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-phrase-detectiongit 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-phrase-detection)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-phrase-detection"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-phrase-detection/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-phrase-detection"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-phrase-detection.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 | $0.00043 | $0.01517 |
| Opus 5 | $0.00022 | $0.00758 |
| Sonnet 5 | $0.00009 | $0.00303 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
malloy-phrase-detection 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 yesterday.
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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Target Construction for get_context
The get_context tool description defines each field and what a call returns. This skill focuses on the parts you won't get right by default: classifying concepts into target types and splitting ambiguous phrases.
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.
Scope of this skill: the patterns below build dimension / measure / view targets. Phrasing for source targets is covered at the end.
A note on matching: get_context searches over the model (sources, fields, views, and their descriptions), not the distinct categorical values stored in the data. To find which literal values a categorical dimension holds, target the dimension, then query its distinct values with execute_query (see the patterns below).
Authoring search_text for entity targets
Write search_text as a brief semantic description of what you're looking for, not an echo of the user's word. This applies even when you already know the entity name from a prior result: still describe it, don't just repeat the name.
One target per concept is enough: the tool handles phrasing variants internally. Don't pile up dimension targets that point at the same field. Use multiple targets only when they describe genuinely distinct concepts (see "Non-obvious decomposition patterns" below).
Target-type decision guide
dimension: categorical attribute to group, filter, or join on. Also used for time and numeric fields.- "region" becomes
"the geographic region"
- "region" becomes
measure: aggregation metric (count, sum, average, rate).- "total revenue" becomes
"the total revenue or sales amount"
- "total revenue" becomes
view: pre-built analysis. Include one whenever the question sounds like a canned report (summary, breakdown, top-N, trend).- "sales summary" becomes
"a summary of sales metrics"
- "sales summary" becomes
source: data domain, for a question that names a subject area rather than fields (phrasing below).
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.
- yesterday Changed · +2 lines 0919aba0877f
- 10d ago First seen · 81 lines · 43 tokens per session scan A 0676c2b079ec
malloy-phrase-detection is a skill published in the GitHub repository malloydata/publisher (100 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,517 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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