grill-ai-mastery
Hybrid interview that probes AI-engineering mastery by tip-vocabulary depth — entity referencing, loop closure, observability, harness improvement — not by token usage or LOC. Start collaborative (two-way tip exchange), escalate to adversarial probing when depth is lacking. Trigger when the user says "interview me on AI", "stress-test my Claude usage", "evaluate this candidate's AI engineering", or otherwise asks for an AI-collab skill assessment. User-only — never auto-invoke.
npx skills add majiayu000/claude-skill-registry --skill grill-ai-mastery --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
Probe AI mastery by what the subject *names*, not by how much they *generate*. The premise from the chat that prompted this skill: token usage and LOC are noise; concrete tip vocabulary (URL-as-entity-ref, loop closure, observability) is signal. ## Mode disambiguation | Skill | Anchor | Posture | | ------------------------ | ---------------------------------------------- | ----------------------------------------------- | | **`grill-ai-mastery`** | AI-collab tip vocabulary tree (this file) | Hybrid: collaborative → adversarial | | `grill-me` | Any plan/design under test | Linear adversarial, recommendation per question | | `request-refactor-plan` | A refactor in particular | Adversarial interview specific to refactoring | This skill is the AI-mastery anchor; `grill-me` is the domain-agnostic version. Pick by what's being assessed. ## Phase 1 — Collaborative tip-sharing Open by asking the subject to *name* a tip they actually use when collaborating with an LLM. Two-way: surface one of yours back as a counter-tip. The exchange is the assessment, not a quiz. Watch for: - Concrete protocol names (URL-as-entity-ref, AGENTS.md, MCP resources, structured outputs) versus generic platitudes
- Mode disambiguation
- Phase 1 — Collaborative tip-sharing
- Phase 2 — Adversarial probe (escalation)
- AskUserQuestion tool contract (Claude Code reference)
- Antipattern: override-checklist UI [LOAD-BEARING]
- Stop conditions
- Anti-patterns to flag in the subject
- Tab-complete note
What does the grill-ai-mastery skill do?
Hybrid interview that probes AI-engineering mastery by tip-vocabulary depth — entity referencing, loop closure, observability, harness improvement — not by token usage or LOC. Start collaborative (two-way tip exchange), escalate to adversarial probing when depth is lacking. Trigger when the user says "interview me on AI", "stress-test my Claude usage", "evaluate this candidate's AI engineering", or otherwise asks for an AI-collab skill assessment. User-only — never auto-invoke.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill grill-ai-mastery --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.