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-queriesgit 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-queries)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-queries"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-queries/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-queries"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-queries.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 | $0.00046 | $0.02643 |
| Opus 5 | $0.00023 | $0.01321 |
| Sonnet 5 | $0.00009 | $0.00529 |
| Haiku 4.5 | $0.00005 | $0.00264 |
Grade A, and why
malloy-queries 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Malloy Query Reference
Only use field names defined in the model. Ground yourself first with get_context; never invent entities or guess field names.
Tool names are written bare here -
get_context,execute_query,search_malloy_docs. The exact prefixed name depends on the host surface; match each against the tools you actually have.
Query Patterns
Simple aggregation:
run: source -> {
aggregate: total_revenue, order_count
}
Group by dimension:
run: source -> {
group_by: category
aggregate: revenue
order_by: revenue desc
limit: 10
}
Time trend:
# line_chart
run: source -> {
group_by: order_date.month
aggregate: revenue
order_by: 1
}
Filtered query:
run: source -> {
where: status = 'active'
group_by: region
aggregate: count_orders, total_revenue
}
Run a pre-built view:
run: source -> view_name
Refine a view with additional options:
run: source -> view_name + { limit: 10, where: region = 'US' }
Percent of total: use all(), not parent().
run: source -> {
group_by: category
aggregate:
revenue
pct_of_total is revenue / all(revenue)
}
Conditional dimensions with pick: pick is a keyword, not a function.
run: source -> {
group_by:
tier is pick 'Premium' when price > 100
pick 'Standard' when price > 50
else 'Budget'
aggregate: count()
}
Wrong: pick('Premium') { ... } (that's not Malloy syntax).
Window functions with calculate:: running totals, lag(), lead(), and other window operations belong in calculate:, not aggregate:.
run: source -> {
group_by: month is order_date.month
aggregate: revenue
calculate: prev_month_revenue is lag(revenue)
order_by: month
}
Field Paths and Joins
Joins are defined in the model. Never write join_one / join_many inside a query. Every query is rooted on one source, and you reach joined sources via dot notation within the query body.
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 · 275 lines · 46 tokens per session scan A 564829bc9265
malloy-queries is a skill published in the GitHub repository malloydata/publisher (100 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 2,643 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.
Other skills, from other repositories
developing-with-streamlit
Use for ALL Streamlit tasks: creating, editing, debugging, beautifying, styling, theming, optimizing, or deploying Streamlit apps. Also custom components, st.components.v2, HTML/JS/CSS work. Discovers and loads version-matched reference docs from the user's installed Streamlit (>=1.57). Triggers: streamlit, st.…
cnsplots
Create, revise, and troubleshoot publication-ready scientific plots in Python with cnsplots, including distribution, regression, heatmap, genomics, survival, set, flow, and multi-panel figures. Use when a user asks for cnsplots code, Cell/Nature/Science-style visualization, precise physical figure dimensions…
reimagine-it-extract
Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette"…
regex
Write, test, and debug regular expressions for pattern matching, search, and text extraction across programming languages. Use when the user asks to match patterns in text, validate input formats (email, URL, IP, phone, date), extract data with capture groups, build search patterns with lookahead or lookbehind, or…
reusable-visualization
Build ONE reusable chart visualization component that receives its data and its settings from the host application instead of fetching them, and declares the fields and config options the host exposes to viewers. Use this whenever a single chart component is reused across many different queries rather than built for…
lightdash-agent-slack-messaging
Use this skill when writing, designing, or generating Slack messages for Lightdash's in-app analytics agent. Triggers when someone asks to create agent update messages, Slack digests, agent notifications, weekly summaries, daily summaries, or any Slack copy for the Lightdash project agent. Also use when asked to vary…