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 Kilo-Org/kilo-marketplace --skill databricks-model-servinggit clone --depth 1 https://github.com/Kilo-Org/kilo-marketplaceWrote 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/kilo-org/kilo-marketplace/databricks-model-serving)<a href="https://agentmods.dev/skills/kilo-org/kilo-marketplace/databricks-model-serving"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/databricks-model-serving/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/kilo-org/kilo-marketplace/databricks-model-serving"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/databricks-model-serving.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 | $0.00164 | $0.03243 |
| Opus 5 | $0.00082 | $0.01622 |
| Sonnet 5 | $0.00033 | $0.00649 |
| Haiku 4.5 | $0.00016 | $0.00324 |
Grade A, and why
databricks-model-serving 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Serving Endpoints
FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.
Model Serving provides managed endpoints for serving LLMs, custom ML models, and external models as scalable REST APIs. Endpoints are identified by name (unique per workspace).
Endpoint Types
| Type | When to Use | Key Detail |
|---|---|---|
| Pay-per-token | Foundation Model APIs (Llama, GPT-5, Claude, Gemini, etc.) | Uses system.ai.* catalog models, pre-provisioned in every workspace. Discover at runtime — see Foundation Model API endpoints below. |
| Provisioned throughput | Dedicated GPU capacity | Guaranteed throughput, higher cost |
| Custom model | Your own MLflow models or containers | Deploy any model with an MLflow signature |
Endpoint Structure
Serving Endpoint (top-level, identified by NAME)
├── Config
│ ├── Served Entities (model references + scaling config)
│ └── Traffic Config (routing percentages across entities)
├── AI Gateway (rate limits, usage tracking)
└── State (READY / NOT_READY, config_update status)
- Served Entities: Each entity references a model (from Unity Catalog or MLflow) with scaling parameters. Get the entity name from
served_entities[].namein thegetoutput — needed forbuild-logsandlogscommands. - Traffic Config: Routes requests across served entities by percentage (for A/B testing, canary deployments).
- State: Endpoints transition
NOT_READY→READYafter creation or config update. Poll viagetto checkstate.ready.
CLI Discovery — ALWAYS Do This First
Do NOT guess command syntax. Discover available commands and their usage dynamically:
# List all serving-endpoints subcommands
databricks serving-endpoints -h
# Get detailed usage for any subcommand (flags, args, JSON fields)
databricks serving-endpoints <subcommand> -h
Run databricks serving-endpoints -h before constructing any command. Run databricks serving-endpoints <subcommand> -h to discover exact flags, positional arguments, and JSON spec fields for that subcommand.
What ships with it
5 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 · 274 lines · 164 tokens per session scan A 8f56229db751
databricks-model-serving is a skill published in the GitHub repository Kilo-Org/kilo-marketplace (175 stars, last pushed 22d ago), licensed Apache-2.0. It adds 164 tokens to every session and 3,243 once invoked, about $0.0008 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 2026-09-03.
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