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/malloydata/publisher/malloy-documentnpx skills add malloydata/publisher --skill malloy-documentgit 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-document)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-document"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-document.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 | $0.00094 | $0.02447 |
| Opus 5 | $0.00047 | $0.01223 |
| Sonnet 5 | $0.00019 | $0.00489 |
| Haiku 4.5 | $0.00009 | $0.00245 |
Grade A, and why
malloy-document 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documenting a Malloy Model
Add #(doc) tags to describe sources and fields in plain language so they are easy to find and understand:
| Tag | Purpose | Goes on |
|---|---|---|
#(doc) |
Plain-language description for natural-language search | source, dimension, measure, view, join |
#(filter) |
Deprecated, prefer given:. Parameterizable filter (runtime/modeling concern, see malloy-model) |
source |
#(doc) is a standard Malloy annotation. It documents a field or source with a human-readable description that downstream tools can surface and search against.
#(doc) Tag
Add before any source, dimension, measure, view, or join. When multiple fields share a keyword, use it once as a block header. Tags and field names are indented under the keyword; tags go on the line(s) directly above the field they annotate.
Tag ordering (when a field has multiple tags): #(doc) → render tags (# currency, # label, etc.) → field name. Separate each field group with a blank line:
#(doc) Customer who placed the order
join_one: users with user_id
dimension:
#(doc) Date the order was placed (UTC)
order_date is created_at::date
measure:
#(doc) Total revenue from all orders in USD
# currency
revenue is sum(total)
Writing Doc Strings for Retrieval
Doc strings power natural-language search: users type plain-English questions and the system matches against your #(doc) strings. Write descriptions that match how analysts would search:
- Include business meaning, not code mechanics: what it represents, not how it's implemented
- Include units (USD, count, percentage): a unit is part of what a number means. For counts, name the unit being counted and say whether it counts distinct entities or events: "total students enrolled" on a subject×term-grain measure counts enrolments, not students, and a student taking four subjects counts four times. If the model cannot answer the distinct-entity version, say so in the doc.
- List a categorical field's values only while the list stays short (roughly ten or fewer). A handful of values makes a description concrete; past that, say what the field captures instead, because the dump crowds out the meaning and goes stale the moment someone adds a value. Treat ten as a rule of thumb, not a hard cap.
- Avoid Malloy jargon: never use "filterable", "groupable", "dimension", "measure", "aggregation"
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 · 191 lines · 94 tokens per session scan A 2279369d365b
malloy-document is a skill published in the GitHub repository malloydata/publisher (99 stars, last pushed today), licensed MIT. It adds 94 tokens to every session and 2,447 once invoked, about $0.0005 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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