Database

Academic paper writing specialist that guides researchers through every stage of manuscript preparation, from structuring an IMRaD paper to responding to peer review, with attention to journal-specific conventions and common rejection pitfalls.

Scientific data visualization specialist that helps researchers create accurate, accessible, and publication-ready figures using best practices in chart selection, color theory, and tools like matplotlib, ggplot2, and D3.

Research grant writing specialist that helps researchers craft competitive proposals for NIH, NSF, EU, and other funding bodies, covering every section from specific aims to budget justification and resubmission strategy.

Research lab management specialist that helps PIs and lab managers run productive, safe, and well-organized research groups, covering mentoring, project management, safety protocols, data management, and building a positive lab culture.

Peer review specialist that helps researchers write constructive reviews, evaluate manuscripts critically yet fairly, and navigate the review process as both reviewer and author with professionalism and rigor.

Research methodology specialist that helps researchers design rigorous studies, select appropriate methods, and navigate ethical review, covering quantitative, qualitative, and mixed-methods approaches across disciplines.

Science outreach and public engagement specialist that helps researchers communicate their work to lay audiences, design outreach programs, handle media, leverage social media, and counter scientific misinformation effectively.

Thesis and dissertation writing specialist that guides graduate students through every stage from proposal to defense, covering committee management, chapter structure, literature review strategies, time management, and publishing from the dissertation.

Triggers when users need help with AI ethics, fairness, or responsible AI development. Activate for questions about fairness metrics (demographic parity, equalized odds, calibration), bias detection and mitigation (pre-processing, in-processing, post-processing), model cards, datasheets for datasets, AI impact assessments, transparency and explainability requirements, dual-use considerations, and environmental impact of training large models.

Triggers when users need help writing AI/ML research grant proposals or planning funded research. Activate for questions about NSF, DARPA, NIH, or industry research grants, positioning AI research for funding, budget planning for compute-heavy research, collaboration letters, broader impacts statements, research timeline planning for ML projects, and industry research partnerships.

Triggers when users need help reviewing ML papers or understanding the peer review process. Activate for questions about NeurIPS, ICML, or ICLR review guidelines, writing constructive criticism, evaluating experimental rigor, spotting common statistical errors in ML papers, assessing novelty versus incrementalism, reviewer calibration, area chair responsibilities, ethical review considerations, and meta-review writing.

Triggers when users need help designing ML experiments, formulating research hypotheses, or planning ablation studies. Activate for questions about baseline selection, controlled experiments in machine learning, statistical significance testing for model comparisons, reproducibility practices (random seeds, environment pinning), compute budgeting for research, and refining research questions in AI/ML.

Triggers when users need help with AI safety, alignment research, or responsible AI development. Activate for questions about RLHF (reward modeling, PPO, DPO, KTO), constitutional AI, scalable oversight, interpretability methods (mechanistic interpretability, probing, attention analysis), red teaming, jailbreak robustness, alignment tax, corrigibility, the value alignment problem, and AI governance frameworks.

Triggers when users need help with experiment management and tracking for ML research. Activate for questions about experiment management systems (Weights & Biases, MLflow, Neptune, Aim), hyperparameter logging, artifact versioning, experiment comparison and visualization, collaborative research workflows, compute cost tracking, experiment metadata standards, and reproducing experiments from logs.

Triggers when users need help conducting systematic literature reviews in AI/ML, constructing taxonomies of research areas, or writing survey papers. Activate for questions about identifying research trends and gaps, keeping up with the arXiv flood, daily paper filtering, conference proceeding analysis, citation network analysis, and research community mapping.

Triggers when users need help with ML benchmark design, dataset curation for evaluation, or evaluation protocol construction. Activate for questions about leaderboard management, benchmark saturation detection, cross-dataset generalization, statistical testing for model comparison (McNemar, paired bootstrap), avoiding benchmark gaming, and standard benchmark suites like GLUE, SuperGLUE, ImageNet, MMLU, HELM, and BIG-Bench.

Triggers when users need help writing ML research papers for top venues. Activate for questions about writing for NeurIPS, ICML, ICLR, CVPR, paper structure conventions, abstract writing, related work positioning, experiment section best practices, figure and table design, rebuttal writing, camera-ready preparation, supplementary material organization, and arXiv preprint strategy.

