grilling

grilling is a skill for Claude Code, Codex from mrclrchtr/supi. It costs 38 tokens per session (621 once invoked), scanned A, original, MIT.

A guided questioning method for examining a plan, decision, or idea. It asks about each unresolved choice in stages until you and the agent share an understanding.

In plain words
What is it for?
Use it to stress-test plans, compare options, and work through dependent decisions one round of questions at a time.
Why use it?
It exposes assumptions and unanswered decisions before they cause problems. You can use it when you want your thinking challenged in detail.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present. Also seen: mentions subagents.

Part of the mattpocock-skills plugin — 25 skills shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/mrclrchtr/supi/grilling
Any agent
npx skills add mrclrchtr/supi --skill grilling
Clone the repo
git clone --depth 1 https://github.com/mrclrchtr/supi

Made for: Claude Code, Codex.

Or install mattpocock-skills, the plugin that ships this one along with the rest of its 25 skills.

Wrote 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.

agentmods badge for grilling

README.md
[![agentmods](https://agentmods.dev/badge/skills/mrclrchtr/supi/grilling.svg)](https://agentmods.dev/skills/mrclrchtr/supi/grilling)
Your own site
<a href="https://agentmods.dev/skills/mrclrchtr/supi/grilling"><img src="https://agentmods.dev/badge/skills/mrclrchtr/supi/grilling.svg" alt="Measured on agentmods" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 621 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.00621
Opus 5 $0.00019 $0.00311
Sonnet 5 $0.00008 $0.00124
Haiku 4.5 $0.00004 $0.00062

Measured 6d ago against content hash 5859ffd22734, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

grilling 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 6d 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.

skills/productivity/grilling/SKILL.md · 28 lines

What it actually says

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.

Use ask_user for each round. Put all frontier questions in one form. Fill the fields as follows:

  • title: Name the subject and the round.
  • intro: Summarize what is settled and why this frontier is open.
  • questions: Include the current frontier, up to the form limit of 10. If the frontier is larger, ask the first 10 and recompute it from the answers.
  • id: Use the question number, such as Q1.
  • header: Start with the question number and add a title.
  • prompt: State the decision and the context that the user needs.
  • type: Use choice when the answer set is known. Use text only for an open answer.
  • options: For a choice question, use stable value ids, concise label text, and a brief description. Set multi to true only when the user can select more than one option.
  • details: Explain trade-offs or consequences for a choice option. It can also contain a sketch.
  • recommendation: For a choice question, use the recommended option value, or an array of values when multi is true. For a text question, give the recommended answer. Also give text questions a placeholder that shows the expected answer shape.

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.

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 6d ago First seen · 28 lines · 38 tokens per session scan A 5859ffd22734

Subscribe to this mod's changes

grilling is a skill published in the GitHub repository mrclrchtr/supi (86 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 621 once invoked, about $0.0002 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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