ilya-perspective

Analyze AI research questions with Ilya Sutskever-style cognitive frameworks and external verification.

1|Updated Apr 14, 2026
One-click install
npx skills add https://github.com/ChenyqThu/wentian --skill ilya-perspective
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ilya-perspective
Source: https://github.com/ChenyqThu/wentian/tree/main/experts/ilya
Command: npx skills add https://github.com/ChenyqThu/wentian --skill ilya-perspective

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides a research-grade, decision-focused cognitive lens for AI questions by embedding Ilya Sutskever's heuristics and expression DNA into responses. It helps users get disciplined judgments about model design, alignment trade-offs, and long-term strategy without superficial hype or unsupported numeric forecasts.

Core Features & Use Cases

  • Cognitive DNA: Encodes Ilya's core heuristics (compression-as-understanding, scale-as-tool, safety-capability entanglement, research aesthetics) to guide analysis.
  • Agentic Workflow: When facts matter, mandates external verification steps (websearch / citation) before making capability or timeline claims.
  • Safety-aware refusals: Uses explicit templates and epistemic hedging to decline or defer on competition-sensitive technical details.
  • Use Cases: Evaluate a new model architecture's research promise, prioritize alignment experiments, perform due-diligence on a lab's safety posture, or distill a research roadmap from mixed public signals.

Quick Start

Analyze the proposed AI research direction using Ilya Sutskever's perspective and summarize core risks, likely research priorities, and a concise recommendation.

Frequently Asked Questions about ilya-perspective

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

FAQPage Schema
How do I evaluate AI model architectures and alignment proposals for scaling and safety trade-offs?▼

To evaluate AI model architectures and alignment proposals, this analysis applies Ilya Sutskever's cognitive heuristics on compression, scaling, and generalization to deliver structured judgments on safety trade-offs and research promises.

What is compression-as-understanding in AI research evaluation?▼

Compression-as-understanding is a core heuristic that guides cognitive analysis of model designs, treating a model's ability to compress data effectively as a primary indicator of its fundamental research promise and generalization capabilities.

How to perform due-diligence on an AI lab's safety posture and research roadmap?▼

Perform due-diligence on an AI lab by distilling mixed public signals into a structured research roadmap, applying epistemic hedging and citation-backed fact checks before making any capability or timeline claims regarding safety posture.

Can I use this approach to get specific numeric forecasts for AI timelines and capabilities?▼

You cannot get specific numeric forecasts for AI timelines, as the analysis mandates epistemic hedging and uses explicit templates to decline or defer on competition-sensitive technical details and unsupported capability claims.

Does the analysis support external verification for AI safety and capability claims?▼

The analysis supports external verification for AI safety and capability claims by mandating an agentic workflow that requires websearch and citation-backed fact checks before asserting any research findings or model capabilities.

When should I not use expert-perspective cognitive analysis for AI strategy?▼

You should not use this cognitive analysis when seeking specific numeric forecasts, competition-sensitive technical details, or conclusions that bypass the required epistemic hedging and citation-backed verification workflow.