Claude Skills by brycewang-stanford

Use when running the final pre-submission preflight for a Physical Review Letters manuscript — length check, format, files, Supplemental Material, classification, author info, and metadata. Verifies submission readiness; does not write content.

Use when partitioning content between the Physical Review Letters body and its Supplemental Material, ensuring the Letter stands alone while derivations and extended data live in the SM. Organizes the SM; does not write the Letter body.

Use when deciding which prl-* sub-skill to invoke next, or when sequencing manuscript work from scope-fit through revision for a Physical Review Letters (PRL) manuscript. Routes — does not replace — the specialized skills.

Use when polishing the prose of a Physical Review Letters manuscript for APS house style, concision, precise notation, and out-of-subfield readability. Polishes language; does not restructure the result or manage length budget.

Use when framing the contribution of a Production and Operations Management (POM) manuscript — stating the managerial insight for practicing operations managers and the advance to OM knowledge, tied to the target Department. Frames the contribution; it does not run the analysis (pom-data-analysis) or polish prose (pom-writing-style).

Use when executing and reporting the analysis for a Production and Operations Management (POM) manuscript — proving and numerically illustrating an analytical model, or estimating and validating an empirical / behavioral / operations-data-science study. Executes and reports; it does not pick the method (pom-methods) or frame the contribution (pom-contribution-framing).

Use when positioning a Production and Operations Management (POM) manuscript within an operations conversation — against the target Department's prior work and adjacent OM journals (M&SOM, Management Science, Operations Research, JOM). Positions the paper; it does not build the model (pom-theory-development) or state the contribution (pom-contribution-framing).

Use when selecting or auditing the method for a Production and Operations Management (POM) manuscript — analytical modeling (optimization, stochastic models, game theory), empirical identification, behavioral experiments, simulation, or operations data science. Matches method to the operations question; it does not execute the analysis (pom-data-analysis).

Use when drafting the revision and point-by-point response after a Production and Operations Management (POM) revise-and-resubmit — addressing the Department Editor and referees, splitting changes between the main paper and the e-companion, and respecting the 32-page cap. Drafts the response after revisions; it does not interpret the decision letter (pom-review-process).

Use when understanding or planning for Production and Operations Management (POM) review — Department Editor screening, double-blind review, the rigor-and-practice evaluation, and the strict no-resubmission-after-rejection rule. Explains the process and reads decisions; it does not draft the response (pom-rebuttal).

Use when running the pre-submission preflight for a Production and Operations Management (POM) manuscript on ScholarOne — Department Editor routing, 32-page limit, double-blind files, e-companion designation, 350-word abstract, same-data disclosure, and the no-resubmission-after-rejection rule. Preflights the submission; it does not interpret decisions (pom-review-process).

Use when designing the exhibits for a Production and Operations Management (POM) manuscript — result tables, comparative-statics and sensitivity plots, model schematics, empirical tables, simulation plots — and deciding what stays in the 32-page main paper vs. the online e-companion. Designs exhibits; it does not run the analysis (pom-data-analysis).

Use when building the model or mechanism for a Production and Operations Management (POM) manuscript — formalizing assumptions, deriving equilibria/propositions for analytical work, or developing OM hypotheses for empirical/behavioral work. Builds the argument; it does not run the analysis (pom-data-analysis) or frame the contribution (pom-contribution-framing).

Use when deciding whether an operations-management project fits Production and Operations Management (POM), which POM Department to route it to, or whether to target M&SOM, Management Science, JOM, or another venue. Sets the question and venue; it does not build the model (pom-theory-development).

Use when deciding which pom-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for a Production and Operations Management (POM) manuscript. Routes — it does not replace — the specialized skills, and it identifies your method track (analytical / empirical / behavioral / data-science).

Use when revising the prose of a Production and Operations Management (POM) manuscript — front-loading the operations problem, balancing theory with practice relevance, applying author-year citations, and enforcing the 32-page cap with an e-companion split. Polishes prose; it does not build the model (pom-theory-development) or design exhibits (pom-tables-figures).

Use when defining eligibility criteria and coding studies for a Psychological Bulletin review or meta-analysis — inclusion/exclusion rules, a codebook, double-coding, and inter-rater reliability. Governs how studies enter and are coded; effect-size modeling lives in psychbull-meta-analysis-methods.

Use when designing and documenting the systematic literature search for a Psychological Bulletin review or meta-analysis — databases, search strings, grey literature, deduplication, and a PRISMA-compliant records flow. Builds a reproducible search; it does not screen or code studies.

