Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 354 files from 1 739 authors, of which 61 713 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Runs computational fluid dynamics simulations with the FluidSim Python framework using pseudospectral FFT methods, with HPC support and output analysis. Use when simulating Navier-Stokes equations (2D/3D), shallow water equations, or stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Part of the AlterLab Academic Skills suite.
Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.
Reads, writes, and analyzes geospatial vector data with the GeoPandas Python library (shapefiles, GeoJSON, GeoPackage), with PostGIS support and integration with matplotlib, folium, and cartopy. Use for spatial analysis and geometric operations — buffer analysis, spatial joins and overlays between datasets, dissolving boundaries, clipping, calculating areas and distances, reprojecting coordinate systems, choropleth mapping, or converting between vector file formats. This is for tabular vector data; for raster/satellite/DEM work, spectral indices (NDVI), or spatial ML on earth observation prefer the geomaster skill. Part of the AlterLab Academic Skills suite.
Runs high-performance genomic interval analysis with gtars (databio), a Rust toolkit with Python bindings — the performance-critical backend for the geniml ML library. Use when computing overlaps/jaccard/coverage between BED region sets, indexing intervals with IGD, generating uniwig accumulation/coverage tracks, tokenizing genomic regions for ML, splitting single-cell fragments into pseudobulks, or computing GA4GH refget sequence digests. NOT for training region embeddings (use alterlab-geniml) or non-genomic spatial joins (use alterlab-geopandas). Part of the AlterLab Academic Skills suite.
Covers geospatial science across remote sensing, GIS, spatial analysis, and machine learning for earth observation — satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), raster and DEM operations, spectral indices (NDVI/EVI/NDWI), spatial statistics, point cloud processing, network analysis, and cloud-native workflows (STAC, COG, Planetary Computer), with examples across Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use for remote sensing workflows, satellite/raster image classification, terrain/slope/hillshade analysis, spatial ML on earth-observation data, hydrological modeling, marine spatial analysis, or atmospheric science. For pure tabular vector work with no raster/EO aspect (plain GeoPandas sjoin, buffer, overlay, dissolve, choropleths) prefer the geopandas skill; for celestial-sphere astronomy coordinates (ICRS/galactic, FITS, WCS) prefer the astropy skill. 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.
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.
Trains and differentiates quantum circuits with PennyLane, a hardware-agnostic quantum machine-learning framework with automatic differentiation and PyTorch/JAX/TensorFlow integration. Use when training quantum circuits via gradients (parameter-shift, backprop, adjoint), building hybrid quantum-classical models or quantum neural networks, or running differentiable variational algorithms (VQE, QAOA). For IBM Quantum hardware and Qiskit Runtime prefer alterlab-qiskit; for Google Quantum AI or NISQ circuits prefer alterlab-cirq; for open-system Lindblad/master-equation dynamics prefer alterlab-qutip. 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.
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.
Guides advanced social science research methods — discourse analysis (Fairclough CDA, Gee), conversation analysis, quantitative content analysis, Qualitative Comparative Analysis (QCA), process tracing, archival research, participatory/community-based research (PAR, CBPR), Delphi and Q methodology, social network analysis (SNA), bibliometrics and scientometrics, systematic mapping reviews, and program/policy evaluation. Use when designing or conducting any of these studies — choosing a specialized method, building a coding scheme, establishing causal mechanisms in case studies, mapping relational or subjective data, or applying an evaluation framework. For interpretive/qualitative coding (thematic analysis, grounded theory, IPA, ethnography, qualitative content analysis) use alterlab-qualitative-methods; for combining qual+quant strands use alterlab-mixed-methods. Part of the AlterLab Academic Skills suite.
Simulates open quantum systems with QuTiP, the Quantum Toolbox in Python, solving Lindblad master equations (mesolve), Monte Carlo trajectories (mcsolve), and unitary dynamics (sesolve). Use when studying master-equation or Lindblad dynamics, decoherence, dissipation, quantum optics, cavity QED, or open-system time evolution. NOT for circuit-based quantum computing or hardware execution — for IBM Quantum circuits prefer alterlab-qiskit, for Google Quantum AI or NISQ circuits prefer alterlab-cirq, and for gradient-trained quantum ML prefer alterlab-pennylane. Part of the AlterLab Academic Skills suite.
