammate review.

Frequently Asked Questions about team

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

FAQPage Schema
How do I run an AI code review that cross-checks findings against the repository?▼

To run an AI code review, delegate the task to an AI teammate reviewer. The lead orchestrates the providers, verifies all findings against the source code, and presents the confirmed results without applying automatic fixes.

What is adversarial AI teammate review and how does it work?▼

Adversarial AI teammate review is a process where an AI reviewer evaluates code or design changes. The system orchestrates multiple providers to assess the task in parallel and validates every finding against the codebase before showing the results.

Can I use multiple AI providers for parallel code assessments?▼

Yes, you can use multiple AI providers for parallel code assessments. The system is provider-agnostic, preserving provenance across different models while coordinating parallel assessments when needed.

Does the AI teammate automatically apply code fixes after a review?▼

No, the AI teammate does not automatically apply code fixes. It requires explicit user confirmation for actions, ensuring no automatic fixes are applied without your direct approval after completing the verification process.

How do I maintain provenance and traceability for AI-assisted design reviews?▼

You maintain provenance and traceability for AI-assisted design reviews by using the built-in verification system. It saves raw outputs and verified artifacts, enforcing provenance tracking throughout the provider orchestration workflow.

What are the limitations of using an on-demand AI teammate for research tasks?▼

A limitation of using an on-demand AI teammate for research tasks is that all findings are strictly checked against the codebase. This verification-first approach means results lacking direct source code provenance will be filtered out.

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