AlterLab Academic Skills

Orchestrates matplotlib, seaborn, and plotly with opinionated publication styles to produce journal-ready figures. Use when preparing journal-submission figures that need multi-panel layouts with bold panel labels, statistical significance annotations, error bars, colorblind-safe palettes (Okabe-Ito), or specific journal formatting (Nature, Science, Cell). Does NOT cover raw low-level plotting or fine-grained control of individual plot elements; for building custom plots from scratch or tuning every artist and rcParam prefer alterlab-matplotlib instead. 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; to evaluate an existing manuscript use alterlab-peer-review. 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.

Looks up a Turkish academic's official YOKSIS-backed profile, current affiliation, unvan (academic title), publications, research projects, and supervised theses on the YOK Akademik portal (akademik.yok.gov.tr/AkademikArama/), a server-rendered JSP app with no public JSON API, by scraping its verified endpoints (AkademisyenArama POST search, viewAuthor.jsp profile, AkademisyenProjeBilgileri, AkademisyenYonTezBilgileri) keyed by an opaque authorId, with Turkish-character (Iı Şş Ğğ Çç Öö Üü) normalization for name matching. Use when the request is to verify a Turkish academic's current institution, find a researcher on YOK Akademik, confirm affiliation for authorship or a recommendation letter, or list someone's supervised theses or projects. For admission statistics (kontenjan, taban puan) use alterlab-yokatlas; for the national thesis full-text archive use alterlab-yok-tez; for publication metadata enrichment use alterlab-openalex. Part of the AlterLab Academic Skills suite.

Prepares academic career documents and professional-development materials for faculty and researchers. Use when drafting academic CVs, research statements, teaching philosophies, diversity statements, faculty-position cover letters, tenure dossiers, promotion narratives, academic portfolios, or mentorship statements, and when planning conference-networking strategy, building academic web presence, setting up ORCID profiles, or understanding impact metrics. Part of the AlterLab Academic Skills suite.

Writes competitive research grant proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC — applies agency-specific formatting and review criteria, prepares budgets, and drafts broader-impacts, significance statements, and innovation narratives that comply with submission requirements. Use when drafting or revising a grant proposal, aligning a proposal to a funding agency's review criteria, or preparing grant budgets and compliance sections. For Turkey's TÜBİTAK 1001/1002-A national proposals use alterlab-tubitak-proposal; to write a reviewer's critique of someone else's proposal use alterlab-peer-review; for a journal manuscript use alterlab-scientific-writing. Part of the AlterLab Academic Skills suite.

Provides LaTeX templates, formatting requirements, and submission guidelines for major scientific venues (Nature, Science, PLOS, IEEE, ACM, Cell Press), conferences (NeurIPS, ICML, ICLR, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). Also covers venue-specific writing style (tone, abstract format, what reviewers prioritize). Use when preparing a manuscript, conference paper, poster, or grant proposal and you need the right template, page/word limits, citation style, figure specs, or venue conventions. For substantive prose/content development defer to alterlab-scientific-writing; for grant argumentation defer to alterlab-research-grants. Part of the AlterLab Academic Skills suite.

Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 amplicon distribution (2026.1; renamed to "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier classify-sklearn against a version-matched SILVA 138 or Greengenes2 classifier, and diversity core-metrics-phylogenetic — teaching the .qza/.qzv artifact-and-provenance model and the 2026.1 feature-table summarize change (the former summarize_plus). Use when the request mentions QIIME2, QIIME 2, qiime, 16S, 18S, ITS, amplicon, microbiome, ASV, DADA2 denoising, feature table, taxonomic classification, or core-metrics diversity. For downstream alpha/beta diversity, PCoA, and PERMANOVA on the exported feature table prefer alterlab-scikit-bio; this is conda-only (no pip install). Part of the AlterLab Academic Skills suite.

Analyzes spatial transcriptomics with squidpy (1.8.x) on AnnData and SpatialData objects, routing platforms correctly: Visium spots use spatial_neighbors(coord_type='grid') and pair with deconvolution, while Xenium/MERFISH single-cell data use coord_type='generic'/Delaunay neighbors and spatialdata-io readers (xenium, visium_hd, merscope). Runs sq.gr.spatial_neighbors, nhood_enrichment, co_occurrence, spatial_autocorr (Moran's I for spatially variable genes), ripley, and ligrec. Use when the user wants spatial transcriptomics, squidpy, Visium/Xenium/MERFISH analysis, neighborhood enrichment, co-occurrence, or spatially variable genes; QC/clustering uses alterlab-scanpy and spot deconvolution (destVI/Tangram) uses alterlab-scvi-tools. Part of the AlterLab Academic Skills suite.

