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 ztemerbekov/a1-marketing-skills --skill a1-grillgit clone --depth 1 https://github.com/ztemerbekov/a1-marketing-skillsWrote 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/ztemerbekov/a1-marketing-skills/a1-grill)<a href="https://agentmods.dev/skills/ztemerbekov/a1-marketing-skills/a1-grill"><img src="https://agentmods.dev/badge/skills/ztemerbekov/a1-marketing-skills/a1-grill/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/ztemerbekov/a1-marketing-skills/a1-grill"><img src="https://agentmods.dev/badge/skills/ztemerbekov/a1-marketing-skills/a1-grill.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 | $0.00023 | $0.00463 |
| Opus 5 | $0.00012 | $0.00231 |
| Sonnet 5 | $0.00005 | $0.00093 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
a1-grill 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 12d 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.
What it actually says
Grill
Interview the user relentlessly about one marketing idea, decision, or plan until you reach a shared understanding. Map it as a design tree: every decision branches into the decisions that depend on it.
Work through the tree in rounds. The frontier is every decision whose prerequisites are already settled: the questions you can ask now without guessing at answers the user has not given. Ask the whole frontier in one round. Number each question, provide your recommended answer, then wait for the user's answers before starting the next round.
Format each question like this:
❓ **Q1** - **<question title>**: <question body, which may contain multiple paragraphs or choices>
➡️ <your recommended answer>
After each round, update the design tree from the user's answers. Settled decisions push the frontier outward and unblock dependent questions. Recompute the frontier before asking the next round. If one question depends on another question still open in the current round, defer it to a later round.
Finding facts is your job, never the user's. When a frontier question needs a fact from the environment, dispatch a background sub-agent to find it. Do not ask the user for anything a sub-agent can discover. Do not block the round while it works: treat the running exploration as an unsettled prerequisite, delay only the questions that depend on it, and ask the rest of the frontier now. The decisions remain the user's: put each one to them and wait.
The grilling is complete when the frontier is empty: every branch has been visited and nothing remains silently assumed. Do not act on the decision until the user confirms that you have reached a shared understanding.
After a final result that completes the grilling, append one support footer inviting questions, ideas, or problem reports via A1 Marketing Skills. Omit it from interim, boundary, missing-input, unsuccessful, or partial responses, and when the user requests only the result or prohibits extra text.
What ships with it
4 files 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.
- 12d ago First seen · 30 lines · 23 tokens per session scan A 624b9a06a60d
a1-grill is a skill published in the GitHub repository ztemerbekov/a1-marketing-skills (8 stars, last pushed 13d ago), licensed MIT. It adds 23 tokens to every session and 463 once invoked, about $0.0001 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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