grilling

grilling is a skill for Claude Code, Codex from toverux/cantrips. It costs 46 tokens per session (1,001 once invoked), scanned A, original, MIT.

A structured interview for stress-testing a plan, decision, or idea before writing a specification. It asks questions in rounds, follows dependencies between decisions, and records each question with a recommendation.

In plain words
What is it for?
Use it to examine product plans, technical decisions, or project ideas with many connected trade-offs. It is also suited to clarifying requirements before implementation.
Why use it?
It exposes missing requirements and hidden choices instead of filling them in with guesses. This helps reach shared understanding when a project is still unclear.

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; mentions Claude Code.

Part of the cantrips plugin — 25 skills, 1 agent 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/toverux/cantrips/grilling
Any agent
npx skills add toverux/cantrips --skill grilling
Clone the repo
git clone --depth 1 https://github.com/toverux/cantrips

Made for: Claude Code, Codex.

Or install cantrips, the plugin that ships this one along with the rest of its 25 skills, 1 agent.

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/toverux/cantrips/grilling.svg)](https://agentmods.dev/skills/toverux/cantrips/grilling)
Your own site
<a href="https://agentmods.dev/skills/toverux/cantrips/grilling"><img src="https://agentmods.dev/badge/skills/toverux/cantrips/grilling.svg" alt="Measured on agentmods" height="20"></a>
Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,001 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 $0.00046 $0.01001
Opus 5 $0.00023 $0.00500
Sonnet 5 $0.00009 $0.00200
Haiku 4.5 $0.00005 $0.00100

Measured today against content hash 9e6aa935c577, 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 today.

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/grilling/SKILL.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 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 this as a decision 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. Number sequentially across the whole interview rather than restarting each round, so an answer naming Q2 points at one question. Then wait for the user's answers before the next round.

Each question should be formatted like so:

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

➡️ <your recommended answer>

A presented question is locked: title, body, options and recommendation stay word-for-word as first shown, and only the user's own feedback can reshape or retire it. Presenting is therefore a commitment — put a question to the user only once you are sure nothing still running can change what you'd ask.

Every message of the interview ends with its questions: newly ready ones in full in the format above, under fresh numbers, then one line naming the numbers still open from earlier messages. The lock is what makes that line safe — a number keeps pointing at the exact text first shown, so scrolling to it beats reprinting it. Anything above the questions — findings, replies to the user's remarks — is commentary.

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.

When the user's answers carry a question or remark of their own, answer it before opening the next round; never let a new round bury the reply.

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 — in the background where the harness supports it (Claude Code: do not use run_in_background: false) — rather than asking the user for anything you could look up yourself. Don't block on it: a running exploration is an unsettled prerequisite — ask the rest of the frontier now, and hold every question the report could reword. A question is downstream of an exploration when its body, options, or recommendation would read differently depending on what comes back — a body that mentions the pending result has declared itself downstream; when in doubt, hold it, since a held question costs one round and a locked question overtaken by facts costs the user's trust in every question still on the table. When an exploration reports, give its findings one short paragraph and fold them into the questions it unblocks; a finding that bears on a locked question goes in that paragraph as commentary for the user to weigh, and the question stands as asked. When the fact lives in external docs or specs rather than the environment, propose /research instead. The decisions are the user's — put each to them and wait.

Closing

The session is done when the frontier is empty: every branch of the decision tree visited, nothing left silently assumed. Where another skill invoked this interview, hand back to it there — the close belongs to whichever skill owns the flow. Otherwise close with a single sentence stating that shared understanding is reached — no summary — then recommend the next step as a flow pointer (read flow-pointers.md for the format) and wait for the user's go-ahead:

  • /spec (user-invoked) when the outcome is a feature worth a written contract — in this session, since it synthesizes the interview; implementation then starts fresh.
  • /implement (user-invoked) directly when it's a small fix that needs no spec — in this session, which already carries the context.
  • /prototype when an unresolved question survived the interview and needs empirical evidence — in this session, feeding the verdict back here.
  • /research when an unresolved question needs facts from primary sources — it runs in the background while this session continues.
  • /questionnaire (user-invoked) when an unresolved question needs facts only another person holds — in this session, drafting the document; the answers come back on the recipient's clock.

Read the full file on GitHub · 44 lines

Files

What ships with it

1 file 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. today Changed 9e6aa935c577
  2. 5d ago First seen · 44 lines · 46 tokens per session scan A 869af70a204e

Subscribe to this mod's changes

grilling is a skill published in the GitHub repository toverux/cantrips (2 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 1,001 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 2026-08-31.

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