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 estuary/agent-skills --skill materialize-databricks-creategit clone --depth 1 https://github.com/estuary/agent-skillsWrote 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/estuary/agent-skills/materialize-databricks-create)<a href="https://agentmods.dev/skills/estuary/agent-skills/materialize-databricks-create"><img src="https://agentmods.dev/badge/skills/estuary/agent-skills/materialize-databricks-create/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/estuary/agent-skills/materialize-databricks-create"><img src="https://agentmods.dev/badge/skills/estuary/agent-skills/materialize-databricks-create.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00076 | $0.01911 |
| Opus 5.5 | $0.00030 | $0.00764 |
| Sonnet 5.5 | $0.00015 | $0.00382 |
| Haiku 4.5 | $0.00008 | $0.00191 |
Grade A, and why
materialize-databricks-create 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 1mo 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Databricks Materialization
Create a Databricks materialization using flowctl to stream data from Estuary collections into Databricks Unity Catalog tables.
Applies to: materialize-databricks
Step 0: Load Connector Documentation
Before proceeding, fetch the official connector docs for prerequisites, config reference, and setup instructions.
Load the docs page: https://docs.estuary.dev/reference/Connectors/materialization-connectors/databricks/
Use WebFetch to load this page. It covers:
- Prerequisites (Unity Catalog, SQL Warehouse, credentials)
- Full config property reference
- Authentication setup (PAT or OAuth2 M2M)
- Staging via Unity Catalog Volumes
Search Kapa for tribal knowledge (if the Estuary MCP is configured):
Search kapa ai knowledge sources for "materialize databricks common issues"
If Kapa MCP is not configured, the user can set it up: https://docs.estuary.dev/features/mcp-integration/
This skill provides the flowctl workflow and troubleshooting that docs don't cover.
Step 1: Gather Requirements
Before writing any YAML, ask the user:
- Workspace URL? — Databricks workspace hostname (e.g.,
dbc-abc123.cloud.databricks.com) - SQL Warehouse HTTP path? — Found in SQL Warehouse > Connection details
- Catalog and schema? — Unity Catalog name and target schema
- Authentication method? — Personal Access Token (PAT) or OAuth2 Machine-to-Machine (M2M)
- Non-default data plane? — Most users use the default. Ask if they need a non-default data plane.
- Source collections? — Which Estuary collections to materialize
- Hard deletes? — Off by default. Without it, deleted rows stay in Databricks marked with
_meta/op: 'd'. Enable to physically remove them. - Delta updates? — Off by default. Switches from one-row-per-key (standard merge) to append-only. Use for event logs or history tables.
- Sync schedule? — Controls how often batches are written to Databricks (default: 30 minutes,
0sfor real-time). Affects latency and compute cost.
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.
- 1mo ago First seen · 214 lines · 76 tokens per session scan A 57da7cce494a
materialize-databricks-create is a skill published in the GitHub repository estuary/agent-skills (7 stars, last pushed 26d ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,911 once invoked, about $0.0003 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 2026-08-31.
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