Dispatch long-running GPU/CPU jobs to remote compute with a provider-agnostic submit → poll → harvest pattern across SLURM/HPC (sbatch, squeue, sacct) and managed APIs (Modal, RunPod, GCP Batch / Vertex AI). Use when submitting a batch job to a cluster, polling job status, retrieving result artifacts from a scheduler or cloud GPU provider, or writing a portable job-submission wrapper; the foundation-model skills (alterlab-alphafold, alterlab-boltz, alterlab-rfdiffusion, and siblings) dispatch their GPU work through this pattern. For Modal-specific serverless container deployment and autoscaling prefer alterlab-modal instead. Part of the AlterLab Academic Skills suite.
Scaffolds program-level Assurance-of-Learning (AoL) documentation for AACSB (2020 Standard 5) and ABET (Criterion 3 Student Outcomes, Criterion 4 Continuous Improvement) accreditation — program learning outcomes / competency goals, curriculum-to-outcome mapping matrices, direct- and indirect-assessment plans, rubric design, and closing-the-loop continuous-improvement narratives — and validates the structure of an outcome-mapping matrix with scripts/aol_matrix.py. Use when the user needs AACSB or ABET assurance-of-learning material, a program-learning-outcomes set, a curriculum/outcome map or coverage matrix, a direct/indirect assessment plan, a closing-the-loop report, or accreditation self-study text. For single-course design or course rubrics prefer alterlab-teaching-design; for post-award grant reports prefer alterlab-grant-reporting. Part of the AlterLab Academic Skills suite.
Drafts post-award grant deliverables across funder formats — NIH RPPR (Annual/Interim/Final via eRA Commons), NSF annual/final project reports and the public Project Outcomes Report (Research.gov), and Horizon Europe / ERC periodic and final reports (technical Part A/B + financial statements on the EU Funding & Tenders Portal) — plus milestone and deliverable tracking, budget-vs-actual variance narratives, no-cost-extension and rebudgeting justifications, and effort/closeout reporting. Computes report due dates from the award period with scripts/report_deadlines.py. Use when the user needs a grant progress or final report, post-award reporting, an RPPR, a periodic report, a no-cost-extension request, milestone tracking, or a budget-variance narrative. For writing new proposals prefer alterlab-research-grants; for TÜBİTAK proposals prefer alterlab-tubitak-proposal. Part of the AlterLab Academic Skills suite.
Drives preprint deposition across servers (arXiv, bioRxiv, medRxiv, SSRN, OSF Preprints): picks the right server by field, prepares submission metadata, sets the license (arXiv offers CC BY/BY-SA/BY-NC-SA/BY-NC-ND 4.0, the arXiv non-exclusive license, or CC0; bioRxiv/medRxiv offer CC BY/BY-NC/BY-ND/BY-NC-ND/CC0 or No-reuse), maps arXiv category taxonomy, handles immutable versioning and preprint DOIs, checks a journal's preprint/self-archiving policy via the Sherpa Romeo v2 API, and links the posted preprint to the published article. Reuses alterlab-arxiv and alterlab-biorxiv for metadata and alterlab-open-science for data-repository choice. Use when depositing a preprint, choosing a preprint server, preparing an arXiv or bioRxiv submission, setting a preprint license, or checking journal preprint policy; for Zenodo/Dryad/Figshare data deposition prefer alterlab-open-science, for TÜBİTAK Aperta prefer alterlab-aperta. Part of the AlterLab Academic Skills suite.
Drafts evidence-anchored academic reference and recommendation letters across types — graduate admission, faculty hiring, tenure/promotion external review, fellowship, and award nomination — from a structured prompt of candidate accomplishments, role context, evaluator relationship, and audience, calibrating specificity and register to the letter type and running a no-fabrication guard that flags unsupported superlatives, unanchored rankings, and claims with no evidence in the supplied dossier. Ships scripts/letter_scaffold.py to emit a typed section skeleton and scripts/claim_guard.py to lint a draft for evidence-free assertions. Use when the request is to write a recommendation or reference letter for a student or colleague, a tenure or promotion external-review letter, a fellowship or award nomination, or to check a letter draft for unsupported claims. For a candidate writing their own CV, research statement, or career narrative prefer alterlab-academic-career. Part of the AlterLab Academic Skills suite.
Designs validated research data-capture instruments and aligns them to CDISC submission standards. Builds REDCap projects from a requirements spec: instrument and field design, the 18-column data dictionary (Variable/Field Name, Form Name, Field Type, Choices, Branching Logic, Text Validation, Identifier?), field validation (date_ymd, integer, number, email, phone), branching/show-field logic, longitudinal events and survey settings, and lints a data dictionary for common errors. Maps a study to CDISC: CDASH collection fields, SDTM domain mapping (DM, AE, VS, LB, EX, CM, MH across Interventions/Events/Findings classes), and NCI-EVS controlled terminology. Use when the user wants to build a REDCap project, write or lint a data dictionary, set up branching logic or validation, or map a study to CDISC SDTM/CDASH/CDISC CT. For LabArchives ELN bridging use alterlab-labarchive; for Likert/sampling/reliability use alterlab-survey-design. Part of the AlterLab Academic Skills suite.
Drafts course-level generative-AI use policies and syllabus statements: assigns each graded task a permitted/restricted/prohibited tier (modeled on Cornell's prohibit/allow-with-attribution/encourage framework), writes the disclosure clause with a verbatim APA (OpenAI, 2023) or MLA Works Cited citation template for ChatGPT, and adds assessment-integrity, accessibility, and equity language bound to the institution's own academic-integrity code. Ships scripts/policy_builder.py to emit a paste-ready statement and scripts/policy_lint.py to flag a vague or self-contradicting draft. Use when the request mentions a syllabus AI policy, a course statement on ChatGPT or generative AI, an academic-integrity clause for AI tools, an AI-disclosure rule, or per-assignment permitted/prohibited AI tiers. For full course/backward design, syllabus, or rubrics use alterlab-teaching-design; for human-subjects AI-tool ethics use alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Accesses real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API (requires a free API key from alphavantage.co). Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Part of the AlterLab Academic Skills suite.
Runs Denario (AstroPilot-AI), a multiagent AI system for scientific research assistance that automates end-to-end research workflows from a described dataset through idea, methodology, computational results, and a publication-ready LaTeX paper. Built on AG2 + LangGraph with a cmbagent analysis backend. Use when driving the Denario pipeline (Denario.get_idea/get_method/get_results/get_paper), generating research ideas from a dataset description, auto-developing methodology, executing analysis agents, or emitting a journal-formatted (APS/AAS/JHEP/ICML/NeurIPS/PASJ) LaTeX manuscript. 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.
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.
Queries the OFR (Office of Financial Research) Hedge Fund Monitor API for time series on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management, including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms (no API key or registration required). Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data. 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.
Queries the U.S. Treasury Fiscal Data API across 54 datasets and 182 data tables (no API key required) for federal financial data on national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics. Part of the AlterLab Academic Skills suite.
Develops and runs genomics pipelines on the DNAnexus cloud platform using the dxpy Python SDK and dx CLI — build apps/applets, write dxapp.json, upload/download data, and execute jobs/workflows over FASTQ/BAM/VCF files. Use when building or running a DNAnexus app, applet, or workflow, writing dxapp.json, using dx-app-wizard or dx build, calling dxpy (find_data_objects, DXApplet.run, upload_local_file), or uploading/downloading sequencing data on DNAnexus. For LatchBio (Latch SDK @workflow/@task, LatchFile) use alterlab-latchbio instead; for Benchling LIMS use alterlab-benchling. Part of the AlterLab Academic Skills suite.