Trains single-agent reinforcement learning agents with Stable-Baselines3 — PPO, SAC, DQN, TD3, DDPG, and A2C behind a scikit-learn-like API. Use for standard single-agent RL experiments, quick prototyping, well-documented algorithm implementations on Gymnasium environments, or adding callbacks and evaluation. For high-throughput parallel training, multi-agent systems, or custom vectorized environments prefer alterlab-pufferlib. Part of the AlterLab Academic Skills suite.

Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for AI/ML training or cancer imaging research. 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.

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.

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.

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.

Analyze and engineer protein glycosylation — scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and reach curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). Use when identifying or designing glycosylation sites, optimizing therapeutic-antibody or biologic glycoforms, or doing glycoprotein engineering and vaccine-design work. 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.

Conducts comprehensive, systematic literature reviews across multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar), with PRISMA flow tracking, study screening (title/abstract and full-text), evidence-table extraction, and risk-of-bias assessment, producing professionally formatted markdown documents and PDFs with verified citations in multiple styles (APA, Nature, Vancouver). Use when running a systematic literature review, meta-analysis, research synthesis, or broad literature search, building a PRISMA flow diagram, screening studies, extracting an evidence table, or assessing risk-of-bias across biomedical, scientific, and technical domains. Part of the AlterLab Academic Skills suite.

Builds slide decks for research talks in PowerPoint and LaTeX Beamer, providing slide structure, design templates, timing guidance, and visual validation. Use when making conference presentations, seminar talks, research presentations, thesis-defense slides, or any scientific talk deck. 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.

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.

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.

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.

Computes the annual Turkish academic-incentive (akademik teşvik) score for Devlet (state) university öğretim elemanı (academic staff) from a classified activity list, applying the verified Akademik Teşvik Ödeneği Yönetmeliği (2018/11834) Faaliyet Hesaplama Tablosu headline puanları (Proje 20, Araştırma 15, Yayın 30, Patent 30, Atıf 30, Tebliğ 20, Ödül 20) with the k (author-count), p (Q1 1/Q2 0.8/Q3 0.5/Q4 0.25), and r (project-role) coefficients via türü puanı = Σ(faaliyet oranları) × headline (MADDE 8/2), then enforcing the per-type headline ceilings, the 100 cap (MADDE 8/3), and the net-30 payment gate (MADDE 10/3) in a deterministic scripts/tesvik_score.py. Use when the user wants to calculate akademik teşvik puanı, check the en az 30 (net-30) threshold, apply TABLO 4 ceilings, or estimate teşvik eligibility; keeps its coefficients separate from doçentlik scoring. For doçentlik point eligibility prefer alterlab-docentlik-eligibility. Part of the AlterLab Academic Skills suite.

Writes scientific manuscripts in full flowing paragraphs (never bullet points) via a two-stage process — section outlines with key points using research-lookup, then conversion to prose — applying IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, and reporting guidelines (CONSORT/STROBE/PRISMA). Use when drafting or revising research papers, journal submissions, or any manuscript section (abstract, introduction, methods, results, discussion), or professional/technical reports. For a specific venue's LaTeX template and house style use alterlab-venue-templates; to build a BibTeX bibliography or verify reference metadata use alterlab-citation-mgmt. Part of the AlterLab Academic Skills suite.

Generates concise (3-4 page), focused medical treatment plans in LaTeX/PDF format across all clinical specialties — general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management — using SMART goal frameworks, evidence-based interventions with minimal citations, HIPAA compliance, and professional formatting. Use when drafting a brief, actionable patient treatment or care plan with measurable SMART goals and structured follow-up for any specialty. Part of the AlterLab Academic Skills suite.

Guided statistical analysis with hypothesis-test selection, assumption checking, power analysis, and APA-formatted reporting. Use when choosing the appropriate statistical test for data, verifying test assumptions, computing power/sample size, or producing APA-style results for academic research. For implementing specific models programmatically prefer statsmodels. 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.

