agent-native-reviewer

Review code, PRs, and architecture for agent-native action and context parity.

2|Updated Apr 14, 2026
One-click install
npx skills add https://github.com/CristoVentures/LocalCents --skill agent-native-reviewer-cristoventures
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-native-reviewer
Source: https://github.com/CristoVentures/LocalCents/tree/main/.agent/skills/agent-native-reviewer
Command: npx skills add https://github.com/CristoVentures/LocalCents --skill agent-native-reviewer-cristoventures

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reviews code, PRs, and architecture to ensure agents have the same capabilities and visibility as users, preventing gaps where automation cannot help users or vice versa.

Core Features & Use Cases

  • Enforces action parity by validating that every user action has a corresponding agent capability.
  • Ensures context parity by verifying that the agent can access the same data and state as the user.
  • Provides a structured review process and capability map to document gaps and fixes across UI, tooling, and prompts.
  • Use Case: during code review, PR review, or architecture planning to guarantee that agents can perform the same tasks in the same data space as human users.

Quick Start

Run the agent-native-reviewer on a PR to verify action parity and context parity.

Frequently Asked Questions about agent-native-reviewer

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

FAQPage Schema
How do I check agent-native parity during a code review?

To check agent-native parity during a code review, validate that every user action has a corresponding agent capability and that the agent can access the same data and state as the user.

What is agent-native parity in architecture planning?

Agent-native parity in architecture planning ensures agents have the same actions and data visibility as users across UI, tooling, prompts, and data flows, preventing automation gaps where agents cannot help users.

How do I ensure my AI agent has the same data visibility as a user?

Ensure your AI agent has the same data visibility by verifying context parity, which checks that the agent can access the exact same data and state as the human user across all relevant flows.

Can I use a structured review process to document capability gaps in prompts and tooling?

You can use a structured review process to generate a capability map that documents action and context gaps, ensuring safety-conscious prompts and primitive tool usage across your system architecture.

Does this review process work for both PRs and architectural designs?

This review process works for both PRs and architectural designs by enforcing action and context parity across UI, tooling, prompts, and data flows to guarantee agents operate in the same data space as users.