Nx is a tool for managing monorepos, which are repositories containing multiple related projects, across TypeScript and other languages. It helps development teams and AI agents run only affected tasks, cache build results, generate code, and coordinate continuous integration. The catalogue add-ons provide agent skills, commands, agents, instructions, and settings for working with Nx.
Borrowing it
Nothing to install: this file belongs to nrwl/nx. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nrwl/nx/master/.claude/skills/grill-me/SKILL.mdgit clone --depth 1 https://github.com/nrwl/nxWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nrwl/nx/grill-me)<a href="https://agentmods.dev/skills/nrwl/nx/grill-me"><img src="https://agentmods.dev/badge/skills/nrwl/nx/grill-me.svg" alt="Measured on agentmods" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 21 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.01058 |
| Opus 5 | $0.00035 | $0.00529 |
| Sonnet 5 | $0.00014 | $0.00212 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
grill-me scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview the user relentlessly until you reach a shared understanding. Map the subject as a design tree: every decision branches into the decisions that hang off it.
Work the tree in rounds. The frontier is every decision whose prerequisites are already settled — the questions you can ask now without guessing at answers you haven't heard yet. Ask the whole frontier in one round: number each question and give your recommended answer. Then wait for the user's answers before the next round.
Format each question like so:
❓ **Q1** - **<question title>**: <question body, may be several paragraphs, including any choices>
➡️ <your recommended answer>
Each round of answers reshapes the tree — settled decisions push the frontier outward and unblock questions that depended on them. Recompute the frontier and ask the next round. A question whose answer depends on another question still open in this round belongs to a later round, not this one.
Finding facts is your job, never the user's. When a frontier question needs a fact from the environment, dispatch a sub-agent to find it — never ask the user for something you could look up. Don't block on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait: ask the rest of the frontier now. The decisions are the user's — put each to them and wait.
The session is done when the frontier is empty: every branch visited, nothing left silently assumed. Do not act on the outcome until the user confirms you have reached shared understanding.
Two rules that hold regardless of caller
- Never answer your own questions. If a round goes unanswered, stop and leave the subject exactly as it was. A grill that supplies the user's answers manufactures agreement that was never given, and every downstream step that trusts the result inherits the fabrication. Silence means stop, not proceed with the obvious answer.
- Skip what is already settled. A question the caller's own material answers is noise. The point is resolving genuine uncertainty, not walking a checklist.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 47 lines · 70 tokens per session scan A 331203299ef8
grill-me is a skill published in the GitHub repository nrwl/nx (29,318 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 1,058 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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e2e-reviewer
Use when reviewing Playwright or Cypress E2E specs, Page Objects (POM), PRs, pull requests, patches, diffs, or changed test files — asked to review tests, audit test quality, or find weak, flaky, or silently-passing tests; when tests pass CI but prove nothing or miss bugs; when auditing missing awaits, vacuous or…