Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use to understand how Agricultural Systems (AgSy) evaluates a manuscript — single-anonymized peer review with a minimum of two reviewers and an editor decision, desk screening for fit and systems content, what reviewers weigh, and the rapid-review track for Perspectives. Sets expectations and shapes the paper to survive review; it does not contact editors.
Use when handling primary sources and archives for an American Historical Review (AHR) manuscript — distinguishing primary from secondary, criticizing sources, citing manuscripts and collections in Chicago notes, and clearing image permissions. The AHR judges history on the quality and criticism of its evidence. Strengthens source work; it does not invent archival material.
Use when targeting AIChE Journal or deciding whether a chemical-engineering manuscript fits this venue. Encodes the journal's fit, the fundamental-and-generalizable contribution bar, modeling-plus-experiment rigor, house style, the fundamentals-vs-applied routing, official-submission re-check, and desk-reject heuristics.
Use when positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect.
Use when auditing an AISTATS submission for OpenReview readiness, abstract/full-paper deadlines, the 8-page submission body, double-blind anonymity, supplementary material, reproducibility checklist, dual-submission policy, reviewer volunteer requirements, desk-reject triggers, and final-week submission sequencing.
Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.
Use when executing and reporting the analysis for an American Journal of Political Science (AJPS) manuscript. AJPS will have a third-party verifier re-run your exact code against the numerical results in the main text before publication, so analyze reproducibly from the first line. Guides analysis and reporting norms; it does not fabricate results.
Use when defending the research design of an American Journal of Political Science (AJPS) manuscript — causal identification for observational work, experimental and survey-experimental design, formal-empirical linkage, or case-based inference. AJPS reviewers are quantitatively demanding, so identification must license the claim being made. Strengthens the design; it does not write code.
Use when deciding whether a political-science project fits the American Journal of Political Science (AJPS) and which submission type (Article, Research Note, Correspondence) to target. AJPS is a generalist but quantitatively leaning venue that rewards a sharp, well-identified question with a generalizable payoff. Helps frame the question and pick the type; it does not collect data.
Use when defending the research design of an American Journal of Sociology (AJS) manuscript on its own methodological terms — quantitative, comparative-historical, ethnographic, network, or formal. AJS judges each kind of work by the standards of its tradition; the design must support the theoretical claim. Defends the design; it does not run the analysis.