DATA OPS
Postgres schema-change PII gate via GitHub migration webhook
Triggers on a merged migration PR, classifies the new or altered Postgres columns with an LLM.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub webhook fires on merged migration PRGitHub
- ActionRead migration diff and resulting Postgres column definitionsPostgres
- ActionClassify new and altered columns with an LLMOpenAI
- LogicKeep sensitive columns missing a masking policy
- ActionRecord classification verdict in Postgres for auditPostgres
- OutputComment ungated PII columns on the GitHub PRGitHub
What it does
This workflow shifts PII detection left to code review. When a database migration PR is merged, it inspects the new and altered columns, asks an LLM whether each is likely to hold personal data based on its name, type, and the migration diff, and flags any sensitive column that lacks a declared masking or retention policy. It comments findings directly back on the originating GitHub PR.
When to use it
Use it when schema changes ship through migrations and you want governance to catch unguarded PII columns at merge time instead of discovering them weeks later in production.
How it works
- 1A GitHub webhook fires on a merged migration PR.
- 2The workflow reads the migration diff and the resulting Postgres column definitions for the touched tables.
- 3An OpenAI call classifies each new or altered column and notes whether a masking policy is referenced.
- 4A logic step keeps columns judged sensitive but missing a policy.
- 5The workflow records the classification verdict in Postgres for audit.
- 6It posts a review comment on the GitHub PR listing the ungated PII columns and the recommended action.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect OpenAIModels, embeddings, files.
- 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.
More Data Ops workflows
Weekly BigQuery Cost Trend Sheet and Exec Digest
Compiles week-over-week BigQuery scheduled-query cost by owner and dataset into a Google Sheet with trend columns.
Daily BigQuery Scheduled-Query Cost Attribution to Owners
Each morning, totals the prior day's on-demand bytes-billed per scheduled query, maps each query to its owner from a label, and posts a per-owner cost leaderboard to Slack.
BigQuery Per-Team Budget Breach Alert to PagerDuty
Tracks month-to-date BigQuery scheduled-query spend per team and, when a team crosses its monthly budget, pages the team's on-call in PagerDuty and snapshots the spend breakdown…
dbt source freshness watcher with severity-routed alerts
Checks Snowflake loaded-at timestamps against each dbt source's freshness SLA, then routes warnings to Slack and hard breaches to a PagerDuty incident so stale data never…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Raw Sensor Telemetry Archive to BigQuery
Captures every incoming building sensor reading via webhook, normalizes the payload into a consistent schema.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.