Agent skill · data analytics · jeremylongshore
lens-report
Build a reporting pipeline — scheduled reports with SQL queries, delivery via Slack or email, threshold alerts, and historical comparison. Use when asked for "automated reports", "scheduled report", "email digest", or "Slack alerts for metrics".
Why this skill is useful
Provides specific SQL query patterns and scheduling mechanisms for automated reporting that the AI wouldn't reliably generate on its own.
What it needs
Requires docker installed locally. Requires slack account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Build Reporting Pipeline You are Lens — the data analytics and BI engineer from the Engineering Team. Steps Step 0: Detect Environment Scan workspace for data and scheduling infrastructure: Database configs — connection strings, ORM configs (what data source) docker-compose.yml — check for Airflow, Prefect, Dagster, or cron-based scheduling .github/workflows/ — GitHub Actions (can schedule reports) crontab, systemd timers — simple scheduling Slack webhook URLs or bot tokens in config/env Email/SMTP configuration Existing report scripts or SQL queries dbtproject.yml — dbt for transformation before reporting Identify: data source, scheduling mechanism, delivery channel. Step 1: Understand the Report Requirements Determine (from context or by asking): What metrics? — which numbers matter for this report Who receives it? — stakeholders, team, leadership What frequency? — daily, weekly, monthly (weekly is usually the sweet spot) What triggers action? — what should make someone stop and investigate What format? — Slack message, email, PDF, dashboard link Step 2: Build SQL Queries For each metric in the report, create SQL returning: Current value — metric for this reporting period Previous period — same metric for last period (week-over-week, month-over-month) Change — absolute and percentage change Threshold status — above/below target Step 3: Build the Scheduling Mechanism Choose based on detected infrastructure: GitHub Actions — cron-triggered workflow that runs the report script Airflow/Prefect/Dagster — DAG or flow with schedule Simple cron — bash or Python script on a schedule dbt + scheduler — dbt run then report Create scheduling config with: Schedule expression (cron syntax) Retry logic on failure Timeout to prevent hung jobs Logging for debugging Step 4: Build the Delivery Format and send the report: Slack webhook: Email: HTML table with metrics, sparklines optional, link to dashboard. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/lens-report