Results for “transform”

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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Otimiza atenção em transformers com Flash Attention para ganho de 2-4x em velocidade e redução de 10-20x em memória. Use ao treinar/executar transformers com sequências longas (>512 tokens), ao encontrar problemas de memória GPU com atenção, ou quando precisa de inferência mais rápida. Suporta SDPA nativo do PyTorch, biblioteca flash-attn, H100 FP8 e sliding window attention.

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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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Expert en transformation digitale (strategy, change management, technology adoption, maturity assessment)

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Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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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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Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data warehouse tuning (use data-warehouse-optimizer).

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Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)

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Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.

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Test how API-accessible language models respond to attempts to bypass safety filters. The skill covers prompt templates, input transformations, parallel response comparisons, and Hermes configuration, with automated scoring of refusals, answer quality, and speed.

• Evaluating model resistance to safety-filter bypass attempts
• Comparing responses to transformed inputs
• Comparing refusals, answer quality, and speed across models

Explore concepts learned by language models using sparse autoencoders, which separate internal activity into a small set of features. Analyze pretrained models, train custom autoencoders, assess reconstruction quality, and experiment with feature based output adjustments.

• Exploring features with a pretrained SAE
• Training an SAE for a chosen model layer
• Comparing feature responses across input texts

Fix Python type checking failures found locally or in CI, the automated checks run on code changes. It identifies affected files, applies targeted fixes to type declarations and checks, then reruns validation.

• Fix type checking failures reported by CI
• Resolve errors involving values that may be None
• Add a directory to automated type checking

Connect agent tasks into a sequence where each step receives the previous output. Use custom prompts or predefined workflows to analyze code, plan refactoring, generate tests, optimize performance, or transform data through multiple stages.

• Turn code analysis into an improvement plan
• Prioritize and refactor existing code
• Find test gaps and generate unit tests

Explain a trading model's prediction by ranking the input features that contributed to its signal. Returns a Markdown table and saves an attribution record with calculation settings, adding a digital signature when a signing key is available.

• Reviewing which inputs drove a trading signal
• Saving signed records of model explanations
• Reproducing rankings with the same signal and seed

Assess tasks before execution to identify transformations eligible to skip LLM calls. Report the eligible share, recommended models for remaining tasks, and estimated upper costs, then save the routing results for later cost reports.

• Assess similar tasks before a batch run
• Review LLM spending on simple code transformations
• Check the share eligible for Agent Booster

Connect agent tasks in sequence, passing each result into the next step. Build custom workflows or use predefined pipelines for code analysis, refactoring, testing, and optimization, with execution progress, timing, and consolidated results.

• Turn code analysis into an improvement plan
• Prioritize and carry out refactoring
• Find test gaps and generate unit tests

Manage Cloudinary images, videos, and files with natural language requests. Organize folders, configure upload rules, inspect assets, generate transformed versions, and check storage and bandwidth usage through Rube MCP and Composio.

• Organizing campaign assets into folders
• Setting upload rules for product images
• Generating cropped images and thumbnails

Test your understanding of a phase in AI Engineering from Scratch through eight multiple-choice questions. Receive a score, explanations for incorrect answers, and specific lessons to review before continuing your studies.

• Checking your understanding after completing a phase
• Assessing readiness for the next phase
• Finding lessons to review from incorrect answers

Animate characters built from SVG parts using GSAP and CSS transforms. Coordinate gestures and facial expressions, keep joints connected, and control playback by frame in Remotion or validate browser previews with HyperFrames.

• Coordinate head and arm movements
• Check blinks, gaze, and mouth shapes
• Control a GSAP timeline by frame in Remotion

Improve GSAP animation performance by reviewing animated properties, browser layout work, and simultaneous updates. Use transforms, grouped updates, reusable animations, and targeted cleanup to reduce stuttering in lists, mouse followers, and scroll effects.

• Reviewing stuttering GSAP animations
• Reducing animation load in long lists
• Optimizing mouse-following effects

Clean messy tabular data through OpenRefine using command-line operations. Import files, inspect rows, apply reusable JSON transformation histories, and export cleaned CSV or TSV data, with session support for undoing and redoing changes.

• Remove extra spaces from names in a CSV file
• Reuse the same cleaning steps on another dataset
• Inspect sample rows before and after transformation

Inspect and transform local DICOM medical imaging files, including metadata, image frames, and compression formats. Generate technical inventories and preview images, check installed codecs, and review pseudonymized derivatives without treating those checks as proof of de-identification.

• Create a technical inventory of local DICOM files
• Check compression support and frame memory needs
• Export a frame as a non-diagnostic preview

Inspect supported local scientific files before modeling, summarize structure and missing values, and flag possible data leakage or outlier effects. Produces bounded aggregate reports with identifiers obscured and provides a structured analysis report template.

• Check local CSV or TSV data before modeling
• Audit missing values and overlap between data splits
• Compare outlier influence and transformation sensitivity