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 malloydata/publisher --skill malloy-model-as-you-gogit clone --depth 1 https://github.com/malloydata/publisherWrote 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/malloydata/publisher/malloy-model-as-you-go)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-model-as-you-go"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-model-as-you-go/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/malloydata/publisher/malloy-model-as-you-go"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-model-as-you-go.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.03875 |
| Opus 5 | $0.00016 | $0.01937 |
| Sonnet 5 | $0.00006 | $0.00775 |
| Haiku 4.5 | $0.00003 | $0.00387 |
Grade A, and why
malloy-model-as-you-go 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.
How it starts
The opening of the file, as written. The whole thing — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model as you go
An analysis that lives in a chat transcript is not reproducible. The numbers were right, and six weeks later nobody can say what "revenue" excluded, which of four timestamps was the order date, or whether the last period was complete. The work is unauditable, so it gets redone.
The fix is to write the assumptions down while the query is still in front of you, in the most durable place your session can write. Answer the question, codify what the answer assumed, answer the next one. After a handful of questions there is a model, or a notebook, where every definition exists because a real question needed it, and every judgment call is on the record.
This skill is the codify step. skill:malloy-analysis answers the question; this skill
decides what to write down afterwards, and where.
Tool names are bare here -
get_context,execute_query,search_database_schema. The exact prefixed name depends on the host; match against the tools you actually have.
The loop
QUESTION → ANSWER → CODIFY ⟲
↑___________|
The user gets a real answer on question one. If three tool calls have gone by without producing an insight they can read, you have drifted into modelling for its own sake. Stop and answer something.
1. Answer the question, with skill:malloy-analysis
This skill starts when someone asks a data question. That question is the unit of work, and it is theirs. Do not widen it into a modelling project, and do not swap it for a more interesting one you found on the way.
A broad ask is still an ask. "Analyse the sales data" is a question whose subject is given and whose metric is not. Pick the most obvious question about that subject, say in one line which one you picked, and answer it. Never open with a row-count table and a menu of "analytical domains"; the menu is worth less than the first real answer would have been.
Load skill:malloy-analysis and follow it: discover the model, construct the query, run it,
verify it, present it. Two of its rules matter most here:
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 · 306 lines · 32 tokens per session scan A 6cf84c4c9100
malloy-model-as-you-go is a skill published in the GitHub repository malloydata/publisher (101 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 3,875 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 2026-08-30.
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