agent-creator-en-v2

Use llamactl - a CLI tool for LlamaAgents

run-llama

Use llamactl to initialize, locally preview, deploy and manage LlamaIndex workflows as LlamaAgents. Required llama-index-workflows and llamactl to be installed in the environment.

Classify files according to specific rules

run-llama

Invoke this skill BEFORE implementing any text/document classification task to learn the correct llama_cloud_services API usage. Required reading before writing classification code." Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.

PDF Processing

run-llama

Invoke this skill BEFORE implementing any text extraction/parsing logic to learn how to use LlamaParse to process any document accurately. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.

Extract structured data from unstructured files (PDF, PPTX, DOCX...)

run-llama

Invoke this skill BEFORE implementing any structured data extraction from documents to learn the correct llama_cloud_services API usage. Required reading before writing extraction code. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.

opentrons-integration

K-Dense-AI

Lab automation platform for Flex/OT-2 robots. Write Protocol API v2 protocols, liquid handling, hardware modules (heater-shaker, thermocycler), labware management, for automated pipetting workflows.

omero-integration

K-Dense-AI

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

labarchive-integration

K-Dense-AI

Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.

get-available-resources

K-Dense-AI

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

context-initialization

K-Dense-AI

Always Auto-invoked skill that creates/updates workspace AGENT.md to instruct the agent to always search for existing skills before attempting any scientific task.

scikit-survival

K-Dense-AI

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

pymc-bayesian-modeling

K-Dense-AI

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

paper-2-web

K-Dense-AI

This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.