Triggers when users need help with open-source ML practices, model release, or community engagement. Activate for questions about Hugging Face Hub (model cards, datasets, spaces), open-weight vs open-source licensing (Apache 2.0, MIT, Llama license), documentation for ML projects, community building around ML tools, contributing to open-source ML frameworks like PyTorch or JAX, and responsible model release practices.

Triggers when users need help reading ML papers efficiently, critically evaluating research claims, or reproducing results from published work. Activate for questions about the three-pass paper reading method, understanding mathematical notation in ML papers, identifying key contributions versus incremental work, building literature maps, and troubleshooting common reproducibility pitfalls.

Triggers when users need help with ML infrastructure, GPU cluster management, or distributed training. Activate for questions about SLURM, Kubernetes for ML workloads, distributed training setup (data parallel, model parallel, pipeline parallel, FSDP, DeepSpeed ZeRO), experiment management at scale, Weights & Biases or MLflow for research, debugging distributed training, profiling GPU utilization, and memory optimization.

Comprehensive knowledge of small solar system bodies including asteroids, comets, Kuiper Belt objects, impact science, planetary defense strategies, asteroid missions, and resource utilization for authoritative scientific discussion.

Deep scientific knowledge of astrobiology: the origin, evolution, distribution, and future of life in the universe. Use when the user asks about the search for extraterrestrial life, biosignatures, habitable zones, extremophiles, the Drake equation, the Fermi paradox, Mars habitability, ocean worlds, exoplanet atmospheres, SETI, or the origin of life. Triggers: "astrobiology", "extraterrestrial life", "biosignature", "habitable zone", "extremophile", "Drake equation", "Fermi paradox", "SETI", "origin of life", "abiogenesis", "panspermia", "ocean world", "Europa", "Enceladus", "Mars life", "exoplanet atmosphere", "technosignature".

Expert knowledge of the intersection of particle physics and astrophysics, covering cosmic rays, neutrino astronomy, gamma-ray observations, dark matter detection, antimatter, and multi-messenger astrophysics for authoritative scientific discussion.

Comprehensive knowledge of black hole physics — formation, types, accretion, observations, and theoretical aspects. Use when the user asks about black holes, event horizons, singularities, Hawking radiation, the Event Horizon Telescope, gravitational waves from mergers, or supermassive black holes. Triggers: "explain black holes", "what is an event horizon", "Hawking radiation", "how do black holes form", "Sagittarius A*", "M87 black hole", "black hole information paradox", "explain accretion disks", "gravitational waves from black holes", "Kerr black holes", "Schwarzschild radius", "supermassive black holes", "Eddington luminosity".

Expert knowledge of numerical methods, simulation codes, data analysis techniques, and software tools used in modern astrophysics, from N-body dynamics to cosmological simulations and machine learning applications.

Deep scientific knowledge of physical cosmology: the origin, structure, evolution, and ultimate fate of the universe. Use when the user asks about the Big Bang, cosmic microwave background, dark matter, dark energy, the expansion of the universe, cosmological parameters, inflation, the LCDM model, or the fate of the cosmos. Triggers: "Big Bang", "CMB", "dark matter", "dark energy", "Hubble constant", "cosmological constant", "inflation theory", "LCDM", "expansion of the universe", "cosmic microwave background", "cosmological parameters", "fate of the universe".

Comprehensive knowledge of exoplanet detection, characterization, and habitability science. Use when the user asks about finding planets around other stars, habitable zones, biosignatures, transit methods, radial velocity, or specific exoplanet systems. Triggers: "how do we find exoplanets", "what is the transit method", "explain radial velocity", "what is the habitable zone", "tell me about TRAPPIST-1", "what is a hot Jupiter", "how does JWST study exoplanet atmospheres", "what is eta-Earth", "explain biosignatures", "Kepler mission results", "what is a super-Earth", "exoplanet atmospheric characterization".

Deep scientific knowledge of galaxies: classification, structure, formation, evolution, dynamics, and active nuclei. Use when the user asks about the Milky Way, galaxy types, galaxy formation and evolution, active galactic nuclei, quasars, supermassive black holes, galaxy mergers, spiral structure, or dwarf galaxies. Triggers: "Milky Way", "galaxy classification", "Hubble sequence", "elliptical galaxy", "spiral galaxy", "galaxy formation", "galaxy evolution", "AGN", "quasar", "supermassive black hole", "galaxy merger", "Sgr A*", "galactic dynamics".

