nimble-databricks-data-products

name	nimble-databricks-data-products
description	Builds Databricks data products from live web data, end to end: discovers the right Nimble web-data agents, scrapes into Delta tables, and produces an AI/BI dashboard and/or a deployed Databricks App — a table → dashboard → app workflow, for production data products or quick demos. Use whenever a request pairs live or scraped web data WITH a Databricks destination — e.g. "scrape Amazon/Walmart prices into a Delta table and build a dashboard", "load Zillow/Instagram/Maps/search results into Databricks and build a dashboard or app", "showcase Nimble + Databricks to a prospect". Prefer it over nimble-web-expert or competitor-intel when the data lands in Databricks. Do NOT use for one-off web fetches or CSV exports with no Databricks destination — use nimble-web-expert instead. Do NOT use for competitor or company research briefings — use competitor-intel or company-deep-dive instead. Do NOT use for generic Databricks work with no Nimble/web-data angle — use the official databricks-* skills instead.
allowed-tools	["Bash(databricks:*)","Bash(python3:*)","Bash(bash:*)","Bash(jq:*)","Bash(npm:*)","Read","Write","Edit","AskUserQuestion","Skill"]
metadata	{"author":"Nimbleway","version":"0.23.0"}

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.

1. databricks current-user me → confirm auth; capture the username (for the default schema).
2. Find a RUNNING SQL warehouse: databricks warehouses list. Prefer one already RUNNING; if none, offer to start one.
3. 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.
4. 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.

Details + exact commands: references/preflight.md.

Phase 1 — Interpret the brief + clarify (AskUserQuestion)

Parse the brief into: domain/entity · search terms · sources · analysis goal. Then ask (batch into one AskUserQuestion call):

• Deliverable — always ask: table / table + dashboard / table + dashboard + app.
• Sources — confirm the agents you matched (e.g. Amazon + Walmart SERP).
• Volume — default ~8–10 search terms, ~100+ rows/source.

Keep the brief's intent (the "analysis goal") — it picks the Phase 4 template and the headline.

Phase 2 — Discover agents + map a unified schema

See references/nimble-agents.md.

1. nimble_agent_list() via SQL, filter by the source/domain keywords.
2. For each chosen agent: nimble_agent_describe('<name>') → read its input params (required ones, exact names, localization/pagination flags). Output fields come from the §2.5 probe, not here.
3. Design one unified table with a source column + a normalized core (product_name, price, currency, rating, review_count, brand, url, …), keeping only fields the chosen agents actually emit. Multi-source comparison hinges on the shared columns.

Phase 3 — Ingest (control table + one set-based call)

See references/nimble-agents.md for the full SQL. Drive ingestion from a control table, not per-keyword files — it's reproducible and expandable (add a row, re-run). 0. Probe ONE call per source first (fail fast). Before fanning out, run a single nimble_agent_run per source and check: status, the real field names, the localization flag, and whether a price casts cleanly. This catches the Walmart-class surprises (localization, currency- string prices, product_price vs price) in ~40s instead of after a wasted full round. Highest- leverage step — see nimble-agents.md §2.5.

1. Create a control (queries) table <schema>.<table>_queries (source, agent, keyword, params_json, localization, enabled) and seed one row per (source × term). params_json uses each agent's real param name (from input_properties); set localization per agent (e.g. amazon_serp true, walmart_serp false).
2. Create the unified results table (source column + normalized core + raw VARIANT).
3. Run one INSERT that calls nimble_agent_run(q.agent, q.params_json, q.localization) via a correlated LATERAL join over the control table, with a /*+ REPARTITION(N) */ hint (N ≈ enabled rows, kept modest — high parallelism can trip API rate limits) so the agent calls run in parallel. It's one long statement → run it async with bash scripts/ingest.sh <WH> ingest.sql.
4. Reconcile against the control table (LEFT JOIN): a term that lands no items returns an empty result, and a correlated LATERAL drops empty rows — so reconcile to confirm every source is covered. If a source shows 0, re-check its localization flag (per-agent) and casts before building; see nimble-agents.md §6 for the diagnostic order.

Phase 4 — Build the deliverable(s)

Choose a template from the matched agents' vertical/entity_type:

Vertical	Dashboard/app shape
Ecommerce (SERP/PDP/CLP)	KPIs; listings & avg price by source/keyword; sponsored share; price-vs-rating scatter; product table with Open links; multi-source → comparison bars + best-effort item-level price gap
Social	volume/engagement by account/post; top-content table; like/follower distributions
Real Estate	price & price/sqft; listings by location; beds/baths breakdowns
Maps / Local	avg rating; review counts; places table
LLM / AEO	source/answer presence; share-of-voice; citation table
fallback	KPIs + 2 categorical bars + the raw table (works off any output_schema)

Comparison depth (hybrid): always build the aggregate/category comparison; additionally try best-effort item-level matching across sources (normalize brand + key tokens). If confident matches exist, add a "same-product price gap" view; otherwise keep the aggregate comparison and note that item-level matching wasn't confident.

• Dashboard → use scripts/build_dashboard.py (compact spec → valid serialized_dashboard, create + publish). It bakes in every Lakeview gotcha. Read references/dashboard-cookbook.md for the spec format and recipes.
• App → follow references/app-cookbook.md (delegates scaffold/deploy to databricks-apps; adds the Nimble-specific SQL, branding, and the numeric-string / light-mode gotchas).
• Branding → references/branding.md (always applied).

Phase 5 — Verify, deliver & share

• Publish the dashboard / confirm the app is RUNNING; collect URLs.
• Summarize what was built and the headline insight (the comparison takeaway).
• Offer to share the dashboard/app link — if a Slack or Notion connector is available, offer to post it there (Slack = the link + headline; Notion = a short dated page). Mention once; don't nag.
• Suggest next steps with sibling skills, e.g. competitor-intel / company-deep-dive for business signals on the brands surfaced, or nimble-web-expert for a one-off deeper pull.
• Offer iterations (more charts, item-level matching, theming, a scheduled refresh job).

Reference map

• references/databricks-skills.md — which official databricks-* skill to use per phase.
• references/install-nimble-integration.md — setup when the integration gate fails.
• references/preflight.md — auth, warehouse, writable-schema discovery (exact commands).
• references/nimble-agents.md — discovery, schema mapping, ingestion SQL + gotchas.
• references/dashboard-cookbook.md — Lakeview JSON recipes + every gotcha (authoritative).
• references/app-cookbook.md — AppKit demo app glue + gotchas.
• references/branding.md — "Powered by Nimble", logo, colors.
• scripts/ingest.sh — async statement fan-out + poll.
• scripts/build_dashboard.py — compact spec → create + publish a dashboard.
• assets/nimble-logo.png — the Nimble mark for app branding.