Use when computing effect sizes and fitting the meta-analytic model for a Psychological Bulletin manuscript — effect-size metrics, random-effects vs. fixed-effect choice, dependent effect sizes (RVE / multilevel), and heterogeneity (Q, I², τ², prediction interval). Guides the core synthesis; moderators and bias diagnostics live in psychbull-moderators-and-bias.

Use when explaining heterogeneity and probing robustness in a Psychological Bulletin meta-analysis — moderator/subgroup analysis, meta-regression, and publication-bias diagnostics (funnel, Egger, trim-and-fill, PET-PEESE, p-curve, selection models) plus sensitivity analyses. Extends the core model; estimation lives in psychbull-meta-analysis-methods.

Use when planning and assembling the transparency package for a Psychological Bulletin submission — protocol preregistration (PROSPERO/OSF), TOP-compliant disclosure, and depositing the database, codebook, and analysis scripts. Covers transparency requirements; it does not run the analysis.

Use when writing the response to a Psychological Bulletin revise-and-resubmit. A synthesis R&R often demands re-running the search, adding studies, or new bias/moderator analyses, so the response must satisfy every reviewer and the editor while keeping the meta-analysis internally consistent. Structures the response letter; it does not fabricate results.

Use when running the final pre-submission preflight for Psychological Bulletin via APA Editorial Manager — masking, the ≤ 250-word abstract, APA 7th style, MARS/PRISMA/JARS checklists, and the transparency package. Final checks; it does not draft content.

Use when building exhibits for a Psychological Bulletin manuscript — the PRISMA flow diagram, forest plots, funnel/bias plots, moderator bubble plots, and MARS-ready summary tables in APA 7th-edition format. Guides exhibit design; it does not generate the underlying estimates.

Use when turning the synthesis results of a Psychological Bulletin manuscript into a genuine theoretical contribution — reconciling conflicting findings, building or refining a model, and stating scope conditions and a research agenda. Makes the synthesis matter; it does not run the analysis.

Use when deciding whether a question is review-worthy and meta-analyzable for Psychological Bulletin and which synthesis type fits (meta-analysis, systematic review, meta-review/meta-synthesis, qualitative review). Tests fit for an APA research-synthesis flagship; it does not write the review.

Use as the entry point for any Psychological Bulletin manuscript. Routes to the right psychbull sub-skill based on where you are in the research-synthesis lifecycle and which synthesis type (meta-analysis, systematic review, meta-review/meta-synthesis, qualitative review) fits. Psychological Bulletin publishes syntheses, not original studies — it dispatches; it does not draft content.

Use when drafting and polishing a Psychological Bulletin manuscript so it reads as an integrative synthesis and meets APA 7th-edition style plus MARS/PRISMA/JARS reporting structure, including the ≤ 250-word abstract. Guides prose and structure; it does not run the analysis.

Use when analyzing and reporting results for a Psychological Science manuscript. The journal requires effect sizes with confidence intervals, full disclosure of exclusions/conditions/measures, and a clear confirmatory/exploratory split, with analysis scripts and data shared. Guides analysis norms; it does not fabricate results.

Use when positioning a Psychological Science manuscript against the literature within the journal's tight word budget. With roughly 40 references and a 2,000-word Introduction+Discussion, positioning must be sharp — establish the gap and the contribution fast. Stakes the contribution; it does not write the literature review.

Use when meeting Psychological Science's open-science requirements. For submissions on or after 1 January 2024 the journal requires open data and materials (case-by-case exemptions), a Research Transparency Statement placed between the Introduction and Methods, persistent identifiers for shared content, and treats transparency limits and preregistration quality as factors in editorial decisions. Prepares compliance; it does not waive requirements.

Use when writing the response to a Psychological Science revise-and-resubmit. Reviews here often demand added robustness, fuller disclosure, or stronger transparency, so the response must address every point and strengthen credibility without breaking the tight format. Structures the response letter; it does not fabricate new results.

Use to understand how Psychological Science evaluates a manuscript — anonymized peer review, editorial weighting of robustness, transparency, and preregistration quality, and the Registered Reports route. Sets expectations and shapes the paper to survive review; it does not contact editors.

Use when designing studies for a Psychological Science manuscript so they meet the journal's standards for power, sample-size justification, preregistration, and confound control. Strengthens the design and pre-analysis plan; it does not write code.

Use when running the final pre-submission preflight for Psychological Science via Manuscript Central — word-format compliance, the 150-word structured abstract, APA 7th-edition style, anonymization, the Research Transparency Statement, and open data/materials with DOIs. Final checks; it does not draft content.

