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 optimizing-databricks-sqlgit 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/optimizing-databricks-sql)<a href="https://agentmods.dev/skills/altimateai/data-engineering-skills/optimizing-databricks-sql"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/optimizing-databricks-sql/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/optimizing-databricks-sql"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/optimizing-databricks-sql.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 | $0.00098 | $0.06255 |
| Opus 5.5 | $0.00039 | $0.02502 |
| Sonnet 5.5 | $0.00020 | $0.01251 |
| Haiku 4.5 | $0.00010 | $0.00626 |
Grade A, and why
optimizing-databricks-sql 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 20d 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 — 461 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Databricks Optimize (DBSQL)
A Databricks/Delta-platform overlay, not a standalone optimizer. The
generic rewrite/analyze/verify pipeline (Steps 1, 5, 6) is the same
pipeline altimate-code's native query-optimize skill runs — same tool
calls, same composition. This skill's only job is the part query-optimize
structurally can't do: detecting Databricks/Delta platform-specific issues
(Z-ordering, table statistics, Photon UDF blocking) that need table
metadata and Databricks docs knowledge, not just query text, and folding
those findings in alongside the generic ones.
If query-optimize is installed in the current environment, prefer
invoking it for Steps 1/5/6 and layer Steps 2–4's Databricks-specific
findings on top of its output, the same way sql-review defers to
sibling skills instead of duplicating them. If it isn't available (e.g.
this skill is running standalone, outside altimate-code), fall back to
calling the same tools directly as described below — the tool calls are
identical either way, so behavior doesn't change based on which path ran.
No rewrite is reported as "optimized" until it clears the validation gate in Step 7 — that gate is a hard requirement, not a suggestion.
When to use this skill
Use when:
- The query is DBSQL (standalone or embedded in a notebook cell) and the question involves Databricks/Delta-specific behavior: Z-ordering, liquid clustering, table statistics, Photon, Unity Catalog, Predictive Optimization.
Do NOT use for:
- Generic, dialect-agnostic SQL optimization with no Databricks-specific
angle → use
query-optimizedirectly. - SQL quality/safety linting unrelated to performance (injection, PII
exposure, style) → use
sql-review. - PySpark DataFrame-level code (
.collect(),.join(), UDFs used as Python objects) → deferred to a later phase, out of scope for now.
Before anything else — confirm the file path resolved correctly
Confirm the resolved file path matches what was requested, before
doing anything else. If the user gave an explicit path (a
/Workspace/... path, a repo path, any specific path), the tool used to
read or write it must resolve to that exact path — not a same-named
file found some other way. If the proper access tool fails, or the only
way to find something is a generic filename search, stop and disclose
it explicitly before analyzing or writing anything: state what path
was requested, what (if anything) was found instead and where, and ask
how to proceed. Never silently substitute a different file and continue
as if it's the same one.
What ships with it
4 files 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.
- 20d ago First seen · 461 lines · 98 tokens per session scan A ddf5474c0a25
optimizing-databricks-sql is a skill published in the GitHub repository AltimateAI/data-engineering-skills (128 stars, last pushed yesterday), licensed MIT. It adds 98 tokens to every session and 6,255 once invoked, about $0.0004 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 2026-09-26.
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