Claude Skills by brycewang-stanford

Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge.

Use when drafting AISTATS author responses or author-reviewer discussion replies under OpenReview, text-only discussion, no-link guidance, no revised-paper upload, anonymity requirements, and decision-focused clarification strategy.

Use when preparing accepted AISTATS papers for PMLR camera-ready submission, 9-page final body, author de-anonymization, proceedings forms, metadata, final appendix handling, registration, in-person presentation, and artifact release.

Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit.

Use when positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, and PMLR archival status.

Use when strengthening AISTATS reproducibility evidence, including the official checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, and code/data release statements.

Use when explaining or planning around AISTATS peer review, OpenReview review release, author-reviewer discussion, reviewer volunteer expectations, reviewer confidentiality, decision criteria, meta-review dynamics, and PMLR proceedings outcomes.

Use when auditing an AISTATS submission for OpenReview readiness, abstract/full-paper deadlines, 8-page submission body, double-blind anonymity, supplementary material, reproducibility checklist, dual-submission policy, reviewer volunteer requirements, and reviewer-discussion policy.

Use when preparing AISTATS supplementary material, appendices, proof details, code/data archives, simulation scripts, additional tables, and anonymized artifacts under deadline, size, anonymity, and reviewer-discretion constraints.

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, and sharpening the AI-statistics contribution before submission.

Use when planning an AISTATS project timeline from venue fit through abstract submission, full-paper upload, supplementary material, review release, author-reviewer discussion, decision, camera-ready, PMLR publication, registration, presentation, and artifact release.

Use when revising an AISTATS paper for concise AI-statistics framing, theorem-and-experiment clarity, 8-page submission compression, double-blind wording, reproducibility clarity, and statistically careful claims.

Use when turning results into an explicit theoretical contribution for an Academy of Management Journal (AMJ) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution and implications; it does not build the original theory (amj-theory-development) or run the analysis (amj-data-analysis).

Use when running and reporting the statistical analysis for an Academy of Management Journal (AMJ) manuscript — measurement validity, common-method bias, the right estimator (HLM, SEM, panel, experiments), endogeneity, and robustness. Executes and reports the analysis; it does not design the study (amj-methods) or frame the contribution (amj-contribution-framing).

Use when the front end of an Academy of Management Journal (AMJ) manuscript needs to engage the right literatures and stake a clear position in an ongoing theoretical conversation. Positions the paper among theories; it does not derive the hypotheses (amj-theory-development) or write the contribution paragraph (amj-contribution-framing).

Use when the research design and method are the bottleneck for an Academy of Management Journal (AMJ) manuscript — matching design (archival, survey, experiment, multi-method, field) and level of analysis to the theoretical question. Designs the study; it does not run the estimation or validity checks (amj-data-analysis).

Use when drafting the revision and response letter after an Academy of Management Journal (AMJ) revise-and-resubmit — structuring point-by-point responses to the action editor and reviewers across a multi-round developmental process. Drafts the response after revisions; it does not interpret the decision letter (amj-review-process).

Use when you need to understand or set expectations for the Academy of Management Journal (AMJ) editorial and peer-review process — desk review, the developmental multi-round R&R culture, and reading a decision letter. Explains the process and how to read decisions; it does not draft the response letter (amj-rebuttal).

Use when running the final pre-submission preflight for an Academy of Management Journal (AMJ) manuscript — ScholarOne portal, anonymization for double-blind review, AOM house-style formatting, files, and ethics declarations. Checks readiness to submit; it does not handle the post-decision response (amj-rebuttal).

Use when building or cleaning the tables and figures for an Academy of Management Journal (AMJ) manuscript — correlation tables, regression/SEM/HLM result tables, the theoretical-model figure, and interaction plots in AOM house style. Finalizes exhibits; it does not run the analysis (amj-data-analysis) or frame the contribution (amj-contribution-framing).

Use when the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

Use when scoping or sanity-checking a research question for an Academy of Management Journal (AMJ) manuscript — fit, theory-drivenness, and feasibility. Decides whether the idea can earn a theoretical contribution; it does not build the theory itself (use amj-theory-development) or position the literature (amj-literature-positioning).

Use when deciding which amj-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an Academy of Management Journal (AMJ) manuscript. Routes — it does not replace — the specialized skills.

Use for full-manuscript prose polish of an Academy of Management Journal (AMJ) manuscript — front-loading the argument, active voice, structure, and AOM house style. Polishes language and structure; it does not create the theoretical contribution (amj-contribution-framing) or fix the analysis (amj-data-analysis).

Use when articulating and differentiating the theoretical contribution of an Academy of Management Review (AMR) manuscript — showing precisely what is NEW versus prior theory and why it matters. Frames the contribution; it does NOT build the theory (amr-theory-development) or check its internal logic (amr-data-analysis).

Use when stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript — checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.

Use when you must identify which theoretical conversation an Academy of Management Review (AMR) manuscript enters and how it "challenges and extends" that conversation. Positions the argument within a literature; it does NOT summarize the field (AMR punishes review-essays) and does NOT build the new theory (that is amr-theory-development).

Use when choosing and applying a theory-construction method for an Academy of Management Review (AMR) manuscript — building constructs, defining their domain, specifying relationships, articulating the underlying logic/mechanisms, and setting boundary conditions. This is the THEORY-CONSTRUCTION craft, NOT empirical method; AMR publishes no datasets, no measures, and no statistical tests.

Use when writing the response document for an Academy of Management Review (AMR) Revise & Resubmit — structuring point-by-point replies that show the theory was genuinely strengthened, not just defended. Drafts the response; revise the manuscript's theory FIRST (amr-theory-development / amr-data-analysis) before writing the letter.

