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-notebook-chatgit 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-notebook-chat)<a href="https://agentmods.dev/skills/malloydata/publisher/malloy-notebook-chat"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-notebook-chat/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-notebook-chat"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/malloy-notebook-chat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 19 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00055 | $0.00565 |
| Opus 5 | $0.00028 | $0.00282 |
| Sonnet 5 | $0.00011 | $0.00113 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
malloy-notebook-chat 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.
What it actually says
Notebook/Report Chat Workflow
Steps to follow when the user asks a question:
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.
- Interpret the user's question as being about the bound notebook/report unless they explicitly ask about something else. Pronouns and shorthand ("this", "it", "the notebook", "the report", "the data", "what's here", "summarize", "key insights", "findings", "anything interesting") all refer to the notebook above. Never respond with a clarifying question about what the user means when the referent is clearly this notebook.
- Start with a brief, natural acknowledgment that references the specific question: one sentence, varied wording.
- The notebook above IS your context. Its code cells define the queries the user cares about. For any question:
- For broad requests like "summarize", "what are the key insights", or "tell me about this notebook", run the notebook's queries via
execute_queryand synthesize the findings across them. Do NOT ask the user to be more specific. - If the question can be answered by a query already in the notebook, run that cell's query via
execute_query(exact code, or a minor variation like adding a filter or changing a group_by). - If the question asks for an analysis that is clearly NOT in the notebook (new source, different package, different domain), then, and only then, call
get_contextto explore. - Do NOT call
get_contextas a default first step. The notebook already tells you what's available.
- Before writing or modifying a query, read the
malloy-queriesskill for syntax patterns. When you tweak a query (add awhere:clause, change agroup_by, etc.), do NOT add#(filter)annotations orgiven:declarations: both live on the source's model file and are inherited by this notebook automatically. Query-levelwhere:filtering inside a cell is fine; declaring a new runtime parameter is a model change, not a chat-time change. - Summarize insights from query results. Do not echo raw rows: the user sees them rendered.
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 Changed 5054a48e030f
- 9d ago First seen · 25 lines · 55 tokens per session scan A f06221136527
malloy-notebook-chat is a skill published in the GitHub repository malloydata/publisher (100 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 565 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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