952 skills · sorted by relevance
Create output schemas, definitions of result fields and display settings, for an Apify Actor by inspecting its source code. Generate dataset and output files, add key-value storage definitions when needed, and update actor.json to reference them.
Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.
Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.
Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.
Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.
Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.
Query documents and conversations stored in cognee to retrieve answers, supporting passages, or context for an AI model. This skill guides search method selection, source filtering, result interpretation, and troubleshooting empty or unexpected results.
Run nf-core workflows on local sequencing files or public GEO/SRA datasets to analyze gene expression, genetic variants, or chromatin accessibility. The skill guides environment checks, sample table preparation, workflow configuration, execution, and output verification.
Create realistic sample data for testing, development, and demos. Specify fields, record counts, value ranges, and business rules, then receive CSV, JSON, SQL INSERT statements, or a Python generator script with validation and usage instructions.
Develop varied ideas for names, writing angles, and problem solving by exploring five candidates internally and presenting the strongest. It also expands small example collections into diverse datasets for evaluation, training, or testing.
Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.
Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
Guides adapting OpenPI robot models to custom datasets and serving them to produce actions from observations. Covers JAX and PyTorch training, checkpoint conversion, and troubleshooting for normalization statistics and GPU memory.
Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.
Guides LLM fine-tuning with Axolotl, from YAML configuration and dataset formats to distributed training across GPUs. Covers LoRA and QLoRA, methods that train on preferences, multimodal support, and troubleshooting using documentation references and code examples.
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
Read and summarize AI research papers from Hugging Face or arXiv links and identifiers. Retrieve paper content alongside structured details about authors, related models, datasets, Spaces, and implementation links to support further investigation.
Search Hugging Face Hub for models, datasets, papers, and AI apps. Inspect documentation, use hosted apps for image generation or transcription, and run or schedule code on cloud CPUs and GPUs through an MCP server connection.
Register arXiv papers on Hugging Face Hub, connect them to models and datasets, and manage authorship claims and profile visibility. Create research articles from Markdown templates and convert them into HTML for sharing.
Calculate the count, sum, minimum, maximum, and average of supplied numbers or datasets, then present the results in a clear, structured format. Analysis results can also be saved to a file when requested.
Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.