332건 · 관련도순
Plan data ingestion and transformation workflows with Apache Airflow, Spark, and dbt. Get guidance on processing changed records, validating data quality, and rerunning failed or historical jobs without creating duplicate results.
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Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.
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Clean, combine, summarize, and reshape tabular data with pandas DataFrames, Python's table-like data structures. Produces transformation code and validation checks, including time series processing and memory-conscious approaches for large datasets.
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Build reversible scroll-controlled visual transformations with a pinned or sticky stage, normalized progress, and video, image-sequence, canvas, SVG, or DOM renderers. Use for hero transformations, product assembly, interface state walkthroughs, object rotation, diagrams, or photo sequences that must move forward and backward with native scrolling.
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Problem-solving strategies for natural transformations in category theory
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Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.
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Performs code upgrades, migrations, and transformations using the AWS Transform (ATX) CLI. Use when upgrading language versions, migrating AWS SDKs, migrating frameworks (Angular, Vue.js, Spring Boot, React), upgrading libraries, optimizing performance, migrating x86 to Graviton, analyzing codebases / generating documentation, or defining custom transformations with natural language. Runs locally on a few repositories or at scale across hundreds via AWS Batch/Fargate.
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Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.
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Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
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Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and reaction templates, with explicit handling of atom mapping, RDChiral template extraction, product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction data.
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Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
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Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
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Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
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Plan and execute a brand transformation — from diagnosis to new brand definition to rollout. Use when the user says "rebrand", "rebranding", "brand refresh", "update the brand", "modernize the brand", "our brand is outdated", "we've outgrown our brand", "the brand needs to change", "brand transformation", "pivot the brand", "we're changing the brand", "the old brand doesn't fit anymore", or when a brand has evolved in direction, audience, or market position and needs its identity to catch up.
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Use when the user wants to apply a named Higgsfield motion preset, asks about VFX presets, transformation effects, elemental effects, or transition presets. Contains the full named preset library with descriptions and prompt usage.
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Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
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Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation. Use when migrating databases, transforming data schemas, moving between database systems, implementing versioned migrations, handling data transformations, ensuring data integrity, or planning zero-downtime migrations.
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Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.
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Provides patterns for unit testing mappers, converters, and bean mappings. Validates entity-to-DTO and model transformation logic in isolation. Generates executable mapping tests with MapStruct and custom converter test coverage. Use when writing mapping tests, converter tests, entity mapping tests, or ensuring correct data transformation between DTOs and domain objects.
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Generate test cases using metamorphic testing by applying transformations based on metamorphic properties. Use when you need to expand test suites, test programs without oracles, validate mathematical or algorithmic properties, or detect subtle bugs through input-output relationships. The skill takes a program, original test cases, and metamorphic properties as input, generates new test cases by applying transformations, executes tests, verifies outputs satisfy properties, reports violations and anomalies, and outputs an expanded test suite with property coverage summary. Supports multiple programming languages and property types.
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Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.
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Spatial reasoning, coordinate geometry, transformations, and trigonometry for geometric problem solving. Covers Euclidean geometry (points, lines, angles, congruence, similarity), triangle geometry (Pythagorean theorem, laws of sines and cosines), circle geometry (tangent lines, inscribed angles, power of a point), coordinate geometry (distance, midpoint, slope, conic sections), transformations as functions (rotation, reflection, translation, dilation), trigonometry (unit circle, identities, inverse functions), and geometric proof techniques. Use when reasoning about shapes, angles, spatial relationships, coordinate problems, or trigonometric computations.
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Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.
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Interactive prompt engineering coach that elevates vague prompts through Socratic dialogue, multiple transformation styles, and guided learning. Use when improving prompts, learning agentic engineering, or wanting coached guidance rather than automated transformation. NEVER auto-executes - always displays and asks first.
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