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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.

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• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build 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.

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• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

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.

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• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

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.

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• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

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.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

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.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

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.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

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.

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• Searching selected datasets or conversation sessions
• Retrieving supporting passages or model context
• Choosing a search method for a question

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.

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• Storing documents and asking questions from the terminal
• Estimating token usage and cost before importing data
• Managing datasets and saved configuration

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.

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• Analyze gene expression from RNA-seq data
• Identify genetic variants in WGS/WES data
• Analyze chromatin accessibility from ATAC-seq data

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.

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• Creating test data that follows business rules
• Preparing realistic sample data for demos
• Populating a test database

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.

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• Finding a distinctive product name
• Developing a fresh angle for a blog post
• Exploring solutions to a technical problem

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.

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• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

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.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

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.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

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.

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• Fine-tune OpenPI models on custom robot datasets
• Convert JAX checkpoints to PyTorch
• Serve policies for ALOHA, DROID, or LIBERO

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.

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• Prepare YAML settings for LLM fine-tuning
• Explore LoRA, QLoRA, and preference training options
• Check dataset formats and input handling

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.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

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.

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• Registering an arXiv paper on Hugging Face Hub
• Linking papers to models and datasets
• Managing authorship claims and profile visibility

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.

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• Compare models for a specific task
• Find training datasets
• Generate images or transcribe audio with Spaces

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.

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• Summarizing a paper from its URL or arXiv ID
• Finding models, datasets, and Spaces linked to a paper
• Checking authors and implementation links

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.

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• Check the count and total of supplied numbers
• Summarize minimum, maximum, and average values
• Get a basic numerical summary of a dataset

Configure the social-science-research plugin through guided questions about your field, institution, journals, and datasets. Save research preferences, researcher details, and R figure colors in project files for use in your research context.

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• Set up a newly installed research plugin
• Register journals and datasets for a research field
• Save key researchers for literature searches

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.

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• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures