Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
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Access the Human Metabolome Database (HMDB, 220K+ metabolites), searching by name, HMDB ID, or structure to retrieve chemical properties, biomarker data, NMR/MS reference spectra, and associated pathways. Use when identifying a human metabolite, looking up its biomarker or disease associations, matching NMR/MS spectra, or running metabolomics annotation. Part of the AlterLab Academic Skills suite.
Runs automated LLM-driven hypothesis generation and testing on tabular datasets with HypoGeniC, combining literature insights with data-driven testing. Use when systematically exploring hypotheses about patterns in empirical data (for example deception detection or content analysis). For manual hypothesis formulation use alterlab-hypothesis-gen; for open-ended creative ideation use alterlab-scientific-brainstorm. Part of the AlterLab Academic Skills suite.
Formulates structured, testable hypotheses from experimental observations using a scientific-method framework — derives predictions, proposes mechanisms, and designs experiments to test them, then renders a LaTeX report. Use when turning observations or data into falsifiable, mechanistic hypotheses, competing-explanation sets, testable predictions, or experimental designs. For a standalone systematic literature review or evidence synthesis (not a means to hypotheses) use alterlab-literature-review; for open-ended brainstorming before any specific observation use alterlab-scientific-brainstorm; to evaluate an existing manuscript use alterlab-peer-review. Part of the AlterLab Academic Skills suite.
Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs), searching by TF name, species, or class, scanning DNA sequences for binding sites, and comparing matrices. Use when doing motif analysis, regulatory genomics, transcription factor binding prediction, or interpreting regulatory/non-coding GWAS variants. Part of the AlterLab Academic Skills suite.
Provide direct REST API access to KEGG (academic use only) for pathway analysis, gene-to-pathway and compound-to-pathway mapping, metabolic reactions, KEGG Orthology (KO), drug-drug interactions, and ID conversion. Use when querying KEGG pathways, mapping genes/compounds to metabolic maps, or running KEGG pathway enrichment via raw HTTP/REST; for protein-protein interaction networks prefer alterlab-string-db, for protein sequences and annotations prefer alterlab-uniprot, and for Python workflows spanning many databases prefer alterlab-bioservices instead. Part of the AlterLab Academic Skills suite.
Integrates the LabArchives electronic lab notebook (ELN) via its REST API — access notebooks, manage entries and attachments, back up notebooks, and bridge to Protocols.io, Jupyter, and REDCap. Use when automating LabArchives ELN workflows, programmatically reading/writing notebook entries or attachments, backing up a LabArchives notebook, or syncing it with Protocols.io, Jupyter, or REDCap. Part of the AlterLab Academic Skills suite.
Builds and deploys bioinformatics pipelines on the LatchBio platform using the Latch SDK — author workflows with @workflow/@task decorators, handle LatchFile/LatchDir I/O, register serverless workflows, configure CPU/GPU task resources, organize data in the Latch Registry, and wrap Nextflow/Snakemake pipelines. Use when developing or deploying a Latch SDK workflow, sizing task resources, working with the Registry, or porting a Nextflow/Snakemake bioinformatics pipeline onto LatchBio. Not for DNAnexus (dxpy/dx CLI) or generic Flyte. Part of the AlterLab Academic Skills suite.
Generates comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner), with professional LaTeX formatting, extensive visuals via scientific-schematics and generate-image, data gathering through research-lookup, and multi-framework strategic analysis (Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, BCG Matrix). Use when producing a market analysis, competitive landscape, industry report, market-sizing study, or consulting-style strategic deliverable. NOT for a single focused, source-cited research question with no report/frameworks (use alterlab-deep-research) or for pulling raw financial data points from an API (use alterlab-alpha-vantage). Part of the AlterLab Academic Skills suite.
Converts files and Office documents to clean, LLM-friendly Markdown with Microsoft MarkItDown (markitdown CLI, Python API, and markitdown-mcp server), supporting PDF, DOCX, PPTX, XLSX/XLS, images (EXIF metadata, LLM descriptions), audio (transcription), HTML, CSV, JSON, XML, ZIP archives, EPUB e-books, Outlook MSG, Jupyter notebooks, and YouTube transcript URLs, with OCR through the markitdown-ocr plugin or Azure Document Intelligence. Use when converting a document, PDF, slide deck, spreadsheet, scanned image, audio file, web page, or e-book into Markdown text for ingestion or LLM processing, extracting text from scans via OCR, transcribing audio, or batch-converting mixed file formats to token-efficient Markdown. Part of the AlterLab Academic Skills suite.
Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use alterlab-pyopenms. Part of the AlterLab Academic Skills suite.
Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound library, flagging PAINS or reactive groups, or assessing drug-likeness of candidate molecules. Part of the AlterLab Academic Skills suite.
Runs Python code in the cloud with Modal — serverless containers, on-demand GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that need GPU acceleration or dynamic scaling. Part of the AlterLab Academic Skills suite.
Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations across species, integrating OMIM, ORPHANET, HPO, ClinVar, and model organism databases. Use when discovering rare disease genes, mapping phenotypes to genes, modeling disease across species, or looking up HPO terms. Part of the AlterLab Academic Skills suite.
Processes and analyzes physiological biosignals with the NeuroKit2 Python toolkit — ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements, or when computing heart rate variability (HRV), event-related potentials, complexity measures, autonomic nervous system assessment, or multi-modal physiological signal integration for psychophysiology research. Part of the AlterLab Academic Skills suite.
Manages microscopy image data on an OMERO server via the OMERO Python API (BlitzGateway) — access images, retrieve datasets, read pixel data, manage ROIs and annotations, and batch-process. Use when connecting to an OMERO server, pulling microscopy images or datasets, analyzing pixels, managing ROIs/annotations, or running high-content screening and microscopy workflows. Part of the AlterLab Academic Skills suite.
Runs and scripts Open Notebook, a self-hosted open-source alternative to Google NotebookLM with a full REST API, for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 18+ AI providers including OpenAI, Anthropic, Google, Ollama, LM Studio, Groq, and Mistral, with data kept on your own infrastructure through self-hosting. For a one-shot file-to-Markdown conversion (no notebook, chat, or search), use alterlab-markitdown instead. Part of the AlterLab Academic Skills suite.
Query the Open Targets Platform GraphQL API for target-disease associations, tractability and safety data, genetics/omics evidence, and known drugs. Use when identifying or prioritizing therapeutic drug targets, assessing target druggability/safety, or gathering target-disease evidence for drug discovery. Part of the AlterLab Academic Skills suite.
Writes liquid-handling protocols for Opentrons OT-2 and Flex robots using the official Opentrons Protocol API v2, with full access to v2 features for production-grade, officially compatible protocols. Use when authoring or running protocols specifically for Opentrons hardware. For multi-vendor automation or broader equipment control use pylabrobot instead. Part of the AlterLab Academic Skills suite.
Search the web, run deep research, and extract content from known URLs via Parallel Web Systems — the Chat API (Beta, OpenAI-compatible `base`/`core` research models) for synthesized answers with inline citations and the Extract API v1 for URL content — with notes on the raw Search, Task, and Responses APIs. Use when running general web searches, current-events/market/technical lookups, broad information gathering, comprehensive research reports, or verifying a specific URL's content (requires PARALLEL_API_KEY). For scholarly paper retrieval or dual-backend academic lookup that auto-routes to Perplexity prefer alterlab-research-lookup instead. Part of the AlterLab Academic Skills suite.
Access the RCSB Protein Data Bank (PDB) for EXPERIMENTALLY determined 3D structures (X-ray, cryo-EM, NMR) of proteins and nucleic acids — searching by text, sequence, or structure similarity and downloading coordinates in PDB/mmCIF format with metadata. Use when retrieving a structure by PDB ID, running sequence or structure similarity searches, or obtaining experimental coordinates for structural biology and drug discovery; for AI-PREDICTED structures of proteins lacking experimental data prefer alterlab-alphafold-db, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.
Run AI web searches with real-time, citation-grounded answers using Perplexity Sonar models (sonar, sonar-pro, sonar-pro-search agentic search, sonar-reasoning-pro) via LiteLLM and a single OpenRouter API key. Use when searching for current information or recent scientific literature, getting answers grounded in cited web sources, verifying a claim against current evidence, or reaching information beyond the model's training cutoff. Requires an OpenRouter API key. This is the direct single-backend Perplexity tool: for automatic routing between Perplexity and other backends use alterlab-research-lookup, and for structured per-paper experimental-data extraction (sample sizes, effect sizes, quality scores) use alterlab-bgpt-search. Part of the AlterLab Academic Skills suite.
Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.
Queries the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biomedical relationships across genes, drugs, diseases, phenotypes, pathways, and biological processes. Use when exploring drug-disease or gene-disease links, building disease-centric knowledge subgraphs, or sourcing relations for drug repurposing and precision-medicine analyses; also points to OptimusKG, the maintainers' successor graph, for new work. Part of the AlterLab Academic Skills suite.
