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/ertra/pm-brain-for-cursor/query-datasetsnpx skills add ertra/pm-brain-for-cursor --skill query-datasetsgit clone --depth 1 https://github.com/ertra/pm-brain-for-cursorWrote 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/ertra/pm-brain-for-cursor/query-datasets)<a href="https://agentmods.dev/skills/ertra/pm-brain-for-cursor/query-datasets"><img src="https://agentmods.dev/badge/skills/ertra/pm-brain-for-cursor/query-datasets.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 | $0.00091 | $0.02828 |
| Opus 5 | $0.00046 | $0.01414 |
| Sonnet 5 | $0.00018 | $0.00566 |
| Haiku 4.5 | $0.00009 | $0.00283 |
Grade A, and why
query-datasets 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
query-datasets
Answer questions about what's in 03-datasets/ using the local indexes at 03-datasets/_indexes/*.db.
The repo ships a small, fictional sample dataset under 03-datasets/ plus
the self-contained tooling under 03-datasets/_tools/. Use this skill for
demos, walkthroughs, and questions of the form "do any support tickets /
calls in the sample mention X?". The skill is read-only.
When this skill applies
- "Do support tickets mention conflicts with the Gmail extension?"
- "Did any prospect on a call ask for an audit log UI?"
- "Is there a ticket about timezone / DST handling?"
- Any factual lookup against
03-datasets/.
When it does NOT apply
- General PM workflow questions not tied to the datasets.
- Requests to modify / add / delete tickets. This skill is read-only.
- Questions spanning multiple corpora at once — ask the user which one instead.
Preconditions
03-datasets/_indexes/<corpus>.dbmust exist and be complete. The file exists ANDSELECT value FROM meta WHERE key='build_complete_at'returns non-empty.- Local Python env must be ready to load the same sentence-transformers model recorded in
meta.modelwhen using hybrid or vector mode. - Python venv at
.venv/with dependencies installed (pip install -r requirements.txt). - If the indexes do not exist yet, build them first:
First run downloadssource .venv/bin/activate python 03-datasets/_tools/build_index.py --corpus support_tickets \ --provider sentence-transformers --model nomic-ai/nomic-embed-text-v1.5 python 03-datasets/_tools/build_index.py --corpus call_transcripts \ --provider sentence-transformers --model nomic-ai/nomic-embed-text-v1.5nomic-ai/nomic-embed-text-v1.5from Hugging Face (~500 MB).
If any of these fail, tell the user what's missing and how to fix it — don't invent results.
Checklist
Do these steps in order. Create TodoWrite items for them when the question is non-trivial.
- Identify the single target corpus from the user's phrasing. Signals:
- "support tickets" / "Zendesk" / "help desk" / "customer support" / "ticket #NNN" →
support_tickets - "calls" / "recordings" / "transcripts" / "what did the prospect say" →
call_transcripts
- "support tickets" / "Zendesk" / "help desk" / "customer support" / "ticket #NNN" →
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 · 165 lines · 91 tokens per session scan A 51d1b7f4bcbd
query-datasets is a skill published in the GitHub repository ertra/pm-brain-for-cursor (22 stars, last pushed 3mo ago), licensed MIT. It adds 91 tokens to every session and 2,828 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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