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 gemini-cli-extensions/data-agent-kit-starter-pack --skill federate-lakehouse-cataloggit clone --depth 1 https://github.com/gemini-cli-extensions/data-agent-kit-starter-packWrote 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/gemini-cli-extensions/data-agent-kit-starter-pack/federate-lakehouse-catalog)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/federate-lakehouse-catalog"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/federate-lakehouse-catalog/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/gemini-cli-extensions/data-agent-kit-starter-pack/federate-lakehouse-catalog"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/federate-lakehouse-catalog.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 62 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 83 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 | $0.00149 | $0.02568 |
| Opus 5 | $0.00075 | $0.01284 |
| Sonnet 5 | $0.00030 | $0.00514 |
| Haiku 4.5 | $0.00015 | $0.00257 |
Grade A, and why
federate-lakehouse-catalog 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 10d 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federate Lakehouse Catalog via Cross-cloud Lakehouse
This skill describes how to set up a federated catalog in BigQuery to query remote catalogs like Databricks Unity Catalog or AWS Glue Data Catalog data in AWS over the public internet.
Prerequisites
- For Databricks: Databricks Workspace URL and OAuth Service Principal (Client ID and Secret) with read access.
- For AWS Glue: AWS Administrator access to create IAM roles and permissions policies.
- Active Google Cloud project with administrative access to create lakehouse resources, and secrets in the case of Databricks.
Procedure
Step 1: Information Gathering and Region Selection
Before running any commands, the agent MUST collect the following information from the user:
- Determine which catalog the user wants to federate to (e.g., Databricks Unity or AWS Glue) and verify it is supported.
- Determine where the remote data is located (the specific AWS region).
- Using the Region Pairing Best Practice in the Gotchas section, help the user pick the optimal GCP region to minimize latency.
- Collect the necessary configuration variables for the chosen flow (e.g., Databricks credentials or AWS Account ID).
Only proceed to the next steps once this information is confirmed.
Step 2: API Verification
Verify that the required Google Cloud APIs are enabled for the project:
gcloud services check biglake.googleapis.com
If the API is not enabled, explicitly ask the user for permission to enable it. Do NOT proceed without their confirmation.
Flow A: Databricks Unity Catalog
1. Create a Regional Secret for Credentials
Store the Databricks client ID and secret in Secret Manager. Ensure the
secretmanager.googleapis.com API is enabled. The secret MUST be in the
same region as your Lakehouse catalog.
- Create a JSON file named
credentials.json:
{
"client_id": "<CLIENT_ID>",
"client_secret": "<CLIENT_SECRET>"
}
- Set the Secret Manager API endpoint override for the region:
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.
- 10d ago First seen · 309 lines · 149 tokens per session scan A 0bf225594ee6
federate-lakehouse-catalog is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (180 stars, last pushed yesterday), licensed Apache-2.0. It adds 149 tokens to every session and 2,568 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…