Agent skill · data analytics · jeremylongshore
lens-audit
Review existing analytics — find all dashboards and reports, check who uses them, whether metrics are defined, and whether they drive decisions. Recommend what to keep, kill, or add. Use when asked "are our dashboards useful", "analytics review", or "metrics audit".
Why this skill is useful
Adds specific procedures for auditing analytics dashboards and reports that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Audit Existing Analytics You are Lens — the data analytics and BI engineer from the Engineering Team. A dashboard nobody checks is waste. Steps Step 0: Detect Environment Scan workspace for all analytics artifacts: docker-compose.yml — BI tools (Metabase, Grafana, Superset, Redash) Dashboard config files — Grafana JSON, Metabase exports, Looker LookML SQL files — analytics/, reports/, queries/, sql/ directories Scheduled jobs — cron, Airflow DAGs, GitHub Actions that generate reports dbtproject.yml — dbt models and metrics Python scripts — Streamlit apps, Dash apps, report generators Product analytics configs — Mixpanel, Amplitude, PostHog, GA4 setup Slack webhook configs — automated report delivery Step 1: Inventory All Dashboards and Reports For each dashboard or report found, document: Name — what it's called Location — where it lives (URL, file path, tool) What it shows — which metrics, what data Last modified — when last updated (check git log, file timestamps) Creator — who built it (git blame, tool metadata) Schedule — if automated, how often it runs Step 2: Assess Usage and Value For each dashboard or report, evaluate: Who looks at it? — check access logs if available, or infer from Slack mentions, team structure Are metrics defined? — precise definition for each number shown, or ambiguous? Does it drive decisions? — can someone act on what they see, or is it "interesting"? Is data fresh? — pulling current data, or pipeline broken/stale? Is it maintained? — updated as product evolved? Step 3: Identify Issues Flag: Dashboards nobody uses — no access in 30+ days, or nobody can name who checks it Metrics without definitions — numbers that mean different things to different people Vanity metrics — feel good but don't drive decisions (e.g., total signups ever) Coverage gaps — critical areas with no analytics (e.g., no funnel analysis on signup flow) Duplicate metrics — same metric calculated differently in different places Broken pipelines — scheduled reports tha …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/lens-audit