Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Coterie Insurance integration. Manage data, records, and automate workflows. Use when the user wants to interact with Coterie Insurance data.
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Complete architecture reference for the A3 insurance platform — file locations, data flow, conventions, microservice map, and fullstack patterns
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Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.
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Query the Bear EHR database using the semantic layer. Use when asked about clients, billing, appointments, insurance, clinical data, procedures, scheduling, programs, staff, vitals, diagnoses, revenue, claims, or any data question about the Bear application.
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Analyzes vehicle insurance daily reports and signing lists. Use when user asks to analyze insurance data, generate business reports, check institution performance, monitor policy trends, or detect business anomalies. Handles Excel/CSV files with fields like premium, institution, customer type, and renewal status.
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PostgreSQL database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Npgsql and EF Core.
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Generates optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.
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SQL Server database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Microsoft.Data.SqlClient and EF Core.
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Generates Repository interfaces and implementations following the Repository pattern. Provides data access abstraction for aggregate roots with EF Core implementations.
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Automates the Karpathy LLM Wiki workflow: turns web, GitHub, and YouTube URLs into well-structured, citable, wikilinked pages with automatic linting and sourcing — invoke with /pin-llm-wiki
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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".
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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".
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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".
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Prepare fundraising financial materials — investor model, data room financial docs, cap table, and use-of-funds narrative. Use when asked to "prepare for Series A", "build our investor model", or "what financials do investors want".
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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".
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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".
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Use when asked to select chart types for analytics dashboards, choose BI visualizations, or design data displays. Examples: "best chart for sales data", "dashboard visualization for metrics", "analytics chart selection"
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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".
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Data engineer — databases, migrations, pipelines, schema design, and query optimization. Use when asked to "design a schema", "write a migration", "build a data pipeline", or "optimize this query".
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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".
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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".
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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".
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Design eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".
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User segmentation and persona creation from mixed data sources — analytics, CRM, support tickets, reviews, or any combination. Use when asked to "build personas", "who are our users", "segment our users", "create user profiles", "define user archetypes", or "who is the target user".
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