데이터·AI 스킬

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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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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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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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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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 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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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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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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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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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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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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Use when a validated ChatGPT/Codex Plugin needs accurate Plugin Directory fields, discovery metadata, starter prompts, and reviewer-facing listing details grounded in the packaged product.

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Use when an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.

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Add support for a new curve data format in the Curves package converter system

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Add a viewer, configure a viewer, change viewer options, find viewer, close viewer, view a scatter plot, bar chart, histogram, line chart, box plot, pie chart, heat map, correlation plot, 3D scatter, trellis, density plot, statistics, on a Datagrok TableView via the datagrok_exec tool. Use whenever the user asks to plot, chart, visualize, show a graph, draw a distribution, color by a column, swap a viewer's axis, toggle a legend / regression line / log scale, replace one viewer with another, close every chart, reset the view to just the grid, or find an existing viewer by type. Plugin viewers like "Chem space", "sequence space", "activity cliffs" are NOT viewer types — they're registered functions — route those to `grok.functions.call`. Does NOT cover filtering (separate skill `datagrok-filtering`), selection (`datagrok-selection`), grid cell rendering (`datagrok-grid-customization`), layout save/restore, or custom-viewer authoring.

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Define semantic type detectors in detectors.js for auto-detecting column data meaning

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Sort, hide, show, reorder, resize, pin, format, and color-code columns in a Datagrok TableView grid via the datagrok_exec tool. Use whenever the user asks to sort by a column (any direction), multi-sort, hide / show / reorder / pin / resize columns, freeze the first N columns, change number-format display, color-code cells (defaults and grid-only tint here; full per-type reference in datagrok-df-and-columns), set row height, or reset the grid back to defaults. Distinct from datagrok-df-and-columns (which owns column-level data metadata like semType, units, friendlyName, and is also where canonical color-coding lives) and from datagrok-viewers (which owns scatter plot / histogram / etc.). Does NOT cover filtering (`datagrok-filtering`), selection (`datagrok-selection`), custom cell renderer authoring (`create-cell-renderer`), saving / restoring layouts, or grid event handlers.

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