“data-science”的搜索结果

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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Design or audit a feature store — serving, freshness, and sharing across models. Use when asked "do we need a feature store", "design a feature store", or "share features across models".

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Audit feature engineering code for leakage, quality issues, and pipeline correctness. Use when asked to "audit our feature pipeline", "is there data leakage", or "find feature quality issues".

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Design an A/B test — power analysis, randomization, and success metrics. Use when asked to "design an A/B test", "how many users do we need", or "run a power analysis".

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Analyze A/B test results — statistical significance, practical significance, and segmentation. Use when asked to "analyze our A/B test", "is this result significant", or "read these experiment results".

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Build a forecasting model for a time series — demand, revenue, or usage prediction. Use when asked to "forecast demand", "predict next quarter revenue", or "build a time series model".

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Survey existing forecasting code or models in a codebase — find gaps, stale models, and missing validation. Use when asked "what forecasting models do we have", "audit our forecasts", or "find stale models".

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Validate and benchmark a forecasting model — walk-forward CV, error metrics, baseline comparison. Use when asked "is this forecast any good", "validate a forecasting model", or "backtest the forecast".

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Audit existing data cleaning code — find missing validation, silent data loss, and quality gaps. Use when asked to "audit our data cleaning", "are we losing data silently", or "find data quality gaps".

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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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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 apps with the Claude API or Anthropic SDK. TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`/`claude_agent_sdk`, or user asks to use Claude API, Anthropic SDKs, or Agent SDK. DO NOT TRIGGER when: code imports `openai`/other AI SDK, general programming, or ML/data-science tasks.

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Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

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Use for data science refit recipes when optimizing a Coworld player.

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Use for data science refit recipes in scripted Coworld policy optimization.

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Use for reusable data science refit recipes in software and coding-agent work.

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Use for Crewrift-specific data science refit recipes when optimizing a player.

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Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.

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Use only when the user explicitly requests strict code-cell-only output for a Jupyter or undergraduate data-science task, or when an ITX2007 assignment requires that format. Do not activate for ordinary data analysis or explanation requests.

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Use for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python code.

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Data Scientist IA — Expert en data science (pandas, numpy, scikit-learn, EDA, feature engineering, statistical analysis)

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Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps

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Expert en data science (pandas, numpy, scikit-learn, feature engineering, model selection)

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Draft and quality-check pages for the IPA Knowledge Hub in the site's house style. Use when creating a new .qmd Hub page from source material, adapting outside content into a Hub page, or reviewing an existing page for structure, tone, frontmatter, references, and Diátaxis fit. Replicates the quality-control workflow of IPA's Knowledge Hub Generator assistant.

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