docs/ONE-PAGER.md

Databricks Cost Leak Hunter

Finds the real-dollar cost leaks in a Databricks workspace and reports them as a dollar-ranked, CFO-grokkable fix list — computed from the customer's own billing tables.

Problem

Databricks spend leaks through four recurring configurations — clusters that never auto-terminate, scheduled jobs on All-Purpose Compute ($0.55/DBU) instead of Jobs Compute ($0.15/DBU), clusters sized for peak that idle below 25% CPU, and a ~2× Photon premium on jobs it doesn't accelerate. The bill says how much but not where or why, and DBU-denominated analysis needs an engineer to translate before a CFO can act.

Solution

A detect → compute → rank → report pipeline. SQL over the workspace's own system.billing.usage joined to system.billing.list_prices (via the Databricks CLI Statement Execution API) produces the dollars; the databricks-workspace-mcp control-plane tools corroborate the live config that explains each leak; a deterministic Python ranker does all the arithmetic; and the output is a CFO-grokkable report — a split confirmed-vs-pending-review headline, a ranked leak table with a Confidence column, and a single-config-change fix per line. A Step-1 grant-chain probe fails fast if billing access is missing.

W5

Who	CFO / FinOps owners and the data-platform engineers who answer to them
What	Audits a workspace for four named leak categories and emits a dollar-ranked FinOps report with Confirmed / Estimated / At-risk labels
When	The bill spikes, the monthly FinOps review, or any "why is my Databricks bill so high?" moment
Where	Claude Code (also Codex-compatible), against a Databricks Premium/Enterprise + Unity Catalog workspace
Why	Dollars come from the workspace's own billing rows and a deterministic ranker — never LLM guesswork — and each fix is one config change

Stack

Layer	Choice
Skill runtime	Claude Code SKILL.md (compatibility: Codex)
Dollar data plane	Databricks CLI Statement Execution API over system.billing.usage × system.billing.list_prices
Config data plane	databricks-workspace-mcp — five read-only cluster/pool/pipeline tools
Arithmetic	Deterministic Python ranker (scripts/rank-and-report.py) + jq
Knowledge	references/*.md loaded on demand (leak-category SQL, CFO format, grant-chain setup, DLT tiers)

Differentiators

1. Confirmed dollars, never estimates — every confirmed figure is computed from the customer's own system.billing.usage joined to list_prices; the two modeled categories are explicitly labeled Estimated and At-risk and never blended in.
2. The LLM never does the arithmetic — a deterministic ranker sums, ranks, and annualizes, and the headline never sums confirmed and unconfirmed dollars under one verb (a regression-critical eval criterion).
3. Every leak is one config change — the second data plane corroborates live config (auto_termination_minutes, cluster_source, autoscale floor, runtime_engine), so each report line names the exact setting to flip.