数据与 AI技能

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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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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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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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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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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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".

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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".

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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".

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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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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".

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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".

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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".

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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".

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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".

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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".

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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".

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Design a cache invalidation and eviction strategy — event-driven invalidation and thundering herd prevention. Use when asked to "design cache invalidation", "fix stale cache data", or "prevent a thundering herd".

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Design cost reduction strategies — model tiering, prompt compression, caching, batch inference. Use when asked to "reduce our AI costs", "set up model tiering", or "cut LLM spend".

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Draft compliance policies required by a framework (access control, incident response, data retention). Use when asked to "write an access control policy", "draft our incident response policy", or "we need a data retention policy".

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Spawn-count analytics for the tonone roster — which agents this project actually uses, from local session transcripts. Use when "which agents do we actually use", "show tonone stats", "prune the roster", or before running apex-profile.

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