Integrates the Benchling R&D platform via its REST API and SDK — access the registry (DNA, proteins), inventory, ELN entries and workflows, build Benchling Apps, and query the Benchling Data Warehouse. Use when automating Benchling lab data management, syncing sample registry or inventory records, scripting ELN entries/workflows, or running SQL against the Benchling Data Warehouse. Part of the AlterLab Academic Skills suite.
Submits and manages protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs), covering protocol selection, input preparation, pricing, and ordering workflows. Use when running cell-free protein expression (validation or optimization), generating fluorescent pixel art, or interacting with Ginkgo Cloud Lab services. 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.
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.
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.
Manages scientific protocols through the protocols.io API v3 — search, create, update, and publish protocols (with DOI), manage steps and materials, handle protocol/step discussions and comments, organize team workspaces, and upload/manage workspace files. Use when discovering, developing, publishing, or citing protocols.io protocols, collaborating on protocol steps/materials, recording experiment runs, or integrating protocols.io into lab documentation. Not for general ELN entries/notebooks (use alterlab-benchling or alterlab-labarchive) or lab-instrument/liquid-handler control. Part of the AlterLab Academic Skills suite.
Enforces statistical-test selection as a discipline, holding the Iron Law NO TEST CHOSEN AFTER SEEING THE P-VALUE: routes the choice through a fixed decision tree (outcome type -> groups -> paired? -> normality) terminating in named tests (t-test, Mann-Whitney U, ANOVA, Kruskal-Wallis, Wilcoxon, Friedman, chi-square, Pearson/Spearman, regression), gates interpretation behind a mandatory Shapiro-Wilk/Levene/linearity assumption check, blocks test-shopping with an Excuse-vs-Reality table and Red-Flags-STOP list, and applies a 3+-tests escalation gate forcing Bonferroni/FDR correction or an exploratory label. Use when choosing or switching a statistical test, asking which test to run, dropping a test after a non-significant result, or running many tests hunting for significance. For executing the chosen test prefer alterlab-statistical-analysis or alterlab-statsmodels; for the broader frozen-plan discipline see alterlab-preregistration-discipline. Part of the AlterLab Academic Skills suite.
Programs lab automation with PyLabRobot, a vendor-agnostic Python framework that unifies control across Hamilton, Tecan, Opentrons, plate readers, and pumps, with simulation support. Use when controlling multiple equipment types or needing unified cross-vendor programming for complex, multi-vendor liquid-handling workflows. For Opentrons-only protocols with the official API, alterlab-opentrons may be simpler. Part of the AlterLab Academic Skills suite.
Enforces pre-registration discipline with the Iron Law NO DATA ANALYSIS WITHOUT A PRE-REGISTERED ANALYSIS PLAN FIRST, a spirit-vs-letter line, an Excuse-vs-Reality rationalization table, and a Red-Flags-STOP list (HARKing, optional stopping, post-hoc covariates, outlier-dropping, test-shopping). Runs a PLAN/COLLECT/CONFIRM/EXPLORE workflow that freezes hypotheses, tests, exclusions, and stopping rules before data, then forces unplanned findings to be labeled exploratory (their p-values lose confirmatory status, per COS confirmatory/exploratory model). Orchestrates, not replaces, alterlab-open-science (OSF/AsPredicted registration), alterlab-statistical-analysis (test selection, assumptions), and alterlab-scientific-thinking (bias grading). Use when analyzing data without a frozen plan, switching the primary outcome or adding covariates after seeing results, weighing early stopping, dropping outliers post-hoc, pre-registering a study, or rationalizing deviation. Part of the AlterLab Academic Skills suite.
