「transform」の検索結果

Think and work like an expert Environmental Chemist. Use when a task calls for Environmental Chemist judgment. Reasons from thermodynamic partitioning (K_oc/K_ow, Henry's law), pathway-specific half-lives, and mass balance through GC-MS/LC-MS/MS/ICP-MS analysis, EPI Suite and fugacity fate models, and EPA SW-846 QA/QC while treating blank contamination, matrix suppression, censored sub-LOD data, and unscoped transformation products as first-class failure modes.

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Think and work like an expert Deep Learning Scientist. Use when a task calls for Deep Learning Scientist judgment. Reasons from CNN/Transformer inductive bias, Li et al. loss landscapes, grokking/mode connectivity, and Kaplan/Chinchilla scaling (~20 tokens/param); designs ResNet/ViT/DiT/MoE/FlashAttention stacks with FLOPs-matched ablations; trains AdamW+cosine/WSD via Megatron-FSDP/DeepSpeed; evaluates FID/MMLU-Pro/MMLU-CF with lm-eval decontamination and Pineau/NeurIPS reproducibility checklists.

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Think and work like an expert Air Quality Scientist. Use when a task calls for Air Quality Scientist judgment. Reasons from source emissions through transformation, transport, and dose using SMOKE/MOVES inventories, WRF-driven CTMs like CMAQ and CAMx, PMF/ME-2 apportionment, and concentration-response functions, while treating rotational PMF ambiguity, AOD-to-PM bias in humid regions, uncalibrated low-cost sensors, and untreated wildfire exceptional events as first-class failure modes.

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End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.

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Statistical testing for differentially abundant proteins between conditions. Covers preprocessing (log2 transformation, normalization), limma and DEqMS workflows with empirical Bayes moderation, fold change shrinkage for accurate effect size estimation, and Python alternatives. Use when identifying proteins with significant abundance changes between experimental groups.

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Architectural thinking partner for an existing repository — scans the codebase, conducts a structured interview, agrees on current architectural state and recommended direction, and produces a shareable insights document. Scoped to one repository, module, or folder. Does not execute transformation — it orients. Use when the user says 'assess my codebase architecture', 'what direction should my codebase go', 'architecture compass', 'understand my architecture', 'audit architecture drift', 'architectural assessment', or 'help me understand what is wrong with my codebase'.

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Use for writing, reviewing, debugging, testing, or optimizing Python code that uses PyArrow arrays, schemas, Tables, RecordBatches, RecordBatchReaders, compute kernels, Dataset scans, Parquet, or Arrow IPC and C data interfaces. Trigger on schema drift, null/type semantics, chunking, batch streaming, predicate projection pushdown, conversion, and memory ownership. Do not use for pandas-only transformations, Polars-only expressions, generic SQL, Arrow Flight service design, C++/Rust Arrow, or Parquet work that does not use PyArrow.

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Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification, research, design, and planning.

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Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

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Use for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.

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Agent Media OS, the single skill for every media need in a HyperFrames project. Resolve BGM, SFX, image, icon, brand logo, voice, color grade, or LUT into a frozen local file or paste-ready block + ledger record (one verb, `resolve`); generate via TTS / music / image models when the catalog misses; produce voiceover, transcription, captions, and background removal through one shared audio engine; operate on media (cut / reframe / transform); and reuse assets across projects. Keeps search noise on disk, hands the agent one path or block. Use for any audio, image, icon, logo, voiceover, caption, color-grading, or media-asset need.

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CesiumJS spatial math - Cartesian3, Cartographic, Matrix4, Quaternion, Transforms, Ellipsoid, BoundingSphere, projections, coordinate conversions. Use when converting between coordinate systems, computing positions on the ellipsoid, performing spatial intersection tests, building model matrices, or working with geographic projections.

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Transform v0.6 specifications into context-window-sized work packages (EPICs) during PRD v0.7 Build Execution. Triggers on requests to create epics, scope work, break down implementation, or when user asks "create epics", "scope work", "break down work", "context window sizing", "what to build first?", "implementation planning", "epic breakdown". Consumes API-, DBT-, FEA-, ARC-. Outputs EPIC- entries with objectives, ID references, dependencies, and context windows. Feeds v0.7 Test Planning.

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Transform validated pain points into articulated user value statements for PRD v0.1 Spark. Triggers on completing problem framing, defining user outcomes, articulating value propositions, or requests like "what value do users get", "define outcomes", "articulate the benefit", "finish v0.1", "pain to value", "what do they gain". Outputs CFD- entries tagged as value hypotheses with evidence tiers. Follows Problem Framing skill in workflow.

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Transform vague product ideas into evidence-anchored problem statements for PRD v0.1 Spark. Triggers on starting new products/features, validating market opportunities, drafting PRD Why sections, or requests like "frame the problem", "define pain points", "write problem statement", "start v0.1", "what problem are we solving". Outputs structured problem tables with CFD evidence IDs.

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You are an expert in Amazon EventBridge, the serverless event bus for building event-driven architectures. You help developers route events between AWS services, SaaS applications, and custom microservices using event rules, patterns, transformations, dead-letter queues, and scheduling — decoupling producers from consumers with content-based routing that scales automatically.

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Read, transform, analyze, and generate Excel and CSV files. Use when a user asks to open a spreadsheet, process Excel data, merge CSVs, create pivot tables, clean up data, convert between Excel and CSV, add formulas, filter rows, or generate reports from tabular data. Handles .xlsx, .xls, and .csv.

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Plans and scripts data migrations between SQL databases: schema mapping, type conversion, batched and resumable loading, validation and cutover. Use when the user mentions "data migration," "database migration," "migrate from MySQL to PostgreSQL," "schema migration," "ETL pipeline," "data transfer," "database consolidation," "legacy migration," or "move data between databases." Covers schema analysis, mapping, transformation, batch processing, validation, and cutover planning. For query optimization during migration, see sql-optimizer.

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Parse and generate CSV files with the csv package — stream large files, handle custom delimiters, transform records, validate data, and generate CSV output from objects. Use when tasks involve data import/export, ETL pipelines, processing uploaded CSV files, or generating downloadable reports.

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Parse and extract data from HTML with Cheerio. Use when a user asks to scrape static web pages, parse HTML files, extract data from HTML, build a web scraper for server-rendered pages, extract text or links from HTML documents, parse RSS/XML feeds, transform HTML content, or process HTML emails. Covers jQuery-style selectors, DOM traversal, text extraction, attribute parsing, and integration with HTTP clients for web scraping pipelines.

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You are an expert in Contentful, the API-first content platform for enterprise teams. You help developers integrate Contentful's Content Delivery API (CDN-backed, read), Content Management API (write), and Content Preview API (draft content) into websites and apps — using typed content models, localization, rich text rendering, image transformations, and webhooks for build triggers.

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Manage images and videos with Cloudinary. Use when a user asks to optimize images, add image transformations, implement responsive images, upload media, or serve optimized assets from a CDN.

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Draw 2D graphics with the HTML5 Canvas API — shapes, text, images, gradients, transformations, pixel manipulation, and offscreen rendering. Use when tasks involve generating images server-side (with node-canvas), creating charts, image compositing, watermarking, or browser-based drawing applications.

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Process multiple documents in bulk with parallel execution. Use when a user asks to batch process files, convert many documents at once, run parallel file operations, bulk rename, bulk transform, or process a directory of files concurrently. Covers parallel execution, error handling, and progress tracking.

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。