What is nimble-databricks-data-products?

A workflow for building Databricks data products from live web data. Databricks is a platform for storing and analyzing data; the workflow can connect collected data to Delta tables, dashboards, or a Databricks App.

Use it to collect live information such as product pricing, store it in Delta tables, and create an AI/BI dashboard or deployed app for demos or production work.

It connects web-data collection with usable analysis in one process. Source-specific discovery and testing reduce errors caused by assuming every data source works the same way.

nimble-databricks-data-products — Skill for Claude Code by Nimbleway

Install

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.

Any agent

npx skills add Nimbleway/agent-skills --skill nimble-databricks-data-products

Clone the repo

git clone --depth 1 https://github.com/Nimbleway/agent-skills

Made for: Claude Code.

Or install nimble, the plugin that ships this one along with the rest of its 15 skills, 1 command, 2 agents.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nimbleway/agent-skills/nimble-databricks-data-products/github.svg)](https://agentmods.dev/skills/nimbleway/agent-skills/nimble-databricks-data-products)

Your own site

<a href="https://agentmods.dev/skills/nimbleway/agent-skills/nimble-databricks-data-products"><img src="https://agentmods.dev/badge/skills/nimbleway/agent-skills/nimble-databricks-data-products/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nimbleway/agent-skills/nimble-databricks-data-products"><img src="https://agentmods.dev/badge/skills/nimbleway/agent-skills/nimble-databricks-data-products.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 246 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,546 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00246	$0.02546
Opus 5	$0.00123	$0.01273
Sonnet 5	$0.00049	$0.00509
Haiku 4.5	$0.00025	$0.00255

Measured 11d ago against content hash 4e2a6574396b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

nimble-databricks-data-products 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 11d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/build_dashboard.py, scripts/ingest.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/nimble-databricks-data-products/SKILL.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Nimble on Databricks — data products builder

Turn a natural-language brief like pricing analysis on dog products from walmart and amazon into working Databricks data products: discover agents → ingest live web search data into Delta → build dashboard and/or app → deliver links. Equally at home for a quick demo or a real, reusable data product.

You are the orchestrator. Databricks mechanics are delegated to the official databricks-* skills (see references/databricks-skills.md); this skill owns the Nimble glue and the gaps (agent discovery, ingestion-from-agents, the AI/BI dashboard JSON, branding).

Golden rules

Discover, don't assume. Read agent names via nimble_agent_list(), input params via nimble_agent_describe('<agent>'), and output fields by probing one call (to_json(parsing[0])) — never hardcode from memory (Amazon search takes keyword, not query).
Probe before fanning out. Run one call per source first to learn its localization flag, field names, and value formats — sources differ (some return numeric prices, others currency strings).
One statement per Statements API call. Multiple ;-separated statements in one call are a parse error.
Each Bash call is a fresh shell. Env vars and cd don't persist — set them inline. See references/preflight.md.
Fail fast, then confirm. Run Phase 0 preflight first; recommend a warehouse + writable schema, then confirm before writing.
Always ask the deliverable. Table / +dashboard / +app is a per-run choice.
Branding is always on, neutral. "Powered by Nimble" + light theme + yellow accent. See references/branding.md.
Leave artifacts in place. No teardown.
Show your work and the headline. End with URLs and the one-sentence insight (e.g. the price gap).

Workflow

Track these as todos so nothing is skipped.

Phase 0 — Preflight (read-only, fail fast)

Lean on the databricks-core skill for the generic checks.

databricks current-user me → confirm auth; capture the username (for the default schema).
Find a RUNNING SQL warehouse: databricks warehouses list. Prefer one already RUNNING; if none, offer to start one.
Integration gate — confirm these exist: nimble_integration.tools.{nimble_search, nimble_extract, nimble_agent_run, nimble_agent_list, nimble_agent_describe}. Quick check: databricks functions list nimble_integration tools. If missing → STOP and walk the user through references/install-nimble-integration.md (Nimble cookbook). Do not try to auto-install.
Recommend + confirm the target: a warehouse and a writable catalog.schema (default users.<username>). Verify writability — some shared catalogs deny CREATE TABLE. Present the recommendation and let the user confirm or override before writing.

Read the full file on GitHub · 161 lines

Files

What ships with it

10 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.

Changes

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.

11d ago First seen · 161 lines · 246 tokens per session scan A 4e2a6574396b

Subscribe to this mod's changes

nimble-databricks-data-products is a skill published in the GitHub repository Nimbleway/agent-skills (53 stars, last pushed 15d ago), licensed MIT. It adds 246 tokens to every session and 2,546 once invoked, about $0.0012 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.

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