MCPInstall

matt-grilling

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Installs
228
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/learnprompt/laoyeye --skill matt-grilling
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads matt-grilling automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Interview the user relentlessly until you reach a shared understanding. Map this 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 a round like so:

❓ **Q1** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>

➡️ <your recommended answer>

---

❓ **Q2** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>

➡️ <your recommended answer>

Each round the user 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 (filesystem, tools, etc.), dispatch a sub-agent to find it; don't ask the user for anything you could look up yourself. Don't block on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait for the sub-agent to report; 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 of the design tree visited, nothing left silently assumed. Do not act on it until the user confirms you have reached a shared understanding.

FAQ

What does the matt-grilling skill do?

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

How do I install matt-grilling?

Run npx skills add https://github.com/learnprompt/laoyeye --skill matt-grilling in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/matt-grilling/ (personal) or .claude/skills/matt-grilling/ (project).

Which AI agents can use matt-grilling?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is matt-grilling safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains matt-grilling?

It is published by learnprompt in learnprompt/laoyeye. It has 228 recorded installs.

More from learnprompt/laoyeye

View all →

steelman-both-sides

learnprompt/laoyeye

双向钢人论证提示语原文。用户在两个选项之间犹豫不决、需要做决策时使用。触发词包括 钢人、双向钢人、steelman、两个选项选哪个、帮我做个决定、正反两面都说最强理由、我该选 A 还是 B。产出两边的最强论证、真正分歧、关键变量,再问一个最可能改变结论的问题。

230 installs

laoyeye

learnprompt/laoyeye

Open the skill to read its SKILL.md instructions.

230 installs

minimum-experiment

learnprompt/laoyeye

最小实验提示语原文。用户纠结的事情靠继续想已经不会更清楚、需要用现实反馈来验证时使用。触发词包括 最小实验、先试试、怎么验证、别空想了、跑个实验、小成本试错、七天验证、已经反复纠结过很多轮、想了很久还是选不出来。产出待验证假设、一个低成本可逆实验的完整设计,以及明天就能做的第一个动作。

229 installs

reverse-teardown

learnprompt/laoyeye

反向拆解提示语原文。用户看到一个优秀的产品、网页、方案、流程、数据看板或作品,想学会它为什么有效时使用。触发词包括 反向拆解、拆解这个、它为什么做得好、我想模仿、逆向拆解、拆解这个好范例、这个牛在哪。产出可复用规律加一份操作清单。只拆做得好的,项目黄了、上线炸了这种失败复盘不用它。

229 installs

fact-check

learnprompt/laoyeye

Open the skill to read its SKILL.md instructions.

229 installs

johari-window

learnprompt/laoyeye

乔哈里视窗协作提示语原文。用户想设定 AI 的协作方式、抱怨回答不贴需求、嫌追问太多或不问就猜、希望先判断信息状态再回应时使用。触发词包括 乔哈里、视窗、开放区、盲区、隐藏区、未知区、协作模式、别猜我的需求、先搞清楚再答。整段挂在会话或任务开头,约束之后的全部协作。

228 installs

Popular testing & qa skills

View category →

tdd

mattpocock/skills

Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.

Testing & QA1.2M installs

grilling

mattpocock/skills

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Testing & QA996.2K installs

migrate-to-shoehorn

mattpocock/skills

Migrate test files from as type assertions to @total-typescript/shoehorn. Use when user mentions shoehorn, wants to replace as in tests, or needs partial test data.

Testing & QA484.1K installs

run-train

lllllllama/rigorpilot-skills

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized trainoutputs/. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Testing & QA337.8K installs

test-driven-development

obra/superpowers

Use when implementing any feature or bugfix, before writing implementation code

Testing & QA261.8K installs

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

Testing & QA216.8K installs