Borrowing it
Nothing to install: this file belongs to AltimateAI/altimate-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AltimateAI/altimate-code/main/.opencode/skills/data-question/SKILL.mdgit clone --depth 1 https://github.com/AltimateAI/altimate-codeWrote 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/altimateai/altimate-code/data-question)<a href="https://agentmods.dev/skills/altimateai/altimate-code/data-question"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/data-question/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/altimateai/altimate-code/data-question"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/data-question.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00025 | $0.03296 |
| Opus 5.5 | $0.00010 | $0.01318 |
| Sonnet 5.5 | $0.00005 | $0.00659 |
| Haiku 4.5 | $0.00003 | $0.00330 |
Grade A, and why
data-question 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 4d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Question
Use the available read-only tools to answer the user's question. Source databases and project files stay unchanged; when exposed, sql_materialize may create session-local tables in the scratch warehouse. All examples below use synthetic tables. Adapt names and dialects after inspection. This skill is self-contained.
Workflow
- Inspect. Use
warehouse_list,schema_inspectand smallsql_executequeries as needed. Inspect actual keys and text before choosing joins, casts or parsing rules: schemas alone hide whitespace, case, prefixes and mixed formats. Preserve meaningful leading zeros; check that normalization does not merge distinct identities. - Plan. State the population, row grain, denominator, units and material assumptions. Decide which joins and aggregation levels preserve them. Keep this brief; a trivial lookup does not need a checklist or several validation scans.
- Compute in-engine. Filter and aggregate close to the source. Derive large intermediate sets with joins/CTEs or the scratch bridge below, rather than copying rows into
IN (...)orVALUES. Small user-supplied constants are fine. Keep LIMIT, sampling and approximate functions exploratory unless the user requests an estimate, or an exact answer would need per-row model labeling of a large population (see "Batch semantic labels, then count"); then say the answer is an estimate. For exact final answers, aggregate the full intended population in SQL. - Verify relevant invariants. Check key uniqueness, before/after join counts, missing matches and null share when they affect the answer. Use
sql_checkfor these invariants and extraction coverage; for cross-warehouse comparisons, first materialize both sides intoscratchwithsql_materialize. Reconcile components with totals and preserve weighting.SUM(group_mean * contributing_count) / SUM(contributing_count)recovers the overall mean for disjoint, complete groups using unrounded means and counts of the same non-null measure;AVG(group_mean)gives each group equal weight.sql_analyzeis optional. If available, a read-onlydata-explorerviataskcan independently re-derive a difficult result. - Answer. Explain the basis in one or two sentences (population, grain, caveats), retaining useful tables or explanation before it. Finish with
**Answer:** <requested value(s), with units>. If evidence or available tools are insufficient, finish with**Answer:** unable to determine — <why>. Never guess a missing result.
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.
- 4d ago Changed cc45c44b4151
- 5d ago Changed cedf0ab2dc19
- 6d ago First seen · 193 lines · 25 tokens per session scan A 03b81c629cdb
data-question is a skill published in the GitHub repository AltimateAI/altimate-code (813 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 3,296 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-17.
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