Use when you need to understand the Academy of Management Review (AMR) developmental, multi-round review and interpret a decision letter focused on theoretical novelty and logical soundness. Explains the process and decision types; it does NOT draft the response document (that is amr-rebuttal).

Use when running the final pre-submission preflight for an Academy of Management Review (AMR) manuscript — ScholarOne portal, AOM format and length, abstract, anonymization for review, references, ethics/originality, and required files. Theory-only journal; there is no data-availability or replication step.

Use when building the conceptual exhibits of an Academy of Management Review (AMR) manuscript — process models, 2x2 typologies, multi-level frameworks, and propositions tables that carry theoretical work. These are CONCEPTUAL figures, NOT data plots; AMR has no charts of results because it has no data.

Use when building the actual theory for an Academy of Management Review (AMR) manuscript — turning a positioned puzzle into defined constructs, explicit relationships, propositions, and boundary conditions. Constructs the theory; it does NOT stress-test the argument's logic (that is amr-data-analysis) or design any data collection (AMR has none).

Use when the theoretical puzzle is the bottleneck for an Academy of Management Review (AMR) manuscript — deciding whether an idea is theory-worthy and AMR-fit before any theorizing begins. Frames the puzzle; it does NOT build constructs (that is amr-theory-development) or design any empirical study (AMR has none).

Use when deciding which amr-* sub-skill to invoke next, or when sequencing a theory-building manuscript from theoretical-puzzle framing through developmental-review revision for an Academy of Management Review (AMR) submission. Routes — does not replace — the specialized skills.

Use when polishing the prose and structure of an Academy of Management Review (AMR) manuscript to AOM house style and an argument-driven voice. Late-stage polish; it does NOT build theory or check logic — do those first with amr-theory-development and amr-data-analysis.

Use to judge whether a manuscript's landing point is an ACCOUNTING issue (accounting information / disclosure / audit / standards) versus a corporate-finance mechanism, before drafting for 《会计研究》 (Accounting Research). Re-routes finance-mechanism papers to a finance journal.

Use when the empirical identification strategy is the bottleneck for a 《会计研究》 (Accounting Research) manuscript — exogenous standard / regulatory changes (event study, DID), PSM, Heckman, IV — and you need to stress-test the design before drafting tables. Prefers standard/regulatory shocks as the cleanest accounting identification source.

Use when writing the contribution and implications section for a 《会计研究》 (Accounting Research) manuscript — articulating both the accounting-theory contribution (role of accounting information) and concrete implications for standard-setters, regulators, and audit/reporting practice. Use after results and tables are final.

Use when writing or verifying the institutional / accounting-standard / regulatory background for a 《会计研究》 (Accounting Research) manuscript — getting standard clauses, regulatory rules, and effective dates exactly right, since precise institutional detail is a defining bar of this journal. Use before measurement and identification.

Use when building or repairing the literature review for 《会计研究》 (Accounting Research) — entering the accounting research lineage (earnings quality, conservatism, audit quality, disclosure, value relevance) rather than piling recent Chinese empirical citations, and positioning the gap as an accounting contribution.

Use when constructing or auditing accounting measures for 《会计研究》 (Accounting Research) — discretionary accruals (modified Jones), real earnings management (Roychowdhury), conservatism (Basu / C-Score), comparability, disclosure indices, audit-quality and tax-avoidance proxies — so each measure is built transparently and reproducibly. Use before identification.

Use when the mechanism analysis for a 《会计研究》 (Accounting Research) manuscript is missing or mis-specified — the channel must be an information mechanism (reduce information asymmetry, raise disclosure quality, constrain earnings management, improve audit assurance), not a generic pricing or financing channel. Use after the main result holds.

Use when responding to 《会计研究》 (Accounting Research) reviewer reports / an R&R — drafting a point-by-point response letter and 修改说明 that addresses accounting-measure construction, institutional/standard accuracy, identification, and information-mechanism concerns, while keeping the revision anonymous. Use only after the manuscript itself has been revised.

Use when building the robustness section for a 《会计研究》 (Accounting Research) manuscript — replacing accounting measures with alternatives, adding governance / institutional controls, placebo tests, and endogeneity follow-ups — so the main result survives an accounting referee's scrutiny. Use after mechanism is settled.

Use for the pre-submission preflight to 《会计研究》 (Accounting Research) — checking footnote (当页下注) conventions, the 著者—出版年制 reference style, abstract/keyword limits, article length, the online-only submission system via the Accounting Society of China site, double-blind anonymity, and document hygiene. Use right before submitting; verify every rule against the journal's current 投稿指南.

Use when finalizing tables and figures for a 《会计研究》 (Accounting Research) manuscript — keeping the main table lean, reporting coefficients with the right precision, and interpreting economic magnitude (not just significance stars) for accounting measures. Use after robustness is settled.

Use when framing or sharpening a topic for 《会计研究》 (Accounting Research) — turning a generic "X affects Y" empirical idea into an accounting contribution anchored in China's institutional setting, accounting theory, and standard-setting / practice relevance. Use before literature review.

Use when deciding which acr-* sub-skill to invoke next, or when sequencing manuscript work from fit positioning through rebuttal for 《会计研究》 (Accounting Research). Routes — does not replace — the specialized skills.

Use when sharpening the theoretical contribution and the "so what" for an Administrative Science Quarterly (ASQ) manuscript once results exist. Frames the contribution; it does not develop the underlying theory or run analysis.