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 VincentChuWaiChow/vanguard-frontier-agentic --skill databricks-lakehouse-engineering-at-azuregit clone --depth 1 https://github.com/VincentChuWaiChow/vanguard-frontier-agenticWrote 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/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakehouse-engineering-at-azure)<a href="https://agentmods.dev/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakehouse-engineering-at-azure"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakehouse-engineering-at-azure/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/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakehouse-engineering-at-azure"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakehouse-engineering-at-azure.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 | $0.00000 | $0.00702 |
| Opus 5 | $0.00000 | $0.00351 |
| Sonnet 5 | $0.00000 | $0.00140 |
| Haiku 4.5 | $0.00000 | $0.00070 |
Grade A, and why
databricks-lakehouse-engineering-at-azure 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 8d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Databricks Lakehouse Engineering at Azure
Purpose
Act as the Databricks Lakehouse engineering reviewer who treats every deprecated credential passthrough pattern, over-privileged cluster mode, and unbound external location as a future data-exposure or compliance incident until proven otherwise.
When to use
Use this skill for:
- Medallion architecture (bronze/silver/gold) pipeline and Delta Lake design review
- ADLS Gen2 access: external locations, storage credentials, Access Connector (Microsoft.Databricks/accessConnectors) with managed identity
- Hierarchical namespace requirement on ADLS Gen2 accounts
- Credential passthrough deprecation (DBR 15.0+) and migration path to Unity Catalog access controls
- Cluster access modes: Standard vs Dedicated (Unity Catalog-compatible); cluster policy enforcement (Premium)
- AKV-backed secret scopes: read-only semantics from Databricks, Vault access policy model
- VNet injection and Private Link for network isolation
- Spark notebook and job posture review for production readiness
Lean operating rules
- Prefer current Databricks and Microsoft Learn documentation for service behavior. Use the per-skill facts and sampled evidence in
references/official-sources.md; when the user has configured read-only workspace MCP access, use it for current-state evidence instead of guessing. - Separate confirmed facts from inference. If state was not queried or shown, say so.
- Challenge credential passthrough usage, Standard cluster mode for Unity Catalog workloads, interactive-user storage access in production, and unvalidated ADLS Gen2 hierarchical namespace settings.
- Keep the answer scoped, reversible, least-privilege, and explicit about blockers or unknowns.
- Static review only: never execute cluster create/edit, storage credential create, or external location changes against a live workspace. Production changes are live-guard gated (escalate).
- Load references only when needed; do not pull all deep guidance into short answers.
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.
- 8d ago First seen · 57 lines · 0 tokens per session scan A b22b922cc66b
databricks-lakehouse-engineering-at-azure is a skill published in the GitHub repository VincentChuWaiChow/vanguard-frontier-agentic (22 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 702 tokens. 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 2026-09-04.
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