Deep knowledge of gravitational wave theory, detection methods, landmark observations, waveform physics, source types, pulsar timing arrays, future detectors, and multi-messenger astronomy for authoritative scientific discussion.

Deep scientific knowledge of the large-scale structure of the universe: the cosmic web, galaxy clusters and groups, superclusters, filaments, voids, baryon acoustic oscillations, structure formation theory, and observational surveys. Use when the user asks about the cosmic web, galaxy clusters, superclusters, voids, BAO, large-scale structure surveys, N-body simulations, or the distribution of matter in the universe. Triggers: "cosmic web", "galaxy cluster", "supercluster", "Laniakea", "Great Attractor", "baryon acoustic oscillations", "BAO", "galaxy survey", "SDSS", "DESI", "Euclid", "large-scale structure", "cosmic void", "filament", "N-body simulation".

Comprehensive knowledge of observational astronomy techniques, instruments, and methods. Use when the user asks about telescopes, spectroscopy, photometry, astrometry, imaging techniques, astronomical surveys, coordinate systems, or atmospheric effects on observations. Triggers: "how do telescopes work", "explain spectroscopy", "what is adaptive optics", "how do astronomers measure distance", "explain redshift", "what is the magnitude system", "how does Gaia work", "explain CCD imaging", "what is seeing in astronomy", "compare Hubble and JWST", "explain the electromagnetic spectrum in astronomy".

Comprehensive knowledge of solar system science including planets, moons, dwarf planets, ring systems, planetary formation, geological processes, and space missions. Use when the user asks about any solar system body, planetary atmospheres, missions to planets or moons, or how the solar system formed. Triggers: "tell me about Mars", "what is Europa's ocean", "how did the solar system form", "explain Titan's atmosphere", "what did Cassini discover", "compare gas giants and ice giants", "what are dwarf planets", "explain planetary differentiation", "Mars rover missions", "what is the Great Red Spot", "explain tidal heating", "ring systems of Saturn".

Comprehensive knowledge of radio astronomy including the radio window, telescope designs, interferometry principles, radio sources, key science areas, and current and future facilities for expert-level scientific discussion.

Deep scientific knowledge of solar physics: the Sun's internal structure, energy generation, magnetic activity, solar cycle, space weather, heliosphere, and observational missions. Use when the user asks about the Sun, solar structure, solar flares, coronal mass ejections, sunspots, the solar cycle, space weather, the heliosphere, solar wind, solar neutrinos, or solar observatories. Triggers: "Sun", "solar physics", "solar flare", "CME", "coronal mass ejection", "sunspot", "solar cycle", "space weather", "heliosphere", "solar wind", "Parker Solar Probe", "coronal heating", "solar neutrino", "Carrington event", "solar activity".

Comprehensive knowledge of stellar physics — formation, structure, evolution, and death. Use when the user asks about how stars form, stellar classification, the HR diagram, nuclear fusion in stars, supernovae, neutron stars, white dwarfs, or variable stars. Triggers: "how do stars form", "explain the HR diagram", "what is a red giant", "how does nuclear fusion work in stars", "what is a supernova", "explain neutron stars", "what are Cepheid variables", "stellar spectral types", "white dwarf physics", "mass-luminosity relation", "explain binary star evolution", "what is the Chandrasekhar limit".

Activate this skill when the user is comparing measured performance results (latency, throughput, accuracy, cost per unit, energy) across systems, versions, models or configurations and needs to report the comparison without misleading anyone. Triggers on "benchmark comparison," "performance comparison," "A vs B benchmark," "is the speedup real," "effect size," "statistical significance," "practical significance," "benchmark report," "variance and repeats," or "comparative analysis of benchmark results." Covers equalizing conditions, handling run-to-run variance with repeats and confidence intervals, effect sizes, the difference between statistical and practical significance, aggregating across benchmarks, and tables and charts that do not distort.

Activate this skill when the user wants to audit a comparison for bias, is worried that their own comparison is slanted, or must produce a comparison that a sceptical or adversarial reader will accept. Triggers on "biased comparison," "cherry-picked criteria," "apples to oranges," "survivorship bias," "anchoring," "fair comparison," "conflict of interest," "pre-register criteria," "Simpson's paradox," or "is this comparative analysis fair." Covers the common distortions in comparative work, incommensurable units, disclosure of conflicts, pre-registration of criteria and weights, and a review protocol for catching bias before publication.

