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-genai-agent-engineeringgit 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-genai-agent-engineering)<a href="https://agentmods.dev/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-genai-agent-engineering"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-genai-agent-engineering/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-genai-agent-engineering"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-genai-agent-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 70 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.
- medium Excessive Agency · line 104 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00086 | $0.03126 |
| Opus 5 | $0.00043 | $0.01563 |
| Sonnet 5 | $0.00017 | $0.00625 |
| Haiku 4.5 | $0.00009 | $0.00313 |
Grade A, and why
databricks-genai-agent-engineering 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
databricks-genai-agent-engineering
Purpose
This skill decides whether an agent architecture is correctly engineered on Databricks: agents are wrapped in ResponsesAgent for compatibility, retrieval indexes are correctly configured for the query patterns, context is grounded and budgeted, tools are properly scoped via Unity Catalog governance, MCP servers have clear governance categories, model providers are supported, and gateway policies align with business requirements. Sound design avoids index-sync mismatches, context starvation, tool-privilege leaks, and unsupported model providers.
When to use
- A user is designing an agent that retrieves from Databricks AI Search and needs confirmation on index variant and sync mode.
- A user is building context-grounding logic and needs to confirm chunking, budget, and assembly strategy.
- A user is integrating external tools via MCP and needs to confirm the server category and governance scope.
- A user is selecting an external model provider and needs to confirm it is supported on Databricks.
- A user is configuring Unity AI Gateway for rate limiting, cost control, or policy enforcement and needs to validate the design.
When NOT to use
- No retrieval index or tool list is stated — ask for the specific index and tool strategy before reviewing.
- The question is whether the agent's answer is correct — route to
databricks-genai-evaluation-observability-agent. - The question is about tracing and instrumentation — route to
databricks-genai-evaluation-observability-agent. - The question is about access control on the source data — route to
databricks-unity-catalog-governance-agent. - The question is about model lifecycle and serving endpoints — route to
databricks-mlops-agent. - The question is about cost from external model spend — route to
databricks-finops-cost-agent.
Scope
- Mosaic AI Agent Framework authoring patterns and ResponsesAgent interface wrapping for playground and deployment compatibility.
- Databricks AI Search index configuration: variant choice (Delta Sync Databricks-managed, Delta Sync self-managed, Direct Vector Access, full-text BETA), sync mode (continuous, triggered, manual), and query API.
- Context engineering: chunking and grounding strategy, context budget in tokens, and assembly logic.
- Tool inventory and governance: Unity Catalog functions, MCP server categories (managed, external, custom), and privilege scoping.
- External model provider selection and validation against Databricks support matrix.
- Unity AI Gateway policy: rate limiting, traffic splitting, fallbacks, budget management, content policies, and logging.
What ships with it
6 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 · 134 lines · 86 tokens per session scan A c6000bbfcc78
databricks-genai-agent-engineering is a skill published in the GitHub repository VincentChuWaiChow/vanguard-frontier-agentic (22 stars, last pushed 2d ago), licensed Apache-2.0. It adds 86 tokens to every session and 3,126 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 2026-09-04.
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