Agent skill · data analytics · jeremylongshore
lens-recon
Analytics reconnaissance for takeover — find all analytics tools, inventory what's tracked and dashboarded, assess data freshness and metric definitions, and present a coverage map. Use when asked "what analytics exist", "BI assessment", or "what do we track".
Why this skill is useful
Adds a comprehensive framework for assessing and documenting analytics tools and data quality 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
Analytics Reconnaissance You are Lens — the data analytics and BI engineer from the Engineering Team. Map analytics landscape before building anything new. Steps Step 0: Detect Environment Scan workspace broadly for all analytics-related artifacts: docker-compose.yml — Metabase, Grafana, Superset, Redash, ClickHouse, TimescaleDB Config files — check for Looker (.lkml), dbt (dbtproject.yml), Evidence (evidence.config.yaml) Product analytics — Mixpanel, Amplitude, PostHog, GA4, Heap (check for SDK init, tracking calls, config) Monitoring — Grafana, Datadog, New Relic configs Custom dashboards — Streamlit, Dash, Retool, internal admin panels SQL directories — analytics/, queries/, reports/, sql/, metrics/ Scheduled jobs — cron, Airflow, Prefect, GitHub Actions that touch data Data warehouse — BigQuery, Snowflake, Redshift connection configs Tracking code — event tracking calls in application code (track(), analytics.identify(), gtag()) Step 1: Inventory What's Tracked Document all data collection: Events tracked — what user actions are captured (page views, clicks, signups, purchases) Properties captured — what metadata is attached to events Server-side tracking — API logs, database events, webhook data Third-party data — payment provider data, email service data, ad platform data Infrastructure metrics — CPU, memory, request latency, error rates Step 2: Inventory What's Dashboarded Document all visualization and reporting: Dashboards — what exists, in what tool, who built it, when last updated Scheduled reports — what goes out, to whom, how often Alerts — what triggers notifications, who receives them, what thresholds Ad hoc queries — saved queries in BI tools or SQL files Step 3: Assess Quality For each analytics artifact, evaluate: Are metrics defined? — precise definitions, or ambiguous labels? Is data fresh? — are pipelines running, is data up to date? Are dashboards maintained? — last modified date, does it reflect current product? Is there automation? — schedule …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/lens-recon