「transformation」の検索結果

Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.

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Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

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Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.

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Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit

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Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

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Use when managing multiple initiatives across time horizons (now/next/later, H1/H2/H3), balancing risk vs return across portfolio, sizing and sequencing bets with dependencies, setting exit/scale criteria for experiments, allocating resources across innovation types (core/adjacent/transformational), or when user mentions portfolio planning, roadmap horizons, betting framework, initiative prioritization, innovation portfolio, or resource allocation across horizons.

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Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

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Create and manage database migrations with reversible changes, proper naming conventions, and zero-downtime deployment strategies. Use this skill when creating database migration files, modifying schema, adding or removing tables/columns, managing indexes, or handling data migrations. Apply when working with migration files (e.g., db/migrate/, migrations/, alembic/, sequelize migrations), schema changes, database versioning, rollback implementations, or when you need to ensure backwards compatibility during deployments. Use for any task involving database structure changes, index creation, constraint modifications, or data transformation scripts.

日本語の概要は準備中です。原文の説明を表示しています。

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

日本語の概要は準備中です。原文の説明を表示しています。

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

日本語の概要は準備中です。原文の説明を表示しています。

Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.

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Assess organization's digital transformation readiness. Evaluate data culture, technology adoption, and process maturity.

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Generates a Mermaid sequence diagram showing how data moves between components. Use when tracing request flows or documenting data transformation pipelines.

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Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.

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Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.

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Operate the ai-data-science-team package for AI-assisted data loading, EDA, pandas transformations, SQL analysis, H2O/MLflow modeling, multi-agent teams, and Streamlit app workflows.

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Mathematical visualization skill for equations, proofs, and geometric concepts. **Triggers when:** - User mentions equations, formulas, or mathematical expressions - Request involves mathematical proofs or derivations - Content includes geometric relationships - User mentions LaTeX, calculus, algebra, geometry, trigonometry - Patterns: "equation", "formula", "prove", "derive", "graph", "plot" **Capabilities:** - LaTeX equation rendering with color-coded components - Function graphing and transformations - Geometric constructions and proofs - 3D mathematical surfaces - Step-by-step derivations with highlights

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Create original short videos using the 服务改造｜私域工序到公开身份回归 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.

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Generate images using Google's Nano Banana Pro (gemini-3-pro-image-preview). Accepts text prompts and optionally images (for editing/transformation) as INPUT. Returns generated IMAGES as OUTPUT. Use when user asks to create, generate, edit, or draw images, infographics, visualizations, diagrams, charts, or illustrations. Excellent for data-accurate infographics and text rendering.

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Use when planning with fixed deadline or target outcome, working backward from future goal to present, defining milestones and dependencies, mapping critical path, identifying what must happen when, planning product launches with hard dates, multi-year strategic roadmaps, event planning, transformation initiatives, or when user mentions "backcast", "work backward from", "reverse planning", "we need to launch by", "target date is", or "what needs to happen to reach".

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Write code through a disciplined, BDD-framed Test-Driven Development loop: build a behavior test list, then drive each behavior through red-green-refactor with an enforced observed-failure gate. Use when the user wants to implement, build, or write code test-first, "do TDD", follow "red-green-refactor", drive code from tests, choose the next test by the Transformation Priority Premise (TPP) or ZOMBIES ordering, or grow a feature behavior-by-behavior with tests leading. This skill writes and changes code; it does not produce a test plan document (use automated-test-planning, or manual-test-planning for a plan a person runs by hand), review or audit existing code (use code-review), restructure existing code outside a TDD loop (use refactor, or plan-a-change to plan a multi-module restructure), specify what a feature should do (use plan-a-feature), or find the root cause of a bug (use investigate). Runs its loop to completion without pausing for review; to review each behavior as it lands, use pairing.

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Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).

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Automatically migrate Python web applications between frameworks (Flask → FastAPI, Django → FastAPI). Use when you need to migrate an existing web application to a modern framework while preserving functionality. The skill analyzes the codebase, updates routes, handlers, configuration, dependency injection patterns, and tests. Creates git commits for each migration phase and generates a comprehensive summary of all changes. Supports automatic dependency updates, code transformations, and test adaptations.

日本語の概要は準備中です。原文の説明を表示しています。