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.
npx skills add jrobelia/inventree-plugin-ai-toolkit --skill batch-grill-megit clone --depth 1 https://github.com/jrobelia/inventree-plugin-ai-toolkitWrote 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/jrobelia/inventree-plugin-ai-toolkit/batch-grill-me)<a href="https://agentmods.dev/skills/jrobelia/inventree-plugin-ai-toolkit/batch-grill-me"><img src="https://agentmods.dev/badge/skills/jrobelia/inventree-plugin-ai-toolkit/batch-grill-me/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jrobelia/inventree-plugin-ai-toolkit/batch-grill-me"><img src="https://agentmods.dev/badge/skills/jrobelia/inventree-plugin-ai-toolkit/batch-grill-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00020 | $0.00333 |
| Opus 5 | $0.00010 | $0.00167 |
| Sonnet 5 | $0.00004 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
batch-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.
This is a copy
86% identical to grilling — 25 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.
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.
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.
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.
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 · 16 lines · 20 tokens per session scan A 7ca3ecc5c021
batch-grill-me is a skill published in the GitHub repository jrobelia/inventree-plugin-ai-toolkit (1 stars, last pushed 22d ago), licensed MIT. It adds 20 tokens to every session and 333 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to grilling, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
dotnet-testing-advanced-tunit-advanced
A guide for advanced TUnit testing in .NET. TUnit is a testing framework; the guide covers tests driven by data, dependency injection, integration tests, retries, time limits, and filtering.
dotnet-testing-autodata-xunit-integration
A guide for using AutoFixture, a .NET library that creates test data, with xUnit, a .NET testing framework. It explains attributes such as AutoData, InlineAutoData, and MemberAutoData for supplying values to parameterized tests.
dotnet-testing-autofixture-nsubstitute-integration
A .NET testing guide for using AutoFixture and NSubstitute to automatically create test data and substitute versions of dependencies.
dotnet-testing-advanced-aspire-testing
A guide to testing .NET Aspire distributed applications, which are programs made of several coordinated services. It covers tests that run the real service setup through an AppHost, the project that describes how those services are arranged.
dotnet-testing-advanced
A guide that routes advanced .NET testing questions to the right specialist instructions. It covers API and integration tests, database and other container-based tests, microservices, .NET Aspire, and related testing tools.
dotnet-testing-awesome-assertions-guide
A guide to AwesomeAssertions, a .NET library for writing readable checks in automated tests. It covers checks for objects, collections, strings, numbers, exceptions, and asynchronous code.