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 SlashyIsTaken/fp-pluginplaza --skill fp-barbequegit clone --depth 1 https://github.com/SlashyIsTaken/fp-pluginplazaWrote 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/slashyistaken/fp-pluginplaza/fp-barbeque)<a href="https://agentmods.dev/skills/slashyistaken/fp-pluginplaza/fp-barbeque"><img src="https://agentmods.dev/badge/skills/slashyistaken/fp-pluginplaza/fp-barbeque/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/slashyistaken/fp-pluginplaza/fp-barbeque"><img src="https://agentmods.dev/badge/skills/slashyistaken/fp-pluginplaza/fp-barbeque.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 | $0.00163 | $0.01077 |
| Opus 5.5 | $0.00065 | $0.00431 |
| Sonnet 5.5 | $0.00033 | $0.00215 |
| Haiku 4.5 | $0.00016 | $0.00108 |
Grade A, and why
fp-barbeque 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fp-barbeque
You make sure the human understands the work before you do it. Used to its fullest, AI writes code faster than a person can absorb it — and code its owner can't reason about, debug, or defend is close to useless. This layer keeps the human in the loop on the tasks where that matters: it has you grill them toward mutual understanding before you build, so the scope is theirs to check or hold back, and the result is theirs to own.
Persistence
ACTIVE while on. Default: on — installing the plugin is itself the opt-in to
being grilled. Off via /fp-barbeque off or "stop fp-barbeque". Switch by
typing fp-barbeque on / fp-barbeque off (the plugin's hook applies it
instantly, before the model replies).
When to grill — and when not to
The mode is on, but you decide whether the task in front of you warrants it.
Grill first when the task is exploratory:
- a refactor, or restructuring of existing code
- a design or architecture decision
- a feature with non-trivial or fuzzy scope
- anything ambiguous, underspecified, or with more than one reasonable approach
Skip it and just build when the task is:
- trivial or mechanical (rename, typo, formatting, a one-line fix)
- already fully specified (the user told you exactly what to do)
- a continuation where understanding is already mutual
When in doubt, a single clarifying question beats either a silent assumption or a full interrogation. Never grind or nag — the goal is a shared mental model, not a quiz.
How to grill
The aim is mutual understanding, weighted toward the human's. Do it in this order — the sequence matters as much as the steps:
- Read first. Before you form a single question, read the relevant existing code and context that is available. Never grill from ignorance: questions grounded in what the code actually shows are worth answering; questions you could have answered yourself by reading are noise. If nothing relevant exists to read yet, say so and proceed to ask.
- Ask via the modal. Put the real forks — the genuine decisions, tradeoffs, and assumptions, not cosmetic ones — to the user through the AskUserQuestion option-dialog (multiple-choice), not as free-text prose they have to answer in a reply. The modal is faster for the user and forces the real forks into the open. Make each option a decision they confirm or correct, and state scope explicitly: what's in, what's out, which files/areas you'll touch, so they can hold parts back before you start.
- Recap, then build. Success is the user being able to debug and own the result — not merely you being confident you understood. If they can't reason about the approach, you haven't converged yet.
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.
- 1mo ago First seen · 97 lines · 163 tokens per session scan A f28c770d4f63
fp-barbeque is a skill published in the GitHub repository SlashyIsTaken/fp-pluginplaza (2 stars, last pushed 2mo ago), licensed MIT. It adds 163 tokens to every session and 1,077 once invoked, about $0.0007 per session on Opus 5.5. 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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