Context

The Neo organism needs an external sensory organ to ingest SOTA AI and Web UI trends. Following the ideation in Discussion #10296 and the subsequent cross-family review, this skill formalizes a "Clean Room" abstraction pattern to extract industry friction points without stealing code.

The Problem

We need to systematically scan for engine-category innovations (e.g., new ECMAScript features like native type support, SharedWorker paradigms, high-performance zero-allocation techniques) without importing framework-category bias (e.g., React Server Components, hydration logic) which clash with Neo's multi-threaded, VDOM-in-Worker architecture. Furthermore, we must ensure these ingested trends cannot propagate subtle framing biases into our architecture or trigger security risks.

The Architectural Reality

This skill integrates deeply into the organism. It serves as an external sensory input to the right hemisphere (Agent OS / Dream Pipeline), feeding macro-trends into the Native Edge Graph. Simultaneously, it informs the left hemisphere (UI / Core runtime). The skill operates as a hybrid: a daemon for continuous macro-trend monitoring and manual invocation for targeted ideation. The ArchitectureOverview.md and DreamPipeline.md define its operational ecosystem.

The Fix

Create the industry-friction-radar skill using the Progressive Disclosure pattern (SKILL.md router and references/industry-friction-radar-workflow.md payload). The payload will enforce:

• Step 1 (Ingestion): Scan the web for SOTA engine-category trends, filtering out framework-category noise.
• Step 2 (Friction Extraction): Abstract the underlying "Why" (friction point) into a strict structured JSON schema. This JSON must include an explicit citations array for provenance attribution.
• Process Boundary: The output of Step 2 is the ONLY input allowed for Step 3. The raw external context is deliberately dropped.
• Step 3 (Native Ideation): A separate agent or distinct prompt step consumes the JSON projection to propose a Neo-native solution via the ideation-sandbox skill.

Acceptance Criteria

• SKILL.md is created as a lightweight router.
• references/industry-friction-radar-workflow.md contains the strict 3-step abstraction protocol.
• Protocol explicitly filters for engine-category (Unreal/Godot equivalent) over framework-category trends.
• Protocol mandates structured JSON extraction with a citations array (Attribution).
• Protocol mandates a process-boundary isolation (JSON projection) before Step 3.

Out of Scope

• Actually running the radar continuously (this ticket only creates the skill).
• Modifying the Dream Pipeline source code to ingest the JSON (a separate integration ticket).

Related

• Discussion #10296
• Discussion #10119
• Epic #10291

Origin Session ID: c378ce08-1317-4411-94de-968ad42d540c