grill-me

grill-me is a skill for Claude Code, Codex from chinkan/RustFox. It costs 14 tokens per session (35 once invoked), scanned A, a copy of grill-me, MIT.

A structured interview that repeatedly challenges a plan or design with questions.

In plain words
What is it for?
Stress-testing project plans, product designs, technical approaches, and other decisions.
Why use it?
It exposes unclear assumptions, missing details, and weak decisions before implementation begins.

Skill for Claude CodeCodex

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/chinkan/rustfox/grill-me
Any agent
npx skills add chinkan/RustFox --skill grill-me
Clone the repo
git clone --depth 1 https://github.com/chinkan/RustFox

Made for: Claude Code, Codex.

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 grill-me

README.md
[![agentmods](https://agentmods.dev/badge/skills/chinkan/rustfox/grill-me.svg)](https://agentmods.dev/skills/chinkan/rustfox/grill-me)
Your own site
<a href="https://agentmods.dev/skills/chinkan/rustfox/grill-me"><img src="https://agentmods.dev/badge/skills/chinkan/rustfox/grill-me.svg" alt="Measured on agentmods" height="20"></a>
Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 35 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00014 $0.00035
Opus 5 $0.00007 $0.00017
Sonnet 5 $0.00003 $0.00007
Haiku 4.5 $0.00001 $0.00003

Measured 5d ago against content hash 6189dfceb730, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 5d 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.

Origin

This is a copy

100% identical to grill-me — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/grill-me/SKILL.md · 8 lines

What it actually says

Run a /grilling session.

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. 5d ago First seen · 8 lines · 14 tokens per session scan A 6189dfceb730

Subscribe to this mod's changes

grill-me is a skill published in the GitHub repository chinkan/RustFox (7 stars, last pushed 29d ago), licensed MIT. It adds 14 tokens to every session and 35 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to grill-me, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

Vector Databases

Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.

agentic-in/elephant-agent · 26 tokens

release

Cut a versioned release and publish everos to PyPI via the tag-triggered workflow.

EverMind-AI/EverOS · 20 tokens

esm

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…

synthetic-sciences/openscience · 86 tokens

agentfield-use

Whenever you have a discrete task to perform — one the user delegated, or one that arose inside your own work — check FIRST whether an installed AgentField agent covers it, and offload to it by default when one does. Coverage, not task size, is the test: even a small job goes to a covering agent. The check is cheap …

Agent-Field/agentfield · 259 tokens

parse-document

Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.

superlinked/sie · 64 tokens

neuron-structured-output

Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…

neuron-core/neuron-ai · 104 tokens