Scaffolds TÜBİTAK ARDEB national research proposals (1001 Bilimsel ve Teknolojik Araştırma Projeleri and 1002-A Hızlı Destek Modülü) against the official .doc form trees: 1. ÖZGÜN DEĞER (konunun önemi/özgün değer, araştırma sorusu/hipotezi, amaç ve hedefler), 2. YÖNTEM, 3. PROJE YÖNETİMİ (iş-zaman çizelgesi/iş paketleri + B-Planı, araştırma olanakları), 4. YAYGIN ETKİ, EK-1 Kaynaklar, EK-2 Bütçe ve Gerekçesi. Enforces the 600-word TR/EN özet (abstract) caps, program caps (1001 <=36 months/3,000,000 TRY for 2026-1; 1002-A <=12 months/150,000 TRY, rolling), and the four panel review dimensions; submission via PBS (ardeb-pbs.tubitak.gov.tr) with ARBİS prerequisite. Delegates generic grant craft to alterlab-research-grants. Use when the user wants to write a TÜBİTAK 1001 or 1002-A proposal, draft özgün değer or yaygın etki sections, scaffold a Turkish national grant, or map broader-impacts framing to TÜBİTAK terms. Part of the AlterLab Academic Skills suite.

Runs and analyzes molecular dynamics simulations with OpenMM and MDAnalysis — setting up protein and small-molecule systems, assigning force fields, running energy minimization and production MD, and analyzing trajectories (RMSD, RMSF, contact maps, free energy surfaces). Use when simulating protein or ligand dynamics, equilibrating a system, or computing trajectory metrics for structural biology, drug binding, or biophysics. Part of the AlterLab Academic Skills suite.

Writes comprehensive clinical reports — case reports (CARE guidelines), diagnostic reports (radiology, pathology, lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP notes, H&P, discharge summaries) — with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools. Use when drafting a case report for journal publication, a radiology/pathology/lab diagnostic report, an ICH-E3 clinical study report (CSR) or SAE narrative, or SOAP/H&P/discharge patient records needing regulatory-compliant formatting. Part of the AlterLab Academic Skills suite.

Applies computational methods to humanities research — text mining and NLP (LDA/BERTopic topic modeling, sentiment, named entity recognition with spaCy/NLTK), corpus linguistics (concordance, collocation, keyness), digital archives (Dublin Core, TEI XML), GIS for history, network analysis, stylometry and authorship attribution, OCR (Tesseract, Kraken, Transkribus), and data visualization (Gephi, Palladio). Use when distant-reading a literary corpus, mapping historical events or trade networks, attributing disputed authorship, digitizing historical documents, or building digital scholarly editions. 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.

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.

Formats manuscripts for Turkish journals: TR Dizin submission rules (TR Dizin mandates a set abstract word limit, two referees from different institutions, Latin script for non-Latin-alphabet articles, and only suggests a second-language abstract; bilingual oz/abstract ~200-250 words and 3-5 keywords are common journal house conventions to confirm against the journal's yazim kurallari) and the Turkish adaptation of APA-7 (ve ark./vd. for multi-author cites, ampersand only in the reference list, s. for page, Cev. for translator, t.y. for no date, aktaran for as-cited-in, Turkish capitalization). Use when writing for a Turkish/Turkce journal, preparing a TR Dizin submission, needing Turkce APA 7 in-text or reference formatting, or formatting a bilingual oz. For English-language citation styles prefer alterlab-citation-mgmt or alterlab-venue-templates; for live TR Dizin index status use alterlab-trdizin. Part of the AlterLab Academic Skills suite.

Generates professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings — biomarker-stratified patient cohort analyses with outcomes and evidence-based treatment recommendation reports with decision algorithms, supporting GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance, output as publication-ready LaTeX/PDF. Use when building a CDS document, cohort analysis, or treatment recommendation report for drug development, clinical research, or evidence synthesis, or when GRADE grading, hazard ratios, survival/waterfall plots, or biomarker stratification are requested. 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.

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.

