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 tsai09495/matt-pocock-engineering --skill grill-with-docsgit clone --depth 1 https://github.com/tsai09495/matt-pocock-engineeringWrote 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/tsai09495/matt-pocock-engineering/grill-with-docs)<a href="https://agentmods.dev/skills/tsai09495/matt-pocock-engineering/grill-with-docs"><img src="https://agentmods.dev/badge/skills/tsai09495/matt-pocock-engineering/grill-with-docs/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/tsai09495/matt-pocock-engineering/grill-with-docs"><img src="https://agentmods.dev/badge/skills/tsai09495/matt-pocock-engineering/grill-with-docs.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 | $0.00041 | $0.00967 |
| Opus 5 | $0.00020 | $0.00483 |
| Sonnet 5 | $0.00008 | $0.00193 |
| Haiku 4.5 | $0.00004 | $0.00097 |
Grade A, and why
grill-with-docs 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grill with Docs
Interrogate a plan until the user and agent share a precise, evidence-backed understanding. This is a discussion and documentation workflow, not implementation authorization.
Read quality-baseline.md before claiming the discussion or resulting documentation is complete.
Operating loop
- Build a decision tree from the goal, constraints, unknowns, prerequisites, and consequences.
- Classify each unresolved unknown as a fact or a decision.
- Resolve available facts with evidence before asking the user to decide anything downstream of them.
- Compute the frontier: every decision whose prerequisites are settled and which can be answered without guessing at another open answer.
- Ask one frontier round of 1–4 independent questions, then wait for the user's answers.
- Recompute the tree and frontier after each round. Continue until no material branch remains silently assumed.
Use stable numbering and this compact shape for each question:
❓ **Q1 — <title>**
<question and, when useful, concise options>
➡️ **Recommendation:** <recommended answer and main tradeoff>
Ask only one question in a round when its answer will reshape most of the tree, the decision is high-risk or unusually complex, the explanation is long, or the user asks for one-question-at-a-time interaction. Never ask a question in the current round if its answer depends on another question still open in that round.
Do not overwhelm the user with the whole decision tree or mechanically fill a four-question quota. Question count follows the frontier, not a target.
Facts versus decisions
Fact
A fact can be established from the repository, existing documentation, runtime evidence, or an authoritative external source. Examples include current behavior, an existing interface, a schema constraint, or whether an API supports a feature.
Investigate facts directly when access is available. Report the evidence and any remaining uncertainty. Do not ask the user to decide what the code already proves. Use the current agent by default; background or parallel investigation remains an optional enhancement that requires approval.
What ships with it
3 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.
- 9d ago First seen · 96 lines · 41 tokens per session scan A 1edc7ec56f52
grill-with-docs is a skill published in the GitHub repository tsai09495/matt-pocock-engineering (2 stars, last pushed 14d ago), licensed MIT. It adds 41 tokens to every session and 967 once invoked, about $0.0002 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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