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 agentmods add skills/lwalden/aiagentminder/grillnpx skills add lwalden/AIAgentMinder --skill grillgit clone --depth 1 https://github.com/lwalden/AIAgentMinderWrote 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/lwalden/aiagentminder/grill)<a href="https://agentmods.dev/skills/lwalden/aiagentminder/grill"><img src="https://agentmods.dev/badge/skills/lwalden/aiagentminder/grill.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00719 |
| Opus 5 | $0.00005 | $0.00360 |
| Sonnet 5 | $0.00002 | $0.00144 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
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 5d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/aiagentminder:grill - Plan Interrogation
Stress-test a plan or design by walking every branch of the decision tree. This is the intensive counterpart to the Approach-First check embedded in the dev agent profile — use it when a design is non-obvious, high-stakes, or involves multiple interdependent decisions.
Step 1: Scope the Interrogation
Read the plan or design being questioned. Sources may include:
- An approach statement from the
devagent's Approach-First check - A feature description from
docs/strategy-roadmap.md - A freeform plan the user describes
- An existing design doc or PR description
Also read:
DECISIONS.md— for prior decisions that constrain the design spacedocs/strategy-roadmap.md— for scope context
Step 2: Map the Decision Tree
Identify every decision branch in the plan — any point where two or more reasonable approaches exist. Present the branches as a numbered list, noting dependencies between them.
Example:
Decision branches identified:
- Auth storage: session vs JWT
- Rate limiting: middleware vs API gateway (depends on #1)
- Error format: structured vs freeform
Step 3: Walk Each Branch
For each decision branch, one at a time:
- If the codebase can answer it — explore the code first. Check existing patterns, conventions, and constraints. Do not ask the user questions that the code can answer.
- If the user needs to decide — present:
- The options available
- Tradeoffs of each
- Reversal cost: High (hard to undo), Medium (costly but possible), or Low (easy to change later)
- Downstream dependencies: "If you choose X, then Y becomes constrained"
Resolve one branch before moving to the next. If branches have dependencies, resolve the upstream branch first.
Continue until the user says they are satisfied or all branches are resolved.
Step 4: Decision Summary
After all branches are resolved, produce a structured summary:
Grill Summary — {plan name}
Decisions made:
1. {topic}: {choice}. Alternatives: {what was considered}. Reversal cost: {H/M/L}. Rationale: {why}.
2. ...
Open questions (deferred):
- {question} — deferred because {reason}
Constraints discovered:
- {constraint found during codebase exploration}
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.
- 5d ago First seen · 92 lines · 10 tokens per session scan A a3b77e3c0607
grill is a skill published in the GitHub repository lwalden/AIAgentMinder (4 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 719 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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