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-chartsgit 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-charts)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-charts"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-charts/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-charts"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-charts.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 | $0.00057 | $0.04263 |
| Opus 5 | $0.00028 | $0.02132 |
| Sonnet 5 | $0.00011 | $0.00853 |
| Haiku 4.5 | $0.00006 | $0.00426 |
Grade A, and why
malloy-charts 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 10d 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 — 427 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chart Selection for Malloy
Malloy uses Vega-Lite under the hood.
#tags control visualization. Callsearch_malloy_docswith topic "rendering" for the full tag reference (or see https://docs.malloydata.dev/documentation/visualizations/overview).
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.
Decision Tree: Which Chart?
| Data Shape | Default Choice |
|---|---|
| Aggregates only (no group_by) | # big_value |
| 1 time column + 1 measure | # line_chart |
| 1 category + 1 measure | # bar_chart |
| 2 numeric columns | # scatter_chart |
| Geographic (US states) + 1 measure | # shape_map |
| Route data (lat/lon pairs) | # segment_map |
| Multiple perspectives | # dashboard with nest: |
| Nested query to pivot | # pivot |
| Filtered aggregates side-by-side | # flatten |
| Detailed rows | Default table (no annotation) |
| Goal | Renderer |
|---|---|
| Compare categories | # bar_chart (sort by value, limit ~15) |
| Show composition | # bar_chart.stack |
| Trend over time | # line_chart |
| Highlight KPIs | # big_value with # label |
| Correlation | # scatter_chart |
| Compare dimensions | # dashboard (nest chart views) |
| Before/after | # transpose or # pivot |
| Multiple metrics per category | Default table, # flatten, or y=['a','b'] |
Constraints:
- ONE aggregate per chart view (charts render only the first; use
y=['a','b']for multi-measure) - No fixed scale on measure definitions: use
# currencynot# currency=usd0m - One tag per line
- Alias joined fields in
group_bybeforeorder_by - Define measures in source, not in views
Chart Types
# bar_chart
Data shape: group_by = x-axis, aggregate = y-axis, optional 2nd group_by = series.
# bar_chart
view: by_carrier is { group_by: carrier, aggregate: flight_count, order_by: flight_count desc, limit: 10 }
# bar_chart.stack
view: by_region is { group_by: category, region, aggregate: revenue }
# bar_chart { y=['revenue','cost'] }
view: rev_vs_cost is { group_by: category, aggregate: revenue, cost }
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.
- 10d ago First seen · 427 lines · 57 tokens per session scan A a6fb070efb2a
malloy-charts is a skill published in the GitHub repository malloydata/publisher (100 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 4,263 once invoked, about $0.0003 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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