Data & Analytics for Claude Code

AI Operations Team — Embed: Embedding model selection, vector pipeline design, similarity search, and production index management.

Design embedding pipelines — model selection, batching, normalization, index refresh strategy.

Audit embedding infrastructure — model drift, index freshness, query latency, coverage gaps.

Optimize similarity search — ANN index tuning, hybrid search, reranking, query expansion.

Finop skill: finop-recon

Data engineer — databases, migrations, pipelines, data modeling

Data quality and pipeline health check — freshness, schema drift, null rates, orphaned records, pipeline status. Use when asked about "data quality check", "pipeline health", "is our data fresh", or "schema drift".

Build a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to "build ETL", "data pipeline", "move data from X to Y", or "sync data".

Optimize slow database queries — analyze execution plans, add indexes, rewrite queries. Use when asked about "slow query", "optimize SQL", "query performance", or "explain this query".

Database reconnaissance — full inventory of schema, migrations, data volume, backups, connection pooling, and query patterns. Use when asked to "assess this database", "understand the schema", or "database health check".

Design and build database schema — tables, columns, types, indexes, constraints, relationships. Given a domain description, output the schema and write the files. Use when asked to "design schema", "database design", "create tables", or "data model".

Survey corporate governance artifacts and flag gaps

Map current AI safety controls — filter inventory, coverage gaps, latency impact, incident history.

Data analytics & BI engineer — dashboards, metrics design, reporting, data storytelling

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".

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Design and spec an analytical dashboard — define the question each chart answers, write the SQL queries, spec the layout and refresh cadence. Produces a complete dashboard spec ready to implement. Use when asked to "build a dashboard", "analytics dashboard", "BI dashboard", "weekly product health", or "visualize this data".

Produce a complete metrics definition doc — metric name, formula, data source, segmentation, SQL or event tracking spec, and what good/bad looks like. Given a product area, outputs the full metrics spec. Use when asked to "define KPIs", "metrics framework", "what should we measure", "north star metric", or "instrument this feature".

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".

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".

Product analyst — metrics frameworks, funnel analysis, OKRs, A/B test design, and retention analysis

Instrumentation plan — design event taxonomy, property schema, and tracking plan for analytics tools. Use when asked to "what should we track", "instrumentation plan", "set up analytics events", "analytics event schema", "tracking plan", or "instrument this feature".

Metrics architecture — produce a complete metrics plan given a product description. North Star, input metrics tree, instrumentation spec, action triggers, and counter-metrics. Use when asked to "design a metrics framework", "what should we measure", "build a metrics system", "define our KPIs", "what are our success metrics", "metrics strategy", or "what do we track".

Analytics reconnaissance — scan existing event tracking, metric definitions, dashboards, and analytics configuration to understand what is currently being measured. Use when asked to "what are we tracking", "audit our analytics", "what metrics exist", "analytics inventory", or before designing new metrics or instrumentation.

Finance engineer — P&L, runway, unit economics, fundraising, board reporting, and cap table management

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Build an internal dashboard with data tables, filters, detail views, and CRUD. Use when asked to build an "admin panel", "internal dashboard", "back office", or "data dashboard UI".

AI Operations Team — Rank: Retrieval reranking, relevance scoring, learning-to-rank pipelines, and result quality evaluation for search and RAG.

Build ranking evaluation — NDCG/MRR measurement, human relevance labeling, offline eval harness.

Audit ranking quality — metric trends, failure modes, dataset coverage, reranker performance.

Survey IP assets, license obligations, and assignment gaps

Map product data flows and identify regulatory triggers

Growth state reconnaissance — scan existing onboarding flows, acquisition channels, conversion funnels, and growth experiment logs to understand current growth state. Use when asked to "what's our growth state", "audit the funnel", "what growth experiments have we run", "acquisition channel inventory", or before designing new growth experiments.

Retention diagnosis + intervention plan — analyze the retention curve, identify the primary drop-off point, and produce a specific intervention plan with expected impact. Use when asked to "improve retention", "why are users churning", "build a retention playbook", "reduce churn", "win-back campaign", or "users aren't coming back".

Terra skill: terra-recon

Design chunking strategies — semantic splitting, overlap tuning, retrieval-aware chunk sizing.

Audit token usage patterns — avg context size, waste, truncation frequency, budget adherence.

Observability reconnaissance — inventory what monitoring exists, map coverage, highlight blind spots. Use when asked "what monitoring exists", "observability assessment", or "what can we see".

Scored SEO audits for developers/marketers using AI agents: technical health, JSON-LD, E-E-A-T, backlinks, GEO (AI Overviews, ChatGPT, Perplexity). 35 scripts; HTML/Excel/PDF. Default capped crawl scope; --crawl-deep for multi-page broken links + canonical. Optional MCP (Firecrawl, DataForSEO). Parallel workers: agents/PARALLEL-AUDIT.md. Modes: Audit → Plan → Execute.