Enforces results-reporting transparency as a discipline gate built on the Iron Law "NO RESULTS CLAIM WITHOUT REPORTING EVERY ANALYSIS RUN" — a numbered Gate Function (IDENTIFY the claim, LIST every test actually run including the ones that did not "work", CHECK assumptions were reported, CHECK effect size with 95% CI is present, CHECK pre-registration deviations are disclosed, ONLY THEN write the sentence), plus an Excuse-vs-Reality table and Red-Flags-STOP list for selective reporting, cherry-picking, and bare p-values. Use when writing up Results, claiming a finding from a subset of analyses, reporting a p-value without an effect size or confidence interval, dropping outliers post hoc, or omitting analyses that did not pan out. Orchestrates alterlab-statistical-analysis (tests, effect sizes), alterlab-preregistration-discipline (the frozen plan), and alterlab-open-science (TOP, disclosure); it does not run the tests itself. Part of the AlterLab Academic Skills suite.
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server, returning 25+ fields per paper (methods, results, sample sizes, quality scores, conclusions). Use when running a literature review or evidence synthesis, or when needing experimental details (sample sizes, effect sizes, methods, quality scores) that abstracts alone do not provide. Part of the AlterLab Academic Skills suite.
Free, key-less ResearchRabbit analog — builds a citation and co-citation graph around one or more seed papers using the OpenAlex API. Walks both directions of the citation network (works the seed cites and works that cite the seed), ranks the discovered neighbourhood by co-citation strength and bibliographic coupling to surface the papers most central to a topic's literature, and exports the network as GraphML (Gephi / Cytoscape / yEd) and JSON. Use when mapping a literature landscape, finding seminal or highly co-cited papers from a seed DOI, snowballing a reference network, building a citation map / co-citation analysis, or visualizing how a research area's papers connect — no API key required (polite mailto only). Part of the AlterLab Academic Skills suite.
Mixed methods research design and integration strategies for combining qualitative and quantitative approaches. Use when planning convergent, explanatory sequential, exploratory sequential, embedded, transformative, or multiphase designs; when integrating diverse data sources through merging, connecting, or embedding; when constructing joint displays or meta-inferences; or when evaluating quality criteria specific to mixed methods research. Covers Creswell & Plano Clark frameworks, notation systems, and software tools for integration. For single-strand qualitative coding (thematic analysis, grounded theory, saturation, inter-coder reliability) use alterlab-qualitative-methods; for questionnaire/Likert/instrument-validation mechanics use alterlab-survey-design. Part of the AlterLab Academic Skills suite.
Guidance for open science practices — preregistration (OSF Registries, AsPredicted, PROSPERO, ClinicalTrials.gov), open data and FAIR principles, repository choice (Zenodo, Dryad, Figshare), open access routes (Green/Gold/Diamond), Creative Commons licensing, reproducible computational workflows (Docker, Binder, Code Ocean), registered reports, open peer review, and TOP Guidelines. Use when preregistering a study, writing the FAIR data-sharing and repository section of a grant data management plan (NSF, NIH, ERC, UKRI), choosing a data repository, navigating open access, or building reproducible analyses. For human-subjects ethics, IRB applications, informed consent, or GDPR/HIPAA compliance, defer to alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Free Elicit-columns analog — ingest N PDFs (or any MarkItDown-supported document) and build a per-paper evidence table with user-defined columns, one row per paper and one column per attribute/question you want pulled from every source. Use when extracting structured data across many papers into a comparison table or data-extraction sheet (sample size, methods, main finding, effect, population/intervention/outcome, limitations), screening a corpus into a spreadsheet, or pulling the same fields from a stack of PDFs into CSV/Markdown. Routes conversion through MarkItDown; offline heuristic backend by default, optional LLM backend for precise answers. Part of the AlterLab Academic Skills suite.
Search the web, run deep research, and extract content from known URLs via the Parallel Web Systems Chat API (OpenAI-compatible) and Extract API, returning synthesized summaries with inline citations. 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.
