--- name: lens-recon-54ec81ffd3b2 description: 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". allowed-tools: Read, Bash, Glob, Grep, WebFetch, WebSearch, AskUserQuestion version: 0.6.4 author: tonone-ai license: MIT --- # 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 (`dbt_project.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?** — scheduled refreshes, alerts, or manual pull? - **Who has access?** — is analytics self-serve or gated behind one person? ### Step 4: Present Coverage Map Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose. ``` ## Analytics Reconnaissance ### Tools in Use | Tool | Purpose | Status | |------|---------|--------| | [Metabase/Grafana/etc] | [what it's used for] | [active/stale/unused] | | ... | ... | ... | ### Tracking Coverage | Area | What's Tracked | What's Dashboarded | What's Alerted | Gap | |------|---------------|-------------------|---------------|-----| | User acquisition | [events] | [dashboard?] | [alert?] | [gap?] | | User activation | [events] | [dashboard?] | [alert?] | [gap?] | | Engagement | [events] | [dashboard?] | [alert?] | [gap?] | | Revenue | [events] | [dashboard?] | [alert?] | [gap?] | | Infrastructure | [metrics] | [dashboard?] | [alert?] | [gap?] | ### Data Infrastructure - **Warehouse:** [BigQuery/Snowflake/Postgres/none] - **Transformation:** [dbt/custom SQL/none] - **Orchestration:** [Airflow/cron/none] - **Freshness:** [real-time/hourly/daily/unknown] ### Assessment - **Defined metrics:** [N] out of [N] dashboard metrics have precise definitions - **Data freshness:** [status — pipelines healthy or broken] - **Self-serve:** [yes/no — can stakeholders query without engineering help] - **Automation:** [N] scheduled reports, [N] alerts configured ### Key Gaps 1. [most critical gap — what's not tracked or dashboarded that should be] 2. [second gap] 3. [third gap] ### What's Working - [positive observation — well-maintained dashboard, good tracking coverage] ``` Present facts. Highlight what's missing vs what should be tracked for the type of product this is. ## Delivery 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. --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/ai-agency/tonone/skills/lens-recon/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 tonone-ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/ai-agency/tonone/skills/lens-recon/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Marketing, Product Management, Sales Roles: Business Development, Data Analyst, Growth Marketer, Marketing Analyst, Product Manager Specialties: API, Database Tools: Airflow, Amplitude, Datadog, Google Analytics, Grafana, Metabase, Mixpanel, Redash, Redshift, Snowflake, Superset Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8e15803b85b7faf12be76c5e113f93e0f38e61cfafd457dbf25673bc863c2fbb