Scaffolds Turkish human-subjects etik kurul (ethics committee) applications and routes a study to the correct committee using the TR Dizin/ULAKBIM 2020 trigger rule: survey, interview, focus-group, observation, or experiment data collection requires a university Girisimsel Olmayan Etik Kurulu (non-interventional committee); drug, device, cosmetic, stem-cell, or BA-BE clinical studies also require a TITCK-approved Klinik Arastirmalar Etik Kurulu plus a separate TITCK permit. Generates bilingual (TR/EN) Etik Kurul Basvuru Formu, basvuru dilekcesi (cover petition), and Bilgilendirilmis Gonullu Olur Formu (informed consent), and lints a consent draft against the TITCK 2023 minimum-content checklist. Use when the user needs a Turkish etik kurul basvurusu, an onam/olur (consent) formu, asks which ethics committee a study requires, or needs TITCK approval guidance. For non-Turkey IRB/Belmont/GDPR use alterlab-research-ethics; for KVKK data plans use alterlab-kvkk-dmp. Part of the AlterLab Academic Skills suite.

Creates publication-quality scientific diagrams with Nano Banana 2 AI and smart iterative refinement, using Gemini 3.1 Pro Preview for quality review and regenerating only when quality falls below the document-type threshold. Use when the request is for a technical or scientific diagram — neural-network architectures, system/block diagrams, flowcharts, biological pathways, circuits, or other complex scientific visuals. For general photos, illustrations, or artwork use generate-image, for text-based Mermaid diagrams use mermaid. 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.

Analyzes text as social-science data — topic modeling (BERTopic with embeddings + class-based TF-IDF, LDA/NMF via scikit-learn or gensim), document embeddings (sentence-transformers), dictionary/lexicon methods, and supervised text classification — choosing the method that matches the inferential goal (discovery vs measurement vs prediction) and validating topic reliability rather than trusting one stochastic run. It uses the verified stack (BERTopic, scikit-learn, gensim CoherenceModel, spaCy, sentence-transformers) with pinned patterns. Use when the request mentions topic modeling, text as data, computational text analysis, document embeddings, dictionary/sentiment lexicons, or classifying a corpus. For training or fine-tuning transformer models prefer alterlab-transformers; for humanities close-reading corpora prefer alterlab-digital-humanities. 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.

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.

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.

Runs a PARTIAL pre-screen of a Turkish associate-professorship (doçentlik) publication list against the ÜAK (Üniversitelerarası Kurul) Sağlık Bilimleri TABLO 10 criteria, applying the author-share rule (full; 0.8 + 0.5; lead-author half-the-rest split) and pass-fail checking the four computable minimums (≥100 total, ≥90 post-doctorate, ≥40 SCIE/SSCI article points, ≥3 lead-author Q1–Q4 articles). It deliberately does NOT emit an ELIGIBLE verdict — it lists the unmodelled mandatory minimums (TR Dizin/national article, citation, congress, teaching, per-category caps) for the user to verify. Use when the user wants to estimate doçentlik eligibility, calculate ÜAK points, or audit lead-author (başlıca yazar) requirements; resolve a journal's live TR Dizin status with alterlab-trdizin first, and compute the akademik teşvik (academic-incentive) score with alterlab-akademik-tesvik. Always verify the full live TABLO 10 — criteria change each term and differ per field. Part of the AlterLab Academic Skills suite.

Analyzes complex-sample survey data with design-based inference — declares a survey design (weights, strata, PSUs/clusters, FPC) before estimating means, totals, proportions, ratios, and quantiles, computes design-adjusted standard errors via Taylor linearization or replicate weights (BRR, Jackknife, Bootstrap), calibrates with post-stratification / raking / GREG, and fits design-adjusted GLMs (linear, logistic, Poisson). Uses samplics (stable Python), the emerging svy successor, or the field-standard R survey + srvyr via Rscript. Use when analyzing GSS/ANES/ESS/DHS/Eurobarometer or any weighted/stratified/clustered survey, when a dataset ships survey weights, or when someone quotes unweighted percentages from a complex survey. For questionnaire and sampling-plan DESIGN prefer alterlab-survey-design; for the sampling-adequacy gate prefer alterlab-ssci-sampling-gate; for causal identification prefer alterlab-causal-inference. 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.

