Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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"
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
Design and implement a feature engineering pipeline for a ML problem. Use when asked to "engineer features for this model", "what features should we build", or "design feature transformations".
The English summary is being prepared. Showing the original description.
Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
Run a user interview — produce an interview guide and synthesize the output into an actionable insight report. Use when asked to "run a user interview", "synthesize these interview notes", "what do users actually want", "build a persona from this feedback", "find the JTBD in these transcripts", or "analyze this interview data".
The English summary is being prepared. Showing the original description.
Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication. Use when asked to "clean this dataset", "handle missing values", or "deduplicate this data".
The English summary is being prepared. Showing the original description.
Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".
The English summary is being prepared. Showing the original description.
Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".
The English summary is being prepared. Showing the original description.
Evaluate model performance — check for accuracy drops, data drift, and error patterns. Use when asked about "model accuracy dropped", "evaluate the model", "check for drift", or "model performance".
The English summary is being prepared. Showing the original description.
Design a data validation pipeline — schema checks, range validation, and quality metrics. Use when asked to "validate incoming data", "add schema checks", or "define data quality metrics".
The English summary is being prepared. Showing the original description.
ML/AI engineer — LLM integrations, prompt engineering, model pipelines, evals, RAG. Use when asked to "add AI to this", "integrate an LLM", "build a RAG pipeline", "evaluate model accuracy", or "improve this prompt".
The English summary is being prepared. Showing the original description.
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".
The English summary is being prepared. Showing the original description.