Use when building tables and figures for a Psychological Science manuscript. The journal uses APA 7th-edition style and asks that tables and figures be embedded within the main text near where they are discussed, and exhibits should show effect sizes and uncertainty. Designs exhibits; it does not run the analysis.

Use when stating the theory and hypotheses for a Psychological Science manuscript. The journal's credibility norms require a clear separation of confirmatory (preregistered) from exploratory analyses and theory stated before results. Structures the argument; it does not run analyses.

Use when deciding whether an empirical psychology project fits Psychological Science and which manuscript type to target. The journal favors concise, high-impact, well-powered work whose claims are robust and broadly relevant. Frames the question; it does not collect data.

Use as the entry point for any Psychological Science manuscript. Routes to the right sub-skill based on lifecycle stage and manuscript type (Research Article, Registered Report Stage 1/2, Registered Report with Existing Data, Commentary), and flags the journal's tight word format and open-science requirements. It dispatches; it does not draft content.

Use when drafting or polishing a Psychological Science manuscript to fit its distinctive word format (Introduction + Discussion + footnotes + acknowledgments + appendices <= 2,000 words combined; Method + Results excluded; 150-word structured abstract) in APA 7th-edition style. Tightens prose and format; it does not invent content.

Use when executing and reporting the analysis for a Public Opinion Quarterly (POQ) manuscript so it survives expert, double-blind review — design-based inference that respects survey weights, strata, and clusters, honest uncertainty, robustness, and reproducibility. POQ verifies that code reproduces every table and figure. Guides analysis norms; it does not fabricate results.

Use when positioning a Public Opinion Quarterly (POQ) manuscript against the literature so it reads as a contribution to public-opinion or survey-methodology scholarship. POQ readers span opinion theory, communication, political behavior, and survey methods, so the paper must engage the right literatures and show the methodological lineage. Stakes the contribution; it does not write the lit review.

Use when writing the response to a Public Opinion Quarterly (POQ) revise-and-resubmit. POQ referees are survey methodologists who often push on Total Survey Error and AAPOR disclosure, so the response must satisfy each referee on substance and method while keeping the associate editor confident the revision converges. Structures the response letter; it does not fabricate new results.

Use to understand how Public Opinion Quarterly (POQ) evaluates a manuscript — double-blind review via ScholarOne Manuscripts, typically two to three referees, the survey-methodology and AAPOR-disclosure bar reviewers apply, and how associate editors may request code/data during review. Sets expectations and shapes the paper to survive review; it does not contact editors.

Use when running the final pre-submission preflight for Public Opinion Quarterly (POQ) via ScholarOne Manuscripts — submission-type selection, double-blind preparation, word caps, AAPOR-standard Appendix A disclosure, the Data Availability Statement, and the replication package. Final checks; it does not draft content.

Use when defending the survey design and measurement of a Public Opinion Quarterly (POQ) manuscript through the Total Survey Error framework — coverage, sampling, nonresponse, measurement (question wording/order/scales), mode effects, and weighting. POQ is the leading survey-methodology journal and expects AAPOR-standard rigor. Strengthens the design; it does not write code.

Use when building tables and figures for a Public Opinion Quarterly (POQ) manuscript so exhibits are self-contained, accessible, and show survey-based uncertainty correctly. POQ word caps count text and notes but exclude figures and tables, so exhibits carry weight without costing the budget — but they must report design-based uncertainty and weighting. Designs exhibits; it does not run the analysis.

Use when building the theoretical argument and hypotheses of a Public Opinion Quarterly (POQ) manuscript — whether the contribution is to opinion/communication theory or to survey methodology. POQ rewards explicit constructs, mechanisms, and testable hypotheses, and methods papers need a clear claim about a survey-error source. Structures the argument; it does not run analyses.

Use when deciding whether a project fits Public Opinion Quarterly (POQ) and which submission type to target. POQ is the leading journal of public opinion and survey methodology, so the test is a contribution to opinion/communication theory, to current public opinion, or to survey validity — not a generic social-science finding. Helps frame the question; it does not collect data.

Use when preparing the transparency materials for a Public Opinion Quarterly (POQ) manuscript — the AAPOR-standard "Appendix A: Disclosure Elements", the replication package deposited to POQ's Harvard Dataverse, and the Data Availability Statement. POQ requires materials that reproduce exactly all published tables and figures, archived before typesetting. Prepares the materials; it does not waive requirements.