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Found 329 Skills
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
Use this for exploratory data analysis (EDA), generating visualizations, finding trends, and deriving insights from datasets using Python (Pandas/Seaborn/Plotly) or SQL.
Use this skill for ANY question about creating test or evaluation datasets for LangChain agents. Covers generating datasets from traces (final_response, single_step, trajectory, RAG types), uploading to LangSmith, and managing evaluation data.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Implements efficient API pagination using offset, cursor, and keyset strategies for large datasets. Use when building paginated endpoints, implementing infinite scroll, or optimizing database queries for collections.
Detects and redacts Personally Identifiable Information (PII) like emails, phone numbers, and credit cards. Use when cleaning logs, datasets, or communications to comply with GDPR/CCPA privacy standards.
Resolves shared ecosystem environment constants (HuggingFace credentials, dataset repo IDs, project root path) for any plugin without depending on internal shared libraries. V2 enforces Token Leakage constraints.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
XAF Memory Leak Prevention - event handler symmetry (OnActivated/OnDeactivated/Dispose), ObjectSpace scoped disposal with using statement, batch processing large datasets, IDisposable pattern for controllers with List<IDisposable> tracker, WeakEventSubscription, static reference anti-patterns, CollectionSource disposal, Session/HttpContext/Application anti-patterns (WebForms), ObjectSpacePool, controller lifecycle tracking, NavigationMonitor, warning signs, diagnostic tools (dotMemory, PerfView, XAF Tracing). Use when diagnosing memory leaks, auditing controller disposal, reviewing ObjectSpace lifetime, or reviewing Session usage in DevExpress XAF applications.
Design and architect Goldsky Turbo pipelines. Use this skill for 'should I use X or Y' decisions: kafka source vs dataset source, streaming vs job mode, which resource size (xs/s/m/l/xl/xxl) for my workload, postgres vs clickhouse vs kafka sink, fan-in vs fan-out data flow, one pipeline vs many, dynamic table vs SQL join, how to handle multi-chain deployments. Also use when the user asks 'what's the best way to...' for a pipeline design problem, or is unsure how to structure their pipeline before building it.