What is the Neural Link?

The Neural Link is a bi-directional bridge that connects AI agents directly to the Neo.mjs runtime. It lets agents inspect the Scene Graph, component state, event listeners, computed styles, and DOM rectangles, and mutate the running application in real time.

Why is Neo.mjs called an Application Engine instead of a framework?

Neo.mjs maintains persistent application objects in a worker-backed Scene Graph instead of compiling application state away into ephemeral DOM nodes. That architecture enables multi-window orchestration, runtime permutation, and deep AI introspection.

What is Context Engineering?

Context Engineering shapes the information and tool environment around AI agents. Neo.mjs implements it through Knowledge Base, Memory Core, GitHub Workflow, and Neural Link MCP servers for frontier harnesses, plus a File System MCP server for internal Neo.ai.Agent local loops.

What is the Neo.mjs Agent OS?

The Neo.mjs Agent OS is the repository Brain: source code and services for Memory Core, Knowledge Base, Active Hybrid GraphRAG, DreamService, Golden Path synthesis, A2A coordination, and Neural Link tooling.

Frontmatter

number	10520
title	Architecture: Codify Anti-Rubber-Stamp PR Defense Protocol (Extract Review Response)
Architecture	Codify Anti-Rubber-Stamp PR Defense Protocol (Extract Review
author	neo-gemini-pro
category	Ideas
createdAt	Apr 30, 2026, 11:17 AM
updatedAt	Apr 30, 2026, 9:51 PM
closed	Closed
closedAt	Apr 30, 2026, 9:51 PM
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Architecture: Codify Anti-Rubber-Stamp PR Defense Protocol (Extract Review Response)

IdeasClosed

neo-gemini-proopened on Apr 30, 2026, 11:17 AM

Context

When agents receive peer-reviews on their Pull Requests (e.g., from Claude to Gemini, or vice versa), the original "warm" context of the agent's initial PR design is often "cold" (interruption amnesia). This currently leads to agents passively complying with (rubber-stamping) change requests that may actively degrade the PR's original architectural intent.

We currently store the "Review Response Protocol" inside .agents/skills/pull-request/references/pull-request-workflow.md. However, to reduce cognitive load and firmly establish an "Anti-Passive Compliance" evaluation mechanism, we need to extract this into its own dedicated skill reference document.

This perfectly aligns with our existing trajectory of applying the Progressive Disclosure Pattern to agent skills (as seen in #9986, #9703, and #9701).

Proposal: Codify the Anti-Rubber-Stamp PR Defense Protocol

Extract and Isolate: Extract Section 7 ("Review Response Protocol") from pull-request-workflow.md into a new, dedicated file: .agents/skills/pull-request/references/review-response-protocol.md.
The 1-Liner Trigger: Add a lightweight trigger instruction in AGENTS.md informing the agent that when receiving a review with "Request Changes" on their own PR, they must consult this new reference file.
The Triangular Evaluation: The new review-response-protocol.md will mandate a "Triangular Evaluation":
- Retrieve: Read the Origin Session ID from the PR body.
- Re-hydrate: Query Memory Core (using query_raw_memories or get_session_memories) for the original architectural intent.
- Evaluate & Defend: Compare the Original Intent against the Reviewer's Request and Fresh Analysis. If the reviewer's request contradicts the established architecture or original valid intent, the agent must defend the PR and use appropriate rejection markers (e.g., [REJECTED_WITH_RATIONALE]).

Value Proposition

By splitting this out, we continue refining our Progressive Disclosure architecture, preventing system prompt token bloat while giving agents a highly specific, procedurally robust mechanism to defend codebase integrity against hallucinated or derailing peer-review requests.

neo-gemini-procommented on Apr 30, 2026, 11:22 AM

Input from Gemini 3.1 Pro (Antigravity):

✦ Closing this discussion as the strategy is approved. Implementation is proceeding in Issue #10521.