Comprehensive qualitative research methods assistant supporting thematic analysis (Braun & Clarke), grounded theory (Strauss & Corbin; Charmaz), interpretative phenomenological analysis (IPA), content analysis, narrative inquiry, ethnography, case study methodology (Yin), coding techniques (open/axial/selective), NVivo-style workflows with Python alternatives, trustworthiness criteria (Lincoln & Guba), reflexivity, and member checking. Use when designing or analyzing qualitative research — thematic analysis, grounded theory, coding data, phenomenology, IPA, ethnography, case study, narrative inquiry, content analysis, qualitative coding, NVivo, trustworthiness, member checking, reflexivity, interview analysis, or focus group analysis. 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 hypothesis-gen; for grading evidence or spotting design flaws use scientific-thinking. 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.
International research ethics and compliance assistant supporting IRB/ethics board applications, informed consent drafting, data management plans, Belmont Report principles, Declaration of Helsinki (2024), GDPR compliance for research, HIPAA considerations, vulnerable populations protocols, deception research, confidentiality and anonymity, research integrity (fabrication/falsification/plagiarism), conflict of interest disclosure, and dual-use research oversight. Use when preparing an IRB or ethics board application, drafting informed consent, writing a data management plan, addressing GDPR/HIPAA in research, protecting human subjects or vulnerable populations, handling animal ethics, or disclosing conflicts of interest. For Turkey-specific etik kurul use alterlab-tr-research-ethics; for KVKK data plans alterlab-kvkk-dmp; for survey wording alterlab-survey-design; for qualitative methodology alterlab-qualitative-methods. Part of the AlterLab Academic Skills suite.
Evaluate scientific claims and evidence quality using evidence grading frameworks (GRADE, Cochrane Risk of Bias), assessing experimental design validity and identifying biases, confounders, statistical pitfalls, and logical fallacies. Use when judging evidence quality, grading certainty of evidence, spotting design or causal-inference flaws, identifying biases or confounders, naming statistical fallacies, or teaching critical analysis. For writing a formal submittable peer review use alterlab-peer-review; for a multi-reviewer mock panel verdict use alterlab-paper-reviewer; for IRB/consent/conflict-of-interest ethics use alterlab-research-ethics. 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, 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.
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 (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 instead (requires PARALLEL_API_KEY and OPENROUTER_API_KEY). Part of the AlterLab Academic Skills suite.
Estimates causal effects from observational and quasi-experimental data — difference-in-differences, instrumental variables, regression discontinuity, panel fixed effects, propensity-score / doubly-robust methods, and heterogeneous treatment effects (CATE) — using the verified Python stack: statsmodels and linearmodels (PanelOLS, IV2SLS), pyfixest (feols, event studies, Sun-Abraham, did2s), DoWhy (identify -> estimate -> refute), EconML (LinearDML, CausalForestDML, DRLearner), and rdrobust for RD. It names the identifying assumption before estimating and runs a refutation/robustness check after. Use when the request mentions difference-in-differences, instrumental variables, regression discontinuity, fixed effects / panel causal estimation, propensity scores, or treatment-effect estimation from non-randomized data. For choosing the design first prefer alterlab-ssci-design-gate; for plain regression or descriptive stats prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
Comprehensive survey and instrument design assistant supporting questionnaire construction, Likert scale design, question types (open/closed/matrix), response bias mitigation, sampling strategies (probability/non-probability), pilot testing, instrument validation (Cronbach's alpha, factor analysis), online survey tools (Qualtrics, REDCap, Google Forms), interview protocol development, focus group facilitation, mixed-mode surveys, and cultural adaptation of instruments. Use when designing a survey or questionnaire, building Likert scales, planning a sampling strategy, pilot testing, validating an instrument (Cronbach's alpha, factor analysis), developing an interview protocol, improving response rates, or working in Qualtrics or REDCap. For analyzing interview/focus-group data use alterlab-qualitative-methods; for qual+quant integration alterlab-mixed-methods; for test selection/power analysis alterlab-statistical-analysis; for IRB/consent alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Builds agent-based models of social systems with Mesa 3 — the current AgentSet API (model.agents.shuffle_do('step'), auto-assigned unique_id, mandatory super().__init__(seed=...)), cell spaces (mesa.discrete_space OrthogonalMooreGrid / classic mesa.space grids), the DataCollector, batch_run parameter sweeps, and SolaraViz — for emergence, segregation, diffusion, opinion dynamics, and cooperation models. It uses the Mesa 3.x API (the old mesa.time schedulers like RandomActivation are removed) and treats the model as a generative theory to be validated, not just run. Use when the request mentions an agent-based model, Mesa, simulating interacting agents, or emergent macro behavior from micro rules. For discrete-event (queueing/process) simulation prefer alterlab-simpy; for reinforcement learning prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.
Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using statsmodels MixedLM and bambi (Bayesian on PyMC) in Python, or the field-standard R lme4 / glmmTMB / brms via Rscript. It enforces the reporting items reviews find under-reported: full fixed + random specification, centering, variance components + ICC, estimation method, assumption checks, model comparisons, and effect sizes. Use when data are grouped/nested (students in schools, repeated measures, panel/longitudinal) and the question concerns within- vs between-cluster variation. For general single-level regression prefer alterlab-statsmodels; for panel fixed effects used for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite.
Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum likelihood (FIML) where a likelihood/SEM model applies. Uses statsmodels MICE / MICEData in Python or the field-standard R mice via Rscript, and warns that single (mean/regression) imputation and scikit-learn's IterativeImputer return one completed dataset without Rubin's-rules pooling, so they understate standard errors if used as multiple imputation. Use when a dataset has missing values, when choosing an imputation strategy, or when reporting how missingness was handled. For general modeling on complete data prefer alterlab-statistical-analysis; for latent-variable models with FIML prefer alterlab-sem-psychometrics. Part of the AlterLab Academic Skills suite.
Runs Qualitative Comparative Analysis — crisp-set (csQCA), multi-value (mvQCA), and fuzzy-set (fsQCA) — for small-to-medium-N configurational research: calibrating raw data into set membership, building and refining a truth table, and Boolean minimization into conservative / parsimonious / intermediate solutions with consistency and coverage. Because there is no maintained Python QCA library, it shells out to R's QCA package (calibrate, truthTable, minimize) via Rscript and documents that dependency honestly rather than faking a Python API. Use when the request mentions QCA, fsQCA, csQCA, configurational or set-theoretic analysis, necessary/sufficient conditions, truth tables, or calibration of conditions. For net-effect estimation of a single treatment prefer alterlab-causal-inference; for interpretive analysis prefer alterlab-qualitative-methods. Part of the AlterLab Academic Skills suite.
Fits and evaluates measurement models — confirmatory factor analysis, full structural equation models, exploratory factor analysis, item response theory, and multi-group measurement invariance — using the verified Python stack: semopy (model syntax =~ / ~ / ~~, Model.fit, inspect(std_est=True), calc_stats for CFI/TLI/RMSEA), factor_analyzer (EFA, KMO, Bartlett, ConfirmatoryFactorAnalyzer), and pingouin/girth, computing McDonald's omega from standardized loadings and judging fit against Hu & Bentler cutoffs. Use when the request mentions confirmatory factor analysis, structural equation modeling, a latent variable or construct model, factor loadings, IRT, or measurement invariance across groups. For deciding whether a scale is trustworthy at all prefer alterlab-ssci-measurement-gate; for plain regression prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
Analyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal calculator with bootstrap CIs; Cohen's / Fleiss' kappa via statsmodels) with 95% CIs and thresholds (alpha >= .80 reliable, .667-.80 tentative). It BRANCHES by design: coefficient-based ICR for codebook / content-analytic coding, versus consensus-and-reflexivity for reflexive thematic analysis where a statistic is not the right criterion. Supports human-vs-LLM double-coding with an alpha check against a human gold standard. Use when coding interviews or open-ended text, building a codebook, or reporting intercoder reliability. For topic modeling / embeddings / supervised text classification prefer alterlab-text-as-data; for the reflexivity gate prefer alterlab-ssci-reflexivity-gate. Part of the AlterLab Academic Skills suite.
Claude Skills — questions
Answers built from the skills we actually parsed.