Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/d-padmanabhan/agent-engineering-handbooknpx agentmods add skills/d-padmanabhan/agent-engineering-handbook/databricksWrote 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/d-padmanabhan/agent-engineering-handbook/databricks)<a href="https://agentmods.dev/skills/d-padmanabhan/agent-engineering-handbook/databricks"><img src="https://agentmods.dev/badge/skills/d-padmanabhan/agent-engineering-handbook/databricks/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/d-padmanabhan/agent-engineering-handbook/databricks"><img src="https://agentmods.dev/badge/skills/d-padmanabhan/agent-engineering-handbook/databricks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 34 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 48 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 409 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 534 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00094 | $0.05623 |
| Opus 5.5 | $0.00038 | $0.02249 |
| Sonnet 5 | $0.00019 | $0.01125 |
| Haiku 4.5 | $0.00009 | $0.00562 |
Grade A, and why
databricks 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 — 562 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Databricks - Playbook
Companion rule: 481-databricks.mdc. This skill turns those patterns into end-to-end workflows.
[!NOTE] Current Databricks names: Lakeflow Spark Declarative Pipelines is the current name for Delta Live Tables (DLT). Declarative Automation Bundles is the current name for Databricks Asset Bundles (DABs). Existing DLT/DAB code and docs still work, but new guidance should use the current names and mention the former names for searchability.
When to invoke
Use when the user is:
- Bootstrapping a new Databricks workspace or account
- Migrating to Unity Catalog (from Hive metastore or no-governance state)
- Designing or reviewing a Lakeflow Spark Declarative Pipeline (formerly DLT)
- Deploying jobs, notebooks, and pipelines with Declarative Automation Bundles
- Setting cluster policies or rolling out Serverless / SQL Warehouses
- Tuning cost (cluster sizing, Photon, spot, job compute vs all-purpose)
- Governing data (Unity Catalog, tags, access control, lineage)
- Recovering from a bad change (Delta time travel /
RESTORE) - Hardening access (SCIM, SSO, service principals, OAuth tokens)
Golden Rules
- Unity Catalog is the default. Hive metastore is for legacy migration only.
- Service principals and OAuth, not personal access tokens, for automation.
- Standard access mode by default. Dedicated access mode only when the workload requires it; no-isolation shared is not acceptable for governed production.
- Job compute for jobs. All-purpose clusters are for exploration.
- Delta is the default table format; Delta Lake for Spark, Delta tables under Unity Catalog.
- Lineage and tags are not optional - govern at ingest, not post hoc.
- Every notebook / repo change runs in CI before it runs against prod data.
- Declarative Automation Bundles deploy projects; Terraform provisions account/workspace infrastructure.
- System tables are operational infrastructure for cost, audit, lineage, and query history.
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.
- 20d ago First seen · 562 lines · 94 tokens per session scan A cab0dd5b2c6f
databricks is a skill published in the GitHub repository d-padmanabhan/agent-engineering-handbook (17 stars, last pushed 2d ago), licensed MIT. It adds 94 tokens to every session and 5,623 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 2026-09-03.
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