Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and metabolic-engineering analyses on SBML genome-scale models. Part of the AlterLab Academic Skills suite.
Query Google Data Commons for public statistical data aggregated from global sources, resolving geographic entities and pulling time-series statistics. Use when working with demographic data, economic indicators, health statistics, or environmental data — population counts, GDP figures, unemployment rates, disease prevalence — or when resolving places to DCIDs and exploring relationships between statistical entities. Part of the AlterLab Academic Skills suite.
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or validating oncology drug targets. Part of the AlterLab Academic Skills suite.
Predicts protein-ligand binding poses with DiffDock diffusion-based molecular docking from PDB structures and SMILES, producing pose confidence scores for virtual screening and structure-based drug design. Use when docking ligands into a protein, generating binding poses, or screening compounds against a target; not for binding affinity prediction. Part of the AlterLab Academic Skills suite.
Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its structure/content/quality before deciding what analysis to run. Covers tabular (.csv .tsv .xlsx .parquet), arrays (.npy .npz .hdf5 .h5 .mat .fits), sequence/genomics (.fasta .fastq .sam .bam .vcf .bed .gff .gtf .h5ad), microscopy (.tif .nd2 .czi .lif .ims .dcm .nii), spectroscopy/MS (.mzML .mzXML .mgf .fid .jdx), chemistry (.pdb .cif .mol .sdf .xyz .gro), and proteomics/metabolomics (.pepXML .mzid .mzTab). For zero-shot forecasting of a series use alterlab-timesfm; to create/configure a chunked cloud array store use alterlab-zarr. Part of the AlterLab Academic Skills suite.
Accesses, analyzes, and extracts data from SEC EDGAR filings using the edgartools Python library. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings. Part of the AlterLab Academic Skills suite.
Run ESM protein language models — ESMC for embeddings and representations, ESMFold2 for structure prediction, and ESM3 for generative multimodal protein design across sequence, structure, and function — locally or through the hosted Biohub Platform API (formerly Forge). Use when working with protein sequences, structures, or function prediction, designing novel proteins, generating protein embeddings, performing inverse folding, or doing protein-engineering tasks. Part of the AlterLab Academic Skills suite.
Queries the FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources, covering GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, fetching U.S. or international economic indicators by FRED series ID, and academic research requiring historical economic time series. Part of the AlterLab Academic Skills suite.
Converts academic papers into promotional and presentation formats with the third-party Paper2All pipeline (LLM calls routed through OpenRouter/OpenAI) — interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster) from a paper PDF and its LaTeX sources. Use when disseminating a paper, preparing for a conference, building an explorable academic homepage, generating a video abstract, or auto-generating a print-ready poster from a finished paper. For a hand-built LaTeX poster use alterlab-latex-posters; for a talk deck use alterlab-scientific-slides. Part of the AlterLab Academic Skills suite.
Analyzes and manipulates materials with the pymatgen toolkit — crystal structures and molecules, phase diagrams and thermodynamic stability, electronic structure (band structures, DOS), surfaces and interfaces, and Materials Project database access. Use when working with crystal structures in materials science, converting between structure formats (CIF, POSCAR, XYZ), analyzing symmetry or space groups, computing phase diagrams, querying the Materials Project API, or handling VASP, Gaussian, or Quantum ESPRESSO output. Part of the AlterLab Academic Skills suite.
Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple spectral comparison and metabolite identification use matchms. Part of the AlterLab Academic Skills suite.
Look up current research and scholarly papers by auto-routing each query to the best backend — the Parallel Web Systems Chat API (general research) or Perplexity sonar-pro-search via OpenRouter (academic paper searches) — and save every result with citations to sources/. Use when finding papers, gathering research data, verifying scientific claims, or assembling citation lists and unsure which search backend fits. For plain general web search or extracting content from a known URL prefer alterlab-parallel-web; to call Perplexity directly with a chosen Sonar model prefer alterlab-perplexity. Part of the AlterLab Academic Skills suite.
Creative research ideation and exploration for open-ended brainstorming, surfacing interdisciplinary connections, challenging assumptions, and identifying research gaps. Use when starting early-stage research planning with no specific observations yet — for open-ended brainstorming sessions, exploring cross-disciplinary connections, or finding gaps. For formulating testable hypotheses from observations or data use alterlab-hypothesis-gen; for grading evidence or spotting design flaws use alterlab-scientific-thinking. Part of the AlterLab Academic Skills suite.
