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Agent skills

Skills you can use with AI coding agents, indexed from public GitHub repositories.

Latest Most Popular

transcribe-audio-podcast-interviews
drshailesh88/integrated_content_OS 2
literature-review
drshailesh88/integrated_content_OS 2
research-paper-extractor
drshailesh88/integrated_content_OS 2
clinical-reports
drshailesh88/integrated_content_OS 2
deep-researcher Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources. Uses file-based research tracking, parallel investigation threads, and context-efficient patterns for deep investigations. ALL MEDICAL CITATIONS FROM PUBMED MCP ONLY.
drshailesh88/integrated_content_OS 2
content-marketing-social-listening Comprehensive content marketing toolkit for discovering viral content opportunities, social listening, knowledge gap analysis, and content demand assessment across platforms. Use when researching trending topics in a niche, finding what's going viral, assessing content knowledge gaps, planning content calendars, or identifying high-demand content opportunities. Integrates with perplexity-search for real-time trend data and research-lookup for deep analysis.
drshailesh88/integrated_content_OS 2
clinical-decision-support
drshailesh88/integrated_content_OS 2
content-trend-researcher Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis
drshailesh88/integrated_content_OS 2
pufferlib This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
drshailesh88/integrated_content_OS 2
fluidsim Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
drshailesh88/integrated_content_OS 2
metabolomics-workbench-database Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
drshailesh88/integrated_content_OS 2
geniml This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
drshailesh88/integrated_content_OS 2
zinc-database Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
drshailesh88/integrated_content_OS 2
astropy Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
drshailesh88/integrated_content_OS 2
pyhealth Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
drshailesh88/integrated_content_OS 2
research-lookup Look up current research information using Perplexity's Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.
drshailesh88/integrated_content_OS 2
drugbank-database Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
drshailesh88/integrated_content_OS 2
pennylane Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
drshailesh88/integrated_content_OS 2
exploratory-data-analysis Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
drshailesh88/integrated_content_OS 2
denario Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
drshailesh88/integrated_content_OS 2
statsmodels Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
drshailesh88/integrated_content_OS 2
torch-geometric Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
drshailesh88/integrated_content_OS 2
scientific-visualization Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
drshailesh88/integrated_content_OS 2
networkx Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
drshailesh88/integrated_content_OS 2