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 AltimateAI/data-engineering-skills --skill snowflake-query-optimizationgit clone --depth 1 https://github.com/AltimateAI/data-engineering-skillsWrote 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/data-engineering-skills/snowflake-query-optimization)<a href="https://agentmods.dev/skills/altimateai/data-engineering-skills/snowflake-query-optimization"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/snowflake-query-optimization/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/data-engineering-skills/snowflake-query-optimization"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/snowflake-query-optimization.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 | $0.00221 | $0.05449 |
| Opus 5.5 | $0.00088 | $0.02180 |
| Sonnet 5.5 | $0.00044 | $0.01090 |
| Haiku 4.5 | $0.00022 | $0.00545 |
Grade A, and why
snowflake-query-optimization 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 today.
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 — 499 lines — stays where its author put it; the contents beside it link to each section on GitHub.
snowflake-query-optimization
Input (one of) → fetch SQL → optimize → parity-validate → benchmark → report.
Delegates warehouse work to altimate-code when available; falls back
to direct Snowflake SQL otherwise. Does not reinvent query-profile parsing.
When NOT to use this skill
- Pasted SQL text, no parity needed →
optimizing-query-text. - Quick rewrite from a
query_id, no re-run or parity →optimizing-query-by-id. - Finding candidate queries to optimize →
finding-expensive-queries. - Refactoring a dbt model →
snowflake-dbt-model-refactoring.
Prerequisites
Before invoking, verify what's actually available — do not fabricate results from file reads alone.
command -v altimate-code # preferred delegate
command -v snowsql # fallback client (any Snowflake client works)
Must have (one of):
altimate-codeinstalled and configured with a Snowflake connection, OR- a working Snowflake client (
snowsql, dbt adapter, Python withsnowflake-connector-python, etc.) and credentials for the account.
Grants required on the Snowflake role:
SELECTonSNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY(preferred), or the ability to runINFORMATION_SCHEMA.QUERY_HISTORY()(fallback).USAGEon the warehouse referenced by the baseline query (needed to re-run it for comparison).
If neither path is available, stop and tell the user.
Time budget
Target end-to-end runtime: ≤5 min (300s), hard ceiling 6 min (360s), excluding Phase 4 parity + Phase 5 benchmark re-runs, which scale with the baseline query's cost and are exempt from the cap. A query being optimized is often slow by nature — forcing parity into a 90s window would push the agent to skip the mandatory hard stop.
Per-phase caps (sum ≤ 300s, parity/benchmark separate): Phase 1 ≤30s · Phase 2 ≤45s · Phase 3 ≤75s · Phase 4 overhead ≤60s (plus re-run time) · Phase 5 overhead ≤30s (plus one re-run of the optimized query). If a non-parity phase exceeds its budget, stop the extra work and move on.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 499 lines · 221 tokens per session scan A 4f86a04e144f
snowflake-query-optimization is a skill published in the GitHub repository AltimateAI/data-engineering-skills (130 stars, last pushed yesterday), licensed MIT. It adds 221 tokens to every session and 5,449 once invoked, about $0.0009 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 2026-10-28.
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