Loading...
Loading...
Found 365 Skills
Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data quality before training.
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.
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.
LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization
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.
Deterministic 3-phase GitHub PR review comment extraction: Authenticate, Mine, Validate. Use when mining tribal knowledge from PR reviews, extracting coding standards from review history, or building datasets for the Code Archaeologist agent. Use for "mine PRs", "extract review comments", "tribal knowledge", or "PR review history". Do NOT use for analyzing patterns, generating rules, or interpreting comments — that is the Code Archaeologist agent's responsibility.
Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
Expert skill for using Future AGI — the open-source end-to-end platform for evaluating, observing, and improving LLM and AI agent applications with tracing, evals, simulations, datasets, gateway, and guardrails.
Find and retrieve proteomics datasets from public repositories including MassIVE and ProteomeXchange (which aggregates PRIDE, PeptideAtlas, jPOST, and iProX). Search by species, keyword, or accession. Get detailed dataset metadata including instruments, publications, species, modifications, and file counts. Use when asked to find proteomics datasets, search for mass spectrometry data, look up ProteomeXchange or MassIVE accessions, or discover publicly available proteomics experiments for a given organism or topic.
N-dimensional labeled arrays for geoscience data. Read/write NetCDF, work with climate and oceanographic datasets, perform multi-dimensional analysis with labeled coordinates. Use when Claude needs to: (1) Read/write NetCDF or Zarr files, (2) Work with multidimensional arrays with labeled dimensions, (3) Analyze climate, ocean, or atmosphere data, (4) Compute temporal aggregations (daily/monthly/annual means), (5) Perform area-weighted statistics, (6) Process large datasets with Dask, (7) Apply CF conventions to scientific data.
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
Quick BI-SmartQ skill with multiple data analysis capabilities: 1. **File Q&A**: Upload Excel/CSV files for intelligent analysis via Quick BI API 2. **Dataset Q&A**: Natural language queries on Quick BI platform datasets, with automatic intelligent table selection and matching 3. **Document Parsing**: Parse PDF/Word/Excel/CSV/images, extract text, and support extracting key fields to generate structured Excel 4. **Dashboard Skill Generation**: Auto-convert QuickBI dashboards into data query skills 5. **Data Insight**: Deep data insight analysis on Quick BI datasets 6. **Data Report**: Auto-generate professional data reports based on analysis results Use when users mention data analysis, smart Q&A, querying data, file analysis, document parsing, dashboard skills, data insight, or data reports.