grill-ai-mastery
OfficialAI interview mastery through collaborative tips.
Software Engineering#ai#observability#interview#loop-closure#tip-vocabulary#entity-referencing#harness-improvement
AuthorOutlineDriven
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: grill-ai-mastery Download link: https://github.com/OutlineDriven/odin-gemini-cli-extension/archive/main.zip#grill-ai-mastery Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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