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 twaldin/flt --skill grill-with-docsgit clone --depth 1 https://github.com/twaldin/fltWrote 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/twaldin/flt/grill-with-docs)<a href="https://agentmods.dev/skills/twaldin/flt/grill-with-docs"><img src="https://agentmods.dev/badge/skills/twaldin/flt/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/twaldin/flt/grill-with-docs"><img src="https://agentmods.dev/badge/skills/twaldin/flt/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 | $0.00057 | $0.01281 |
| Opus 5.5 | $0.00023 | $0.00512 |
| Sonnet 5.5 | $0.00011 | $0.00256 |
| Haiku 4.5 | $0.00006 | $0.00128 |
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 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.
Ask the questions one at a time, waiting for feedback on each question before continuing.
If a question can be answered by exploring the codebase, explore the codebase instead.
How to ask questions
Dispatch rule (check at runtime):
- If
fltis on PATH AND~/.flt/exists, useflt ask human '<json>'via the Bash tool. This persists the Q/A under~/.flt/qna/so the mutator/GEPA loop can train on it. The JSON shape is identical toAskUserQuestion's (1 question per call here — see batch sizing below — with 2-4 options,multiSelect, optionalpreviewfor single-select). Block on the bash call until the human answers; the command prints the answer JSON to stdout. - Otherwise fall back to the native
AskUserQuestiontool. Same JSON shape.
Either way, never dump prose with "Q1/Q2/Q3" sections — that's less readable than the interactive picker.
flt ask human payload shape:
{"questions":[
{"id":"q-3a4f","header":"Auth","question":"Which approach?","multiSelect":false,
"options":[
{"label":"OAuth+JWT","description":"Standard but heavier"},
{"label":"Sessions","description":"Simpler, same-origin only"}
]}
]}
The id field is required for flt ask human (random short slug, agent-provided). For native AskUserQuestion the runtime supplies the id implicitly — you don't need to write one.
Batch sizing for this skill: one question per call. The <what-to-do> block above mandates serial questioning so the user's answer can shape the next branch of the design tree. Don't batch multiple questions even when the underlying primitive supports it.
For each question:
- Be specific. Reference actual code, types, and functions you found during exploration — not "how should errors be handled?" but "if
cancelOrderis called on an Order with no remaining items, should it throw or no-op?" - Pre-populate options — faster for the user to pick than to invent from scratch
- If you have a recommendation, make it the first option and label it "(Recommended)"
- Use the
previewfield on options when mockups/code/configs help visual comparison (single-select only)
Only fall back to prose if the decision genuinely can't be reduced to discrete options.
What ships with it
2 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.
- 1mo ago First seen · 125 lines · 57 tokens per session scan A c31fc2adb520
grill-with-docs is a skill published in the GitHub repository twaldin/flt (5 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,281 once invoked, about $0.0002 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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