Activate this skill when the user needs to compare two or more options, cases, vendors, policies, designs or datasets in a structured way and reach a conclusion that survives scrutiny. Triggers on "comparative analysis," "compare options," "evaluation framework," "decision criteria," "side-by-side comparison," "which is better," "trade-off analysis," or "comparison template." Covers defining the comparison question, choosing units and dimensions, normalizing measures, weighing criteria, drawing conclusions, and keeping the comparison honest when stakeholders already have a favourite.

Activate this skill when the user is designing, conducting or writing up a study that compares several in-depth cases (organizations, programmes, projects, regions, incidents) to explain outcomes or build theory. Triggers on "comparative case study," "multiple case study," "cross-case analysis," "within-case analysis," "process tracing," "structured focused comparison," "case matrix," "case study protocol," or "comparative analysis of cases." Covers the structured focused comparison method, within-case and cross-case analysis, process-tracing tests, building and using the case matrix, and writing findings that separate what the cases show from what the analyst infers.

Activate this skill when the user is designing or critiquing a comparison of a small number of cases (countries, regions, organizations, programmes, historical episodes) to explain an outcome rather than merely rank options. Triggers on "comparative method," "Mill's methods," "most similar systems," "most different systems," "small-N," "case selection," "QCA," "qualitative comparative analysis," "truth table," "comparative politics," or "comparative analysis in social science." Covers Mill's canons of induction, most-similar and most-different systems designs, the small-N versus large-N trade-off, case selection strategies, controlling for confounders without statistics, and the basics of crisp-set and fuzzy-set QCA.

Activate this skill when the user is comparing products, services or competitors for a buying decision, a market analysis, a positioning exercise or a public comparison page. Triggers on "competitor comparison," "product comparison," "feature matrix," "feature comparison table," "pricing comparison," "positioning map," "competitive analysis," "versus page," "battlecard," or "comparative analysis of vendors." Covers building feature matrices that record depth rather than checkmarks, normalizing pricing across packaging models, drawing positioning maps on buyer-relevant axes, gathering evidence fairly, avoiding straw-man comparisons, and writing a comparison the rival's own team would accept as accurate.

Activate this skill when the user is comparing options on money over time: build versus buy, on-premises versus subscription, two capital projects, or a policy against its alternatives. Triggers on "total cost of ownership," "TCO comparison," "cost-benefit analysis," "net present value," "discount rate," "hidden costs," "break-even analysis," "payback period," "scenario analysis," or "comparative analysis of costs." Covers building a TCO model that captures lifecycle and exit costs, discounting and the choice of rate, scenario ranges instead of point estimates, break-even and crossover analysis, and presenting uncertainty so decision-makers see the range and not just the base case.

Activate this skill when the user needs to present a comparison of options, groups, periods or conditions in a table or chart and wants the layout to reveal the differences rather than bury them. Triggers on "comparison table," "side-by-side table," "small multiples," "slope chart," "dumbbell plot," "before and after chart," "normalize scales," "how to order rows," "accessible chart," or "visualizing a comparative analysis." Covers table design for comparison, small multiples with shared axes, slope charts and dumbbell plots for paired values, normalizing scales so unlike measures can share a view, ordering rows and columns for insight, and accessibility requirements that do not degrade the design.

Activate this skill when the user is building or reviewing a scoring model that ranks options against weighted criteria, such as a vendor selection matrix, a prioritization scorecard or an evaluation rubric. Triggers on "weighted scoring," "scoring matrix," "decision matrix," "criteria weights," "vendor scorecard," "multi-criteria decision," "Pugh matrix," "sensitivity analysis," or "comparative analysis scoring." Covers criteria selection, deriving and justifying weights, anchored scoring scales, sensitivity analysis on weights, avoiding false precision, and presenting the matrix so the ranking and its fragility are both visible.

Activate this skill when the user wants to see the shape of a field rather than pool its results: who cites whom, where the evidence clusters, and where it is absent. Triggers on "citation network," "co-citation analysis," "bibliographic coupling," "evidence gap map," "evidence map," "seminal papers," "bibliometric analysis," "VOSviewer," "citation chasing," "mapping the literature," or "literature review visualization." Covers building citation graphs from reference lists (including offline from PDFs), computing centrality and clusters, constructing intervention-by-outcome evidence gap maps, identifying landmark and outlier works, and presenting the map without overclaiming.

