「observational」の検索結果

Use this skill to reason like JoAnn E. Manson, epidemiologist at Harvard Medical School and women's health expert. Trigger this skill whenever you are evaluating hormone replacement therapy (HRT), menopause management, nutritional epidemiology, dietary supplements, or clinical trial methodology. Apply her frameworks when users ask about the risks and benefits of HRT, bioidentical hormones, or targeted supplementation. Reach for this skill to differentiate between symptom management and chronic disease prevention, or when assessing observational data versus randomized clinical trials (RCTs) in public health contexts.

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Foundational concepts in nutrition science — macronutrients, micronutrients, energy balance, Atwater factors, reference intakes (DRI/RDA/AI/UL), food composition tables, and the methods of human nutrition research. Grounds the rest of the department in a shared vocabulary and in the measurement limits of the field, including controlled-trial history, observational-study limits, and the biochemical basis for calorie accounting. Use when a question asks what something "is" nutritionally, how energy and nutrients are measured, or what the reference numbers mean.

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Observational cosmology from Hubble's law to the CMB. Covers redshift, Hubble expansion, the cosmological parameters, the cosmic microwave background, large-scale structure, galaxy rotation curves and dark matter, Type Ia SNe and dark energy, and the current state of Lambda-CDM. Use when reasoning about the large-scale universe, interpreting cosmological surveys, or teaching the Big Bang evidence chain.

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Bisimulation game for resilient skill dispersal across AI agents with GF(3) conservation and observational bridge types.

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Nature-style hydrology manuscript reviewer skill for testing streamflow, runoff, groundwater, water storage, drought, flood, hydroclimate and water-resource claims against water-balance closure, hydrologic signatures, model structure and observational constraints.

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Search the ClinicalTrials.gov registry through its version 2 REST API for interventional and observational studies, their phases, enrolment, endpoints, sponsors, and posted results. Use this skill to survey who is developing what against an indication, date a competitor's programme, read primary and secondary outcome measures, find eligibility criteria, and distinguish a study that completed from one that was terminated or withdrawn. Also trigger on ClinicalTrials.gov, NCT number, trial registry, study phase, enrolment, primary outcome measure, recruiting status, trial sponsor, or competitive landscape.

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Use when a task needs the judgment of an Economist — estimating a demand elasticity or causal treatment effect from observational or experimental data, building a cost-benefit or welfare analysis for a policy or pricing decision, forecasting a macro or market aggregate under stated assumptions, or stress-testing whether a "the data shows X" claim survives a confounding-variable check.

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Use when a task needs the judgment of an astronomer — calculating exposure time or signal-to-noise for a target, writing the feasibility math for a telescope-time proposal, reducing photometric or spectroscopic data, distinguishing a systematic error floor from statistical noise, or reviewing an observational result for calibration or upper-limit red flags.

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Expert guidance across solar physics, planetary science, stellar evolution, cosmology, and observational techniques with 2025 mission data

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Electronic health record analysis — phenotyping, missingness, bias, and observational study design with EHR data.

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Working with observational climate data — reanalyses, station records, anomalies, trends, and honest uncertainty.

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Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Expert-level astrophysics covering stellar physics, galaxy formation, high-energy astrophysics, observational techniques, and the electromagnetic spectrum across wavelengths.

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Expert-level black hole physics covering formation, spacetime structure, accretion disks, jets, Hawking radiation, gravitational waves from mergers, and observational evidence.

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Expert-level dark matter physics covering observational evidence, candidate particles, detection methods, structure formation, and alternative gravity theories.

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Expert-level cosmology covering the Big Bang, cosmic expansion, dark matter, dark energy, CMB, large-scale structure, and observational cosmology.

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当用户询问 ClinicalTrials.gov / 临床试验 / NCT / 试验注册 / 招募状态 / phase 1/2/3 / RCT lookup / sponsor 检索 / interventional trial / observational study / 主次要终点 / trial outcomes / 试验招募中 / recruiting status 时,通过 ClinicalTrials.gov API v2 实时检索官方注册数据。专注实时单次查询(≤1000 条/请求),典型场景: "某 NCT 试验最新状态"、"BTK 抑制剂 Phase 3 在招募试验"、"某试验已发布的主次要终点结果"。明确边界: 批量 >10k 试验 / 全库 SQL 历史分析 → aact-bulk-trials; 关联 PubMed 文献 → pubmed-eutils; RCT 自动 GRADE 评级 → medical-evidence-grading; 引用入报告附录 → evidence-appendix-sync。跨平台无锁(Claude Code / Codex / Cursor 通用)。

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Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.

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End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

日本語の概要は準備中です。原文の説明を表示しています。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Extract study sites, observational variables, coordinates, climate-forcing sources, and cited data sources from ecosystem-modeling papers, PDFs, supplements, or article text, then plan or create EcoSIM simulation workflows and run folders for those sites. Use when Codex is asked to read a paper for EcoSIM feasibility, including requests phrased as "read paper for ecosim feasibility"; read a paper for sites/data availability; reproduce a published site simulation in EcoSIM; compare EcoSIM outputs to paper observations; or convert paper-derived AmeriFlux, FLUXNET, US NLDAS-domain, non-US ERA5, crop, peatland, forest, grassland, wetland, or unmanaged field-site information into EcoSIM inputs and runnable cases.

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Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.

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