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 andre-salvati/databricks-template --skill data-divergencegit clone --depth 1 https://github.com/andre-salvati/databricks-templateWrote 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/andre-salvati/databricks-template/data-divergence)<a href="https://agentmods.dev/skills/andre-salvati/databricks-template/data-divergence"><img src="https://agentmods.dev/badge/skills/andre-salvati/databricks-template/data-divergence/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/andre-salvati/databricks-template/data-divergence"><img src="https://agentmods.dev/badge/skills/andre-salvati/databricks-template/data-divergence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00123 | $0.02841 |
| Opus 5.5 | $0.00049 | $0.01136 |
| Sonnet 5.5 | $0.00025 | $0.00568 |
| Haiku 4.5 | $0.00012 | $0.00284 |
Grade A, and why
data-divergence 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 1mo 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigating a data divergence
Report what happened, not just what differs.
Verify the premise first. "There's a divergence between X and Y" is a hypothesis, not a finding. Reconcile before theorizing — a good share of reports turn out to be the wrong two columns compared, and saying so plainly with numbers is a complete and useful answer.
Steps
- Establish the two sides and the grain. Name the exact objects and the key that identifies one row on each side. If the two sides have different grain — one is a rollup of the other — jump to Across an aggregation boundary; a row-count comparison is meaningless there. If the complaint came from a chart, read its query first — see Dashboards.
- Count, at every layer. One
UNION ALLdown both paths — source → each intermediate → output — withCOUNT(*),MIN/MAXof the partitioning/date column, and a distinct count of the key. Find the first layer where the two sides stop agreeing; that one query eliminates everything upstream of it. - Diff by grain, then by key (below). Stop and read the pattern before forming a theory.
- Read the Delta log for the layer that first diverged (below).
- Check run state — a missing tail is a failed job, not a logic bug. Look at job/pipeline run history and events, and compare the deployed revision against the one in source control; a fix that exists in the repo but was never shipped presents exactly like a code bug that isn't there.
- Report what is proven, what is inferred, and what the retained logs can no longer answer.
On Databricks, drive all of this with execute_sql rather than shelling out. Confirm which identity
the connection authenticates as before trusting environment isolation — an MCP server or shared
service principal may reach further than your own account does.
Diff by grain, then by key
Group both sides by the dimension the report slices on and show only the buckets that differ:
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.
- 1mo ago First seen · 197 lines · 123 tokens per session scan A 98154d7c7ecf
data-divergence is a skill published in the GitHub repository andre-salvati/databricks-template (86 stars, last pushed 19d ago), licensed Apache-2.0. It adds 123 tokens to every session and 2,841 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
data-access
Governed data access patterns for Databricks workloads at Alpura. Load when writing or reviewing data.py, SQL, Unity Catalog references, table reads, filters, or query failures. Enforces three-part names, Gold-only reads for UI apps, WorkspaceClient Statement Execution in Databricks Apps, spark.table() in notebooks…
sl
Skill "sl" from Kaelio/ktx, covering semantic layer, part 1 - schema reference, overlay sources, standalone table sources and standalone sql sources.
historic_sql_patterns
Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.
historic_sql_table_digest
Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic schema projection.
live_database_ingest
Capture semantic-layer and knowledge updates from a live database schema snapshot.
warehouse-init
Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.