Analyze biological data with scikit-bio — sequence analysis and alignments, phylogenetic trees, alpha/beta diversity metrics (including UniFrac), ordination (PCoA), PERMANOVA statistics, and FASTA/Newick I/O. Use for microbiome and community-ecology analysis — computing diversity, distance matrices, and ordination from feature tables. Part of the AlterLab Academic Skills suite.
Survival analysis and time-to-event modeling in Python with scikit-survival. Use when working with censored survival data, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating predictions with concordance index or Brier score, handling competing risks, or implementing any time-to-event workflow. Part of the AlterLab Academic Skills suite.
Train deep generative models for single-cell omics with scvi-tools — probabilistic batch correction and integration (scVI), reference-mapping transfer learning (scArches), differential expression with uncertainty, and multimodal models (totalVI for CITE-seq, MultiVI for multiome). Use when correcting batch effects, integrating multimodal data, or doing advanced probabilistic single-cell modeling — for standard analysis pipelines use scanpy. Part of the AlterLab Academic Skills suite.
Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.
Graph Neural Networks with PyTorch Geometric (PyG) — node and graph classification, link prediction, GCN, GAT, and GraphSAGE layers, heterogeneous graphs, and molecular property prediction. Use when building or training GNNs for geometric deep learning on graph-structured data. Part of the AlterLab Academic Skills suite.
Out-of-core tabular analytics with Vaex — memory-mapped HDF5/Arrow/Parquet via vaex.open, lazy virtual columns, delayed single-pass aggregations on billion-row tables, binned histograms/heatmaps, and vaex.ml transformers on one machine. Vaex is in minimal-maintenance mode (vaex-core 4.19 from Sep 2025 supports only Python 3.9–3.12 and pandas < 3), so use it to run, fix, or migrate existing Vaex code; for new larger-than-RAM work prefer Polars lazy/streaming queries (alterlab-polars) or Dask (alterlab-dask). Use when a project already depends on Vaex, when opening memory-mapped HDF5/Arrow files, batching aggregations with delay=True, or porting a Vaex pipeline to Polars. Part of the AlterLab Academic Skills suite.
Inspect and compare offline AI evaluation experiments, diagnose case-level regressions, and follow scorer history across application, model, or prompt changes. Use when a user asks whether an offline run improved, why a case failed, how scores changed between runs, or how to publish externally computed offline results. Covers immutable scorer versions, comparable dataset cohorts, incomplete coverage, and selective payload reads.
Data layer reference for Android app development (Kotlin). Jetpack androidx. Room / Room 3 (androidx.room3) (Entity, Dao, Query, Migration, prepopulate, createFromAsset), DataStore (Preferences/Proto, Direct Boot), MediaStore, SharedPreferences, scoped storage, Retrofit, OkHttp.
Implement or refine UI motion, gestures, and transitions. Use when the user asks for animation, hover or press motion, drag or swipe behavior, transition timing, or reduced-motion alternatives. Mentioning a drawer, toast, or hover state alone does not request animation.
Manage Apidog project resources through Apidog CLI. Trigger scenarios - running API automation tests/test suites, querying/creating/updating/deleting endpoints, environments, schemas, mocks, branches and other project resources, importing/exporting API documentation, viewing test reports, managing runners, scheduled tasks, notifications and CI/CD configuration. CLI output is structured JSON and often includes agentHints.nextSteps; all commands support --help.
Use this skill whenever the user asks about Apple apps — Reminders, Calendar, Contacts, Notes, Mail, Messages (iMessage/SMS), or tmux sessions. This includes creating/completing reminders, checking/adding calendar events, looking up contacts, reading/writing notes, sending/reading email, reading/sending iMessages and texts, and capturing tmux session content. Also use this skill when the user mentions tasks, todos, scheduling, birthdays, free time slots, or end-of-day summaries. The bridges are CLI tools installed at ~/.claude/ that give Claude Code native access to these Apple apps on macOS.
