Results for “sca”

Scaling Laws for Neural Language Models

Nocaps: Novel Object Captioning at Scale

SVIT: Scaling up Visual Instruction Tuning

Masked Autoencoders Are Scalable Vision Learners

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

Scaling Vision with Sparse Mixture of Experts

CapsFusion: Rethinking Image-Text Data at Scale

Scaling Vision Transformers to 22 Billion Parameters

ImageNet: A Large-Scale Hierarchical Image Database

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identific...

Use to surface fraud, scam and financial-crime signals that customers describe to support before detection systems see them, and to check whether agents recognised and routed them. Trigger for "are customers reporting scams", "new scam pattern targeting our customers", "did we spot the fraud signals", authorised push payment scams, "customer was coached by someone on the phone", or fraud reports arriving through support.

Scans root uv.lock and package-lock.json files with the pinned osv-scanner CLI and emits deterministic advisory records. Opt-in: copy this directory into <home>/skills/dependency-advisory-watch/ and install osv-scanner.

Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures.

Perform forensic on-chain analysis of EVM tokens to detect scams, rug pulls, and soft rugs by cross-referencing on-chain state against team narratives.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scans Substrate/Polkadot pallets for 7 critical vulnerabilities including arithmetic overflow, panic DoS, incorrect weights, and bad origin checks. Use when auditing Substrate runtimes or FRAME pallets.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scans a codebase for security vulnerabilities using CodeQL's interprocedural data flow and taint tracking analysis, with support for multiple languages, scan modes, and data extension models.

Provides expert guidance on enterprise-grade Scala development, covering functional programming, distributed systems, and big data processing with frameworks like Pekko, Akka, Spark, ZIO, and Cats Effect.

EVA-CLIP: Improved Training Techniques for CLIP at Scale

phase: 72

Scans Cairo/StarkNet smart contracts for 6 critical vulnerability patterns including arithmetic overflow, L1-L2 messaging issues, and signature replay. Use when auditing StarkNet projects.

Scaffold plugin directories for Codex with a required plugin.json manifest, optional folders, and marketplace entries.

Use this skill when

Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans.