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 kylehuirevvision/databricks-cost-optimizer --skill optimize-databricks-costsgit clone --depth 1 https://github.com/kylehuirevvision/databricks-cost-optimizerWrote 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/kylehuirevvision/databricks-cost-optimizer/optimize-databricks-costs)<a href="https://agentmods.dev/skills/kylehuirevvision/databricks-cost-optimizer/optimize-databricks-costs"><img src="https://agentmods.dev/badge/skills/kylehuirevvision/databricks-cost-optimizer/optimize-databricks-costs.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 5.1 | $0.00084 | $0.01368 |
| Opus 5 | $0.00042 | $0.00684 |
| Sonnet 5 | $0.00017 | $0.00274 |
| Haiku 4.5 | $0.00008 | $0.00137 |
Grade A, and why
optimize-databricks-costs 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 7d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Databricks Costs
Run a read-only cost audit first, convert evidence into an impact-aware proposal, and mutate Databricks only after the user approves the exact affected workloads and impacts.
Operating rules
- Default to read-only commands and SQL.
- Do not edit, stop, delete, pause, resize, retag, revoke, or create anything during an audit or proposal.
- Treat generated reports as private. Never commit hosts, IDs, emails, query text, secrets, or customer object names.
- Price usage with
system.billing.list_prices.pricing.effective_list.default. Call it effective list cost, not invoice cost. - Include retractions and restatements when summing billing usage; they correct earlier records.
- Exclude the current day and an ingestion-lag window from baseline comparisons. Inspect partial recent days separately.
- Attribute cost to resources and
identity_metadata, then prove the underlying workload before describing a user or service principal. - Prefer reversible changes. Record the before state and rollback command before every approved mutation.
Phase 1: collect evidence
Confirm the Databricks CLI profile and current identity. List accessible warehouses, Apps, Jobs, pipelines, and clusters without changing them.
Resolve the bundled collector relative to this SKILL.md, then run it from any working directory:
python3 <skill-dir>/scripts/audit.py \
--warehouse-id <warehouse-used-to-run-system-table-queries> \
--recent-days 14 \
--output-dir <private-report-directory>
Use --profile <name> for a non-default Databricks CLI profile. Use --dry-run to inspect the SQL without connecting.
If the script cannot access a system table, retain the successful evidence, report the permission gap, and continue with the accessible inventory. Do not broaden permissions during the audit.
Read audit-methodology.md when interpreting the output. Read system-tables.md when adapting a query to the available Databricks schema.
What ships with it
7 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.
- 7d ago First seen · 111 lines · 84 tokens per session scan A b73021b2e4bb
optimize-databricks-costs is a skill published in the GitHub repository kylehuirevvision/databricks-cost-optimizer (5 stars, last pushed 28d ago), licensed MIT. It adds 84 tokens to every session and 1,368 once invoked, about $0.0004 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 2026-08-31.
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