Works with protocols.io through its REST API (v3 and v4 endpoints) and official MCP server — search and retrieve protocols by keyword, URI, or DOI; create private protocols, edit their metadata and steps, and publish them with a DOI; manage protocol and step discussions, workspaces, file-manager items, file uploads, experiment records, and organization exports. Use when discovering, drafting, publishing, or citing protocols.io protocols, recording protocol runs, or wiring protocols.io into lab documentation. Not for general ELN notebooks (use alterlab-benchling or alterlab-labarchive) or for driving liquid-handling robots. Part of the AlterLab Academic Skills suite.
Query PubChem via the PUG-REST API and PubChemPy across 119M+ compounds, searching by name, CID, or SMILES and retrieving molecular properties, bioactivity, and similarity/substructure matches. Use when looking up a chemical compound, converting names/SMILES to CIDs, fetching physicochemical properties, or running cheminformatics structure searches. Part of the AlterLab Academic Skills suite.
Scales reinforcement learning with PufferLib — high-throughput parallel training (PuffeRL), vectorized environments, and native multi-agent systems achieving 2-10x speedups over standard implementations. Use when scaling RL to millions of steps per second, running vectorized or multi-agent setups, building custom PufferEnv tasks, or integrating game environments (Atari, Procgen, NetHack, PettingZoo). For standard single-agent algorithm implementations (PPO/SAC/DQN) or quick prototyping prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.
Reads, writes, and manipulates DICOM (Digital Imaging and Communications in Medicine) medical imaging files with the pydicom Python library. Use when reading/writing/modifying DICOM data, extracting pixel data from CT, MRI, X-ray, or ultrasound images, anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM to other formats, handling compressed DICOM, or processing medical imaging datasets for PACS systems, radiology workflows, and healthcare imaging applications. Part of the AlterLab Academic Skills suite.
Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare (RETAIN, SafeDrug, GAMENet, Transformer, GAT/GCN). Part of the AlterLab Academic Skills suite.
Programs lab automation with PyLabRobot, a vendor-agnostic async Python framework that drives Hamilton STAR/Vantage, Tecan EVO, and Opentrons OT-2 liquid handlers plus plate readers, heater shakers, incubators, centrifuges, pumps, scales, and thermocyclers, with a chatterbox simulator and browser visualizer. Use when writing or simulating liquid-handling protocols in Python, controlling several instrument types from one script, or porting a protocol between robot vendors. For Opentrons-only protocols written with the official Opentrons API, use alterlab-opentrons instead. Part of the AlterLab Academic Skills suite.
Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling engineering design problems with competing objectives. Part of the AlterLab Academic Skills suite.
Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to alterlab-deepchem or alterlab-molfeat. Part of the AlterLab Academic Skills suite.
Scalable deep-learning training with PyTorch Lightning — organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, build data pipelines and callbacks, log to W&B or TensorBoard, and run distributed training (DDP, FSDP, DeepSpeed). Use when structuring PyTorch training loops, scaling neural-network training across GPUs/TPUs, or adding checkpointing, logging, and distributed strategies. Part of the AlterLab Academic Skills suite.
Interact with Zotero reference management libraries using the pyzotero Python client — retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero. Part of the AlterLab Academic Skills suite.
Builds, transpiles, and runs quantum circuits with Qiskit, IBM's quantum computing framework, including Qiskit Runtime primitives (Sampler/Estimator), circuit transpilation, and error mitigation on IBM Quantum hardware. Use when targeting IBM Quantum backends, transpiling circuits, running Runtime sessions or batches, or applying resilience/error mitigation. For Google Quantum AI hardware and NISQ circuits prefer alterlab-cirq; for gradient-trained quantum ML and hybrid quantum-classical models prefer alterlab-pennylane; for open-system Lindblad/master-equation dynamics prefer alterlab-qutip. Part of the AlterLab Academic Skills suite.
Query the Reactome REST API for pathway analysis, over-representation/enrichment, gene-to-pathway mapping, disease pathways, molecular interactions, and expression analysis. Use when running pathway enrichment on a gene list, mapping genes to curated biological pathways, or exploring disease pathways for systems biology studies. Part of the AlterLab Academic Skills suite.
Drives the Rowan cloud quantum-chemistry platform via its Python API for computational chemistry — pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2), with cloud compute and no local setup. Use when running DFT or semiempirical methods, neural network potentials (AIMNet2), molecular property or protein-ligand binding predictions, or automated computational chemistry pipelines. Part of the AlterLab Academic Skills suite.
Classical machine learning in Python with scikit-learn — algorithms, preprocessing, pipelines, and best-practice reference documentation. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, feature preprocessing, or building ML pipelines. Part of the AlterLab Academic Skills suite.
Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations, network traffic, or logistics. Part of the AlterLab Academic Skills suite.
