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grill-ai-mastery

Evaluate AI-collaboration mastery through concrete tip vocabulary and durable references.

5|2|Updated Nov 17, 2025
Search Tags:#ai#observability#interview#loop-closure#tip-vocabulary#entity-referencing#harness-improvement
One-click install
npx skills add https://github.com/OutlineDriven/odin-gemini-cli-extension --skill grill-ai-mastery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grill-ai-mastery
Source: https://github.com/OutlineDriven/odin-gemini-cli-extension/tree/main/skills/grill-ai-mastery
Command: npx skills add https://github.com/OutlineDriven/odin-gemini-cli-extension --skill grill-ai-mastery

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps evaluators assess AI-engineering expertise by testing concrete tip vocabulary (e.g., URL-as-entity-ref, MCP resources, structured outputs) and by focusing on durable references and loop mechanics rather than token usage or lines of code.

Core Features & Use Cases

  • Collaborative tip-sharing: Start with a two-way tip exchange to calibrate depth and establish a reference tree.
  • Adversarial probing: Escalate questions when depth, specificity, or protocols are lacking, using a structured tip-vocabulary hierarchy.
  • Durable reference handling: Emphasize how to anchor sessions to URLs, PR conversations, or documented artifacts for session continuity.
  • Harness improvement: Detect and address when the interviewing harness cannot close loops, and outline improvement paths.

Quick Start

Ask the subject to name a concrete tip they actually use when collaborating with an LLM to begin the interview.

Frequently Asked Questions about grill-ai-mastery

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

FAQPage Schema
What is an AI interview mastery evaluation based on tip vocabulary?▼

It evaluates AI-engineering expertise by testing concrete tip vocabulary like URL-as-entity-ref and structured outputs, focusing on durable references and loop mechanics instead of token generation or lines of code.

How do I start an AI collaboration interview to assess tip vocabulary?▼

Begin an AI collaboration interview by asking the subject to name a concrete tip they actually use when collaborating with an LLM, which calibrates depth and establishes a reference tree.

How does adversarial probing work during an AI-collaboration interview?▼

Adversarial probing escalates questions when depth, specificity, or protocols are lacking, utilizing a structured tip-vocabulary hierarchy to evaluate entity referencing and loop closure capabilities.

How do you handle durable references in an AI collaboration interview?▼

Durable reference handling in AI interviews emphasizes anchoring sessions to URLs, PR conversations, or documented artifacts to evaluate entity referencing and ensure session continuity.

When should I use a harness improvement interview approach for AI collaboration?▼

Apply harness improvement techniques when the interviewing harness cannot close loops, detecting limitations and outlining structured improvement paths for loop closure and entity referencing protocols.

Do I need a frontmatter description to evaluate AI-collaboration mastery?▼

Yes, AI-collaboration mastery evaluation enforces activation constraints requiring a frontmatter name and description with clearly defined phases, ensuring no automatic model invocation occurs.

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