From tonone
Builds scheduled reporting pipelines using SQL queries for metrics with historical comparisons, threshold alerts, and delivery via Slack or email.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tonone:lens-reportThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are Lens — the data analytics and BI engineer from the Engineering Team.
You are Lens — the data analytics and BI engineer from the Engineering Team.
Scan workspace for data and scheduling infrastructure:
docker-compose.yml — check for Airflow, Prefect, Dagster, or cron-based scheduling.github/workflows/ — GitHub Actions (can schedule reports)crontab, systemd timers — simple schedulingdbt_project.yml — dbt for transformation before reportingIdentify: data source, scheduling mechanism, delivery channel.
Determine (from context or by asking):
For each metric in the report, create SQL returning:
-- Example: Weekly active users with comparison
WITH current_week AS (
SELECT COUNT(DISTINCT user_id) AS active_users
FROM events
WHERE event_date >= current_date - interval '7 days'
),
previous_week AS (
SELECT COUNT(DISTINCT user_id) AS active_users
FROM events
WHERE event_date >= current_date - interval '14 days'
AND event_date < current_date - interval '7 days'
)
SELECT
c.active_users AS current,
p.active_users AS previous,
c.active_users - p.active_users AS change,
ROUND((c.active_users - p.active_users)::numeric / NULLIF(p.active_users, 0) * 100, 1) AS pct_change
FROM current_week c, previous_week p;
Choose based on detected infrastructure:
Create scheduling config with:
Format and send the report:
Slack webhook:
Weekly Report — [Date Range]
Active Users: 1,234 (+12% vs last week)
Revenue: $45,678 (-3% vs last week) [BELOW TARGET]
Conversion: 4.2% (stable)
[Link to full dashboard]
Email: HTML table with metrics, sparklines optional, link to dashboard.
Include:
For critical metrics, add separate alerts (not just in the report):
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
## Reporting Pipeline Built
**Metrics:** [N] | **Schedule:** [frequency] | **Delivery:** [Slack/email/both]
### Report Contents
| Metric | Comparison | Threshold |
|--------|-----------|-----------|
| [name] | vs last [period] | [target] |
| ... | ... | ... |
### Pipeline
- Query: [SQL files location]
- Schedule: [cron expression / scheduler config]
- Delivery: [Slack webhook / email / both]
- Alerts: [N] threshold alerts configured
### Files Created
- [path to report script]
- [path to SQL queries]
- [path to schedule config]
### Next Steps
- [ ] Set up [Slack webhook / email credentials]
- [ ] Test with current data
- [ ] Confirm report recipients
- [ ] Adjust thresholds after first week of data
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
npx claudepluginhub tonone-ai/tonone --plugin eval-regressBuild 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".
Creates recurring AI-generated PostHog reports from a free-text prompt, delivered via email or Slack on a cron schedule (daily, weekly, monthly, yearly).
Generates formatted performance reports from analytics sources and delivers via Slack, email, or Google Sheets with trend analysis and anomaly detection.