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 tt-a1i/matt-skills-with-to-goal --skill grillinggit clone --depth 1 https://github.com/tt-a1i/matt-skills-with-to-goalWrote 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/tt-a1i/matt-skills-with-to-goal/grilling)<a href="https://agentmods.dev/skills/tt-a1i/matt-skills-with-to-goal/grilling"><img src="https://agentmods.dev/badge/skills/tt-a1i/matt-skills-with-to-goal/grilling/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/tt-a1i/matt-skills-with-to-goal/grilling"><img src="https://agentmods.dev/badge/skills/tt-a1i/matt-skills-with-to-goal/grilling.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 | $0.00038 | $0.00769 |
| Opus 5 | $0.00019 | $0.00385 |
| Sonnet 5 | $0.00008 | $0.00154 |
| Haiku 4.5 | $0.00004 | $0.00077 |
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 13d 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
88% identical to grilling — 52 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.
How it starts
The opening of the file, as written. The whole thing — 63 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 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.
Question format
Lead each round with a fixed header, then emit every frontier question in one shape. Use emoji as structure anchors and question-type signals — not decoration in every sentence.
Round header:
🔥 **Round N** · K questions
Each question (one type emoji before the number):
<type> **QN** - **<question title>**
<body — prose and/or multiple choices>
💡 <your recommended answer>
Round footer (after the last question):
👇 Reply by number: `1) … 2) … 3) …`
Type emoji (pick one that fits the decision)
| Type | Emoji | Use when |
|---|---|---|
| Scope / in-or-out | 🎯 | Boundary, YAGNI, what ships now |
| Trade-off | ⚖️ | A vs B, pick one path |
| Risk / failure | ⚠️ | What breaks, abuse cases, rollback |
| Naming / language | 🏷️ | Terms, glossary, how we say it |
| UX / UI | 👀 | What someone sees or clicks |
| Data / state | 🧩 | Model shape, transitions, schema |
| Cost / perf | ⚡ | Latency, money, scale |
| Auth / trust | 🔐 | Permissions, tenancy, secrets |
| Timing | ⏱️ | When, sequencing, milestones |
| Challenge | 🪞 | "Do you actually need this?" |
| Dependency | 🔗 | Blockers, prerequisites |
| Open / foggy | 💭 | Still ill-formed; need a direction |
Fixed skeleton (do not invent alternatives each round): 🔥 round · type emoji + QN · 💡 recommendation · 👇 reply prompt.
Keep the body text clean — at most one optional mood emoji in the round header line if it helps (e.g. a long session), never a spray of emoji inside the question body.
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.
- 13d ago First seen · 63 lines · 38 tokens per session scan A a9276819519e
grilling is a skill published in the GitHub repository tt-a1i/matt-skills-with-to-goal (160 stars, last pushed 15d ago), licensed MIT. It adds 38 tokens to every session and 769 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to grilling, differing in 52 lines, and is treated as a copy.
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