This skill should be used when the user asks about "exhibition design", "wayfinding", "wayfinding design", "spatial graphics", "environmental design", "signage system", "event design", "museum design", "trade show design", "environmental graphics", "act as an exhibition designer", "exhibition designer mode", "gallery layout", "visitor flow", "ADA signage", "accessible signage", "directional signs", "information kiosk", "exhibition identity", "spatial hierarchy", "venue branding", "event branding", "pop-up design", "installation design", "experiential design", "exhibit booth", "conference design", "placemaking", or needs expertise in spatial graphics, wayfinding, exhibition and event visual systems, and environmental design. Part of the AlterLab FC Skills collection (VCD department).
This skill should be used when the user asks about "layout design", "editorial design", "grid system", "magazine layout", "book design", "publication design", "page layout", "spread design", "Swiss grid", "modular grid", "column grid", "act as a layout designer", "layout designer mode", "print design", "editorial layout", "page composition", "white space", "bleed and margins", "InDesign layout", "multi-page document", "annual report design", "catalog design", "zine design", or needs expertise in publication design, grid system construction, editorial layouts, and multi-page document architecture. Part of the AlterLab FC Skills collection (VCD department).
Production-grade audit of Amazon product listings against IDQ scoring, CDQ-relevant quality dimensions, Amazon content policy, mobile UX, indexing risk, and conversion best practices. Uses Bright Data MCP for live data. Accepts one or more ASINs and delivers a prioritized findings report with revenue-impact estimates and actionable fixes. Use this skill whenever someone provides ASINs and asks about listing quality, IDQ score, CDQ score, listing compliance, title issues, bullet problems, image requirements, A+ content, indexing, mobile preview, or conversion optimization. Trigger phrases include: "audit my listings", "check my ASINs", "what's wrong with my listing", "why isn't my product indexed", "review my Amazon content", "are my bullets compliant", "do my listings meet Amazon policy", "is my listing mobile-friendly". Requires: Bright Data MCP connection (web_data_amazon_product or scrape_as_markdown tools). Credits: Original framework by Ohad. Production hardening for AI Vault by Jay.
Screen navigation and Navigation reference for Android app development (Kotlin). Jetpack Compose, androidx. Navigation 3 (stable, NavDisplay, NavKey, ResultEventBus, DeepLinkMatcher), Navigation Compose (NavHost, composable, type-safe routes), Fragment navigation (NavHostFragment, Safe Args), deep links, App Links, assetlinks.json, predictive back (OnBackPressedDispatcher).
AI skill for building ApexCharts.js charts and data visualizations (targets v8). Use when the user asks to create, configure, or troubleshoot any chart using ApexCharts (line, area, bar, pie, donut, radialBar, scatter, bubble, heatmap, candlestick, boxPlot, violin, histogram, radar, polarArea, rangeBar, rangeArea, treemap, funnel, pyramid, gauge, sunburst, icicle, unit, waffle, waterfall, dumbbell, streamgraph, raincloud). Covers correct data formats, lifecycle, formatters, tree-shaking, SSR, and the feature platform (plugins, canvas renderer, custom series, small multiples, undo/redo, shareable views, themes, crossfilter, annotation authoring, storyboard, streaming, drilldown, highlight filter, radialBar targets). Knows which chart types and features ship in the default v8 bundle and which need an explicit import or the full bundle. In React / Vue / Angular projects, prefer the framework wrapper packages (`react-apexcharts`, `vue3-apexcharts`, `ng-apexcharts`) over the core API.
Work with ArcGIS Knowledge graphs for storing, querying, and visualizing connected data using KnowledgeGraphLayer, LinkChartView, and openCypher queries. Use for graph databases, relationship visualization, and entity exploration.
Transforms code into clean, testable, and maintainable architecture using SOLID principles, Clean Architecture, and proven design patterns. Use this skill when you need to: design a new feature with proper separation of concerns, refactor legacy or monolithic code, review architecture for SOLID violations, establish dependency-injection boundaries, plan a testing strategy, or document architectural decisions. Language-agnostic; includes concrete Swift/iOS examples from the Essential Developer methodology.
Answer questions about the Supabase docs app (apps/docs) using documented architecture, build pipeline, and review-pattern notes, and apply feature-design principles (codebase reuse, coding minimalism) when proposing or critiquing changes. Use when the user asks "how does X work in the docs app?", "where does Y live?", "is this approach OK for the docs app?", or before writing non-trivial changes under apps/docs/ — especially anything touching the MDX pipeline, markdown generation, content components, federated docs, or contributor-facing authoring patterns, or feature flags and experiments. Can answer architecture questions with Mermaid diagrams when helpful
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.