Audit a full PhD or research-application package for declared-theme fit, coherence, missing evidence, narrative consistency, program fit, recommender risk, and deadline priorities. Use this skill whenever the user asks whether their whole application is ready, how to prioritize fixes before deadlines, whether their CV/SOP/research statement/faculty fit/recommenders align, whether the SOP and letters actually answer the programme's declared theme, specialisation, or track, or what weaknesses an admissions committee may notice across materials. Every audit runs the declared-theme screen first, before any academic- or faculty-fit finding, searching the web for the live official programme page whenever the target is not already known from the conversation.
Write and translate natural, grammatically correct Arabic — Modern Standard Arabic plus Egyptian, Levantine, Gulf, and Maghrebi dialects — that avoids machine-translation tells. Use whenever producing Arabic prose: writing original Arabic, translating into Arabic (especially from English), or editing/proofreading Arabic for grammar, spelling (hamza, ة/ه, ى/ي), agreement, collocations, punctuation, register, or naturalness. Use even when the user only asks to "say this in Arabic" or pastes Arabic text to fix.
Use when a business transaction spans multiple services or aggregates and no distributed-transaction primitive is available — phrases like "saga pattern", "distributed transaction", "compensating action", "choreographed saga", "orchestrated saga", "process manager", "should we use a saga here", "how do we roll back across services". Defines a saga as a sequence of local transactions tied together by compensating actions, with two implementation styles (choreography and orchestration).
Complete ArgoCD cluster bootstrapping skill for multi-repository GitOps environments. Use when provisioning new Kubernetes clusters, registering clusters with ArgoCD, configuring ApplicationSets, setting up cluster secrets, or troubleshooting cluster connectivity issues.
Run a corpus-scale, STATS-ONLY PII audit over a folder of session transcripts LOCALLY and produce an aggregate report — counts by type and by layer, the per-session redaction-rate distribution, document lengths, and a coarse residual proxy. Use when the user says "audit my sessions", "scan folder for PII", "how much PII across these transcripts", "PII stats for my corpus", "is my redaction holding at scale", or points at a directory of transcripts and asks how much personal data it contains. Fully local — raw text never leaves the machine; the report carries ZERO PII values, transcript substrings, or filenames (only anonymized own-NN ids and counts), so the aggregates are safe to surface. Run it on a RED (raw) corpus to size the PII, or on a GREEN (already-redacted) corpus to check residual leakage.
When the user wants to optimize automotive manufacturing supply chains, manage tier suppliers, implement JIT production, or handle automotive-specific logistics. Also use when the user mentions "automotive manufacturing," "OEM supply chain," "tier 1/2/3 suppliers," "sequenced parts delivery," "just-in-time automotive," "vehicle assembly," or "automotive aftermarket." For general manufacturing, see production-scheduling. For lean principles, see lean-manufacturing.
Provides AWS CloudFormation patterns for security infrastructure including KMS encryption, Secrets Manager, IAM security, VPC security, ACM certificates, parameter security, outputs, and secure cross-stack references. Use when implementing security best practices, encrypting data, managing secrets, applying least privilege IAM policies, securing VPC configurations, managing TLS/SSL certificates, and implementing defense in depth strategies.
Populates the local Azure SQL Database container's database (appdb) with realistic sample/test data so a developer has something to build against. Use when the user says "seed the database", "add test data", "populate the dev database", "generate sample data", "fake data", "load fixtures", "insert test rows", "write a seed script", or "bulk load a CSV". This is the Azure SQL engine (EngineEdition 5), not the mssql/server SQL Server image. Distinct from azuresql-db-scaffold (which does a single seed.sql step while bootstrapping an app) and azuresql-db-import (which loads a .bacpac). Reach for this whenever an existing appdb needs volume, fixtures, or believable rows.
Writes integration tests that run IN CODE against a real Azure SQL Database container, spun up per test or per suite with Testcontainers and torn down after. Use when the user asks for "integration tests against SQL", "Testcontainers", "spin up a database for tests", "an ephemeral test database", "a test database per test", "xUnit/Jest/pytest with a real database", or "a database fixture". Use this INSTEAD of the Testcontainers MsSql preset (mcr.microsoft.com/mssql/server), because this is the Azure SQL engine (EngineEdition 5). For wiring the engine into a CI pipeline via service containers or workflow YAML instead, use azuresql-db-ci.
Task tracking system for agents via Backlog.md CLI. Use when creating deferred issues during implementation, filing audit findings, working assigned tasks, or managing project work. Optimized for agent workflows: structured issue filing, priority/labeling system, and task completion tracking.
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.