CHATBOTS
Slack Data-Dictionary Bot: Ask Any Warehouse Column
Answers Slack questions about Snowflake tables and columns by reading live metadata and the curated data dictionary, then replies in-thread with definitions, owners, and upstream…
How it runs
The automated pipeline, trigger to output.
- TriggerSlack mention or /datadict questionSlack
- ActionRead column metadata from Snowflake INFORMATION_SCHEMASnowflake
- ActionFetch curated definition + owner from CodaCoda
- LogicReconcile live schema vs docs, flag drift
- OutputReply in Slack thread with definition, owner, lineageSlack
What it does
Turns a Slack channel into a self-serve data dictionary. An analyst asks "what does `orders.net_revenue` mean?" or "who owns the `dim_customer` table?" and the bot answers in-thread with the column description, data type, owning team, and where the value comes from upstream.
When to use it
When analysts and PMs constantly DM the data team to ask what a column means or whether they can trust it. Use it to deflect repetitive schema questions and keep tribal knowledge searchable.
How it works
- 1A Slack mention or slash command fires with the user's question.
- 2The bot reads live column metadata from Snowflake's INFORMATION_SCHEMA for the referenced table.
- 3It pulls the curated business definition, owner, and lineage notes from the Coda data-dictionary doc.
- 4An agent step reconciles live schema with the curated docs, flagging any drift (e.g., a column that exists in Snowflake but is undocumented).
- 5The bot posts a threaded reply with the definition, type, owner, freshness, and upstream source table.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect CodaDocs, packs, automations.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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