5,823 件 ・ 関連度順
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
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。
Use when planning around the two-step proposal→invited-review process of the Academy of Management Annals (Annals) — the twice-yearly proposal cadence, double-blind proposal review, the editor-only full-review development, and timelines. Explains the process; it does not write the proposal (amann-proposal-framing) or run the portal preflight (amann-submission).
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。
Use when responding to an Academy of Management Annals (Annals) proposal decision or full-review referee letter — framework/attitude asks, coverage gaps, balance complaints, and accessibility. Drafts the response letter and revision plan; it does not run the portal preflight (amann-submission) or redesign the framework from scratch (amann-organizing-framework).
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。
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).
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。
Use when planning an AAAI project timeline from topic selection through abstract, OpenReview submission, two-phase review, rebuttal, decision, camera-ready, registration, presentation, and public artifact release.
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。
Use when running the final pre-submission preflight for an Academy of Management Annals (Annals) proposal or invited full review via the Manuscript Central portal — proposal-vs-invited-review route, deadlines, formatting, CV, and anonymity. Final checks; it does not draft content.
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。
Use when revising an AAAI manuscript for broad-AI-audience fit, a first-page contribution statement legible to non-specialist Phase-1 reviewers, two-column readability, concise novelty claims, reproducibility-checklist alignment, hedged limitations and ethics, and policy-aware framing of AI-system and capability claims.
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。
Use when revising an AAMAS paper so the interaction contribution leads the first page, the solution concept or incentive property is named, the game and agents are legible, claims are paired with proofs or multiagent experiments, the argument fits eight two-column pages plus references, and the wording stays double-blind.
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。
Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.
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。
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).
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。
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).
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。
Use when auditing an AAMAS main-track submission for OpenReview readiness, the 8-page-plus-references limit, the full-paper versus 2-page extended-abstract choice, double-blind anonymity, the supplementary zip cap, track fit across the AAAI/JAAMAS/Blue Sky tracks, dual-submission rules, and desk-reject triggers before upload.
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。
Use when working with the Associate Editor and Editor of an Academy of Management Annals (Annals) review — scope alignment after a proposal is invited, what review-article referees evaluate, and revision expectations. Plans the interaction; it does not run the portal preflight (amann-submission) or draft the response letter (amann-revision).
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。
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
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。
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).
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。
Use when auditing an AAAI main technical track submission for OpenReview readiness, double-blind anonymity, page limits, reproducibility checklist, supplementary material, author limits, multiple-submission policy, and AAAI AI-use policy compliance.
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。
Use when drafting an AAMAS rebuttal to preliminary OpenReview reviews, covering the double-blind constraint, the no-new-results and no-revised-paper norms, how to answer game-theory, MARL, and systems reviewers at once, and how to give the area chair a clean acceptance rationale within a short response window.
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。
Use when organizing AAAI supplementary material, including the technical appendix, multimedia appendix, and code/data ZIPs, while respecting that AAAI supplements are due with the paper, treated as immutable after submission, must stay double-blind, and should never hide main-paper-critical evidence from reviewers who skim the appendix.
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。
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
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。
Use when appraising the cumulative evidence and ensuring balance in an Academy of Management Annals (Annals) review — weighing conflicting findings by credibility, steelmanning rival schools, and handling the author's own work even-handedly. Audits evidence quality and fairness; it does not design the framework (amann-organizing-framework) or build exhibits (amann-tables-figures).
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。
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).
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。
This skill should be used when building, modifying, or debugging a react-admin application — including creating resources, lists, forms, data fetching, authentication, relationships between entities, custom pages, or any CRUD admin interface built with react-admin.
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。
When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome lists, use github. For Medium.com only, use medium-posts. For Grokipedia edits, use grokipedia-recommendations. For traditional Google SERP strategy, use seo-strategy.
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。
Use when a Unity 6.3 game is slow or stutters: low fps, frame spikes, hitches when entering areas or on first effects, GC spikes, too many draw calls or SetPass calls, CPU vs GPU bound questions, Profiler, Profile Analyzer, Memory Profiler, Frame Debugger or Project Auditor work, GPU Resident Drawer or SRP Batcher setup, LOD and Mesh LOD, shader warm-up and variants, memory or VRAM budgets, IL2CPP and build size, or proving a fix with numbers from a player.
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。