Routes a social-science study to its research design — true experiment, quasi-experiment (difference-in-differences, instrumental variables, regression discontinuity, interrupted time series, fixed effects), observational/correlational, qualitative, or mixed — by walking the random-selection and random-assignment decisions, then PINS the identifying assumption the causal claim will rest on (parallel trends, exclusion restriction, continuity at the cutoff, selection-on-observables, or qualitative saturation logic) before any analysis begins. Use when choosing a study design, asking what design to use, framing a causal question from observational data, or deciding experiment vs quasi-experiment vs observational. For executing the analysis prefer alterlab-statistical-analysis; for qualitative design depth prefer alterlab-qualitative-methods; for choosing the statistical test downstream prefer alterlab-test-selection-guard. 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.

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.

Coordinates a social-science study through the stage-gated methods pipeline — question then design-gate then measurement-gate then sampling-gate then the right analysis module then inference-gate then reporting — holding a single YAML Design Passport that each gate reads and appends, and enforcing gate order with PASS / WARN / BLOCK semantics (design-gate and inference-gate are fail-closed). It is thin: it routes and holds the artifact, it does not run analysis itself. Use when a researcher describes a whole social-science study end to end, asks to run the methods pipeline or a full methodology review, or wants the design-to-inference workflow coordinated rather than a single step. For a single stage trigger that gate directly (alterlab-ssci-design-gate, -measurement-gate, -sampling-gate, -inference-gate); for multi-agent execution mechanics see alterlab-workflow-orchestration. Part of the AlterLab Academic Skills suite.

Gates who is sampled, how, and how many before data collection — checks that the sampling FRAME matches the target population (coverage error), that the METHOD is named (probability vs non-probability: simple random, stratified, cluster, systematic, quota, convenience, snowball), that sample SIZE follows the inference paradigm (an a-priori power analysis for hypothesis tests, a precision/margin-of-error target for estimation, or saturation/information power for qualitative studies — never a rule of thumb or collect-until-significant), and that the generalization claim matches the sample (statistical generalization only from probability samples). Use when asking how many participants are needed, planning recruitment, running or checking a power analysis, or judging whether a sample supports a population claim. For questionnaire items prefer alterlab-survey-design; for choosing the statistical test prefer alterlab-test-selection-guard. Part of the AlterLab Academic Skills suite.

Audits final inferential claims against the design, sample, and uncertainty before they are written or published — refuses causal language unless the design's identifying assumption is defended (else downgrades to associational), corrects p-value and confidence-interval misreadings (a p-value is not the probability the null is true, non-significance is not proof of no effect, a 95% CI is not a 95% probability the parameter is inside it), demands effect sizes with intervals rather than significance stars, flags uncorrected multiple comparisons and optional stopping / HARKing, and scopes generalization to the sampling frame. Use when writing or checking a results or discussion section, interpreting a p-value or confidence interval, or deciding whether a finding supports a causal or population claim. For choosing the statistical test prefer alterlab-test-selection-guard; to execute the analysis 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.

Gates measurement quality before a scale or instrument is trusted — checks that each construct is defined and operationalized, that reliability is evidenced with McDonald omega (not Cronbach alpha alone, which assumes tau-equivalence and is only a lower bound), that reliability is not confused with validity (content, criterion, convergent/discriminant construct validity), and that measurement invariance is tested before comparing groups. Use when asking whether a scale or survey instrument is valid, reporting a Cronbach alpha, building or adopting a multi-item measure, or comparing a latent construct across groups. For designing the questionnaire items prefer alterlab-survey-design; for running the confirmatory factor analysis prefer alterlab-sem-psychometrics; to execute basic statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

Gates trustworthiness before qualitative or interpretivist claims are written — the qualitative analog of the measurement gate. Checks that researcher positionality and role are stated, that the four Lincoln & Guba trustworthiness criteria are addressed (credibility via triangulation / member-checking / prolonged engagement, transferability via thick description, dependability via an audit trail, confirmability via reflexive bracketing), and that reflexivity is evidenced rather than asserted. Fail-closed: interpretivist generalizing or causal language is refused without the corresponding warrant. Use when a study is qualitative / ethnographic / interpretivist, when reviewing trustworthiness or reflexivity, or before writing up thematic or grounded-theory findings. For quantitative measurement validity prefer alterlab-ssci-measurement-gate; for running the coding prefer alterlab-qualitative-analysis; for the final claim audit prefer alterlab-ssci-inference-gate. Part of the AlterLab Academic Skills suite.