Activate this skill when the user needs to judge the trustworthiness of included studies and rate certainty in a body of evidence. Triggers on "risk of bias," "RoB 2," "ROBINS-I," "Newcastle-Ottawa," "critical appraisal," "CASP checklist," "GRADE," "certainty of evidence," "summary of findings table," "CERQual," "quality assessment," or "literature review appraisal." Covers matching the appraisal tool to the study design, applying signalling questions consistently, rating GRADE certainty across the downgrading and upgrading domains, appraising qualitative studies, assessing confidence with GRADE-CERQual, and recording judgments so a reader can retrace them.

Activate this skill when the user wants to pool quantitative results across studies or needs to judge whether pooling is defensible. Triggers on "meta-analysis," "effect size," "standardized mean difference," "pooled odds ratio," "fixed effect vs random effects," "heterogeneity," "I squared," "forest plot," "funnel plot," "publication bias," "Egger's test," "metafor," or "literature review statistics." Covers choosing and computing effect sizes, inverse-variance pooling under fixed-effect and random-effects models, quantifying heterogeneity, reading forest and funnel plots, small-study effects, and the conditions under which a meta-analysis should not be done.

Activate this skill when the user hands you a corpus they already have (PDFs, extracted text, reading notes, reference-manager exports) and wants it synthesized with no web access and no database searching. Triggers on "offline literature synthesis," "literature synthesis," "literature review," "synthesize these papers," "what do these PDFs say," "cross-paper matrix," "evidence table from my folder," or "summarize my reading notes." Covers corpus inventory, reading order, structured extraction, cross-study matrices, and writing a synthesis in which every claim traces back to a document and page in the corpus.

Activate this skill when the user wants to identify what a body of literature has not answered and turn that into fundable, answerable research questions. Triggers on "research gap," "gap analysis," "gaps in the literature," "future research," "research agenda," "research questions from a literature review," "proposal from a literature synthesis," "under-researched," or "what is missing in the literature." Covers a typology of gaps (population, method, theory, context, evidence, measurement), writing gap statements backed by counts and citations, distinguishing an absence of studies from an absence of good studies, and converting gaps into prioritized questions and proposal aims.

Activate this skill when the user is moving from a completed search to an included-study set and needs to screen records, resolve disagreements, and extract data into evidence tables. Triggers on "title and abstract screening," "full-text screening," "dual screening," "inter-rater agreement," "Cohen's kappa," "data extraction form," "evidence table," "reference management," "deduplication," "Rayyan," "Covidence," or "literature review screening." Covers two-stage screening, calibration and conflict resolution, deduplication and reference tracking, piloted extraction forms, and building characteristics-of-included-studies and results tables.

Activate this skill when the user is planning a systematic review, scoping review, or other structured literature review and needs a protocol before any searching begins. Triggers on "systematic review protocol," "PICO question," "PEO question," "inclusion and exclusion criteria," "search strategy," "PRISMA flow diagram," "PROSPERO registration," "literature review plan," "review team roles," or "literature synthesis protocol." Covers framing an answerable question, writing eligibility criteria screeners can apply consistently, documenting reproducible database searches, planning the PRISMA flow, registering the protocol, and assigning roles and timelines to a review team.

Activate this skill when the user has qualitative, mixed-methods, or heterogeneous quantitative studies that cannot be pooled statistically and needs a rigorous non-statistical synthesis. Triggers on "thematic synthesis," "narrative synthesis," "framework synthesis," "meta-ethnography," "qualitative evidence synthesis," "coding studies," "theme development," "synthesis without meta-analysis," "mixed-methods review," or "literature review themes." Covers line-by-line coding, descriptive and analytical theme development, best-fit framework synthesis, the structure of a narrative synthesis, and handling heterogeneity across qualitative and mixed evidence.

Activate this skill when the user has finished extraction and appraisal and needs to turn tables into a review manuscript, thesis chapter, or report. Triggers on "writing a literature review," "review structure," "results section of a systematic review," "hedged claims," "conflicting studies," "structured abstract," "plain language summary," "PRISMA checklist," "literature synthesis writing," or "how to write up findings." Covers the standard architecture of a review, moving from evidence tables to prose, calibrating claims to certainty, reporting disagreement among studies without averaging it away, and writing abstracts and plain-language summaries that survive being quoted.