DATA OPS
Pre-Scan Migration PRs for PII Columns Before Merge
On every pull request touching SQL migration files, parses added columns, classifies them for PII, and posts a blocking review comment requiring a tagging plan before merge.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub PR opened on migration filesGitHub
- ActionFetch PR diff and parse new columnsGitHub
- ActionClassify columns for PII with OpenAIOpenAI
- LogicBranch on unclassified high-confidence PII
- OutputPost review comment on GitHub PRGitHub
What it does
When a pull request adds or modifies database migration files, this workflow extracts the columns being created, classifies each one for likely PII, and writes a review comment back on the PR. If high-confidence PII columns are introduced without a documented classification, it flags the PR so the author addresses governance before the change ever reaches the warehouse.
When to use it
Use it to shift PII review left — catching sensitive columns at code review instead of after they ship to Snowflake or BigQuery. It's ideal for teams that want governance enforced in the same place engineers already work.
How it works
- 1A GitHub pull-request webhook triggers on PRs that touch migration paths.
- 2Fetch the PR diff and parse `ADD COLUMN` / `CREATE TABLE` statements to extract new column names and types.
- 3An OpenAI classifier scores each column for PII type and confidence.
- 4A logic branch decides: clean PRs get an approving note, PRs with unclassified high-confidence PII get flagged.
- 5Post the findings as a structured review comment listing each flagged column and the required next step back on the GitHub PR.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect OpenAIModels, embeddings, files.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, 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.