Guides authors through the full Econometric Theory journal submission lifecycle: framing theorem-proof contributions, structuring assumptions and proofs, designing Monte Carlo simulations, managing supplementary material, and preparing single-anonymous ScholarOne submissions with APA references and ET formatting specs.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Use to frame the central result of an Econometric Theory (ET) paper so its generality and importance are legible — stating the theorem and its scope up front, choosing Article vs Miscellanea framing, and matching the new editorial program's themed directions.
Use for the Monte Carlo and numerical-illustration component of an Econometric Theory (ET) paper — designing simulations that show finite-sample behavior tracks the asymptotics, plus any illustrative empirical example. Lighter than empirical journals; the spine stays the theory.
Use when the assumptions, regularity conditions, and asymptotic results are the bottleneck for an Econometric Theory (ET) theorem-proof paper — adapt "identification" to mean stating defensible assumptions, proving the limit theory, and establishing generality, not causal design.
Use to stake an Econometric Theory (ET) contribution against the existing theory frontier — which assumptions you weaken, which limit result you generalize, what was previously unproven — using APA author-date citations, not a standalone literature survey.
Use when an Econometric Theory (ET) decision letter arrives and a response strategy and letter are needed — handling proof-checking referee objections, fixing assumptions and gaps, and using the online Supplement, then writing a point-by-point response. It structures the revision, not the proofs themselves.
English | 简体中文
横跨经管社科 · 人文社科 · 自然科学 · 临床医学 · AI 计算机等多个主流学科,为每一本期刊 / 每一个会议单独编码它的投稿工作流。
🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🗺 路线图 · 🌐 English
先看期刊,再进 Pack。点击任意封面即可进入对应的期刊 Skill 包。
🆕 四个最新学科广度合集 —— 工程技术 40 · 农业环境 30 · 临床医学 30 · 英文人文 36;点击封面进入合集页。
其他合集 · 中文体育学 · 12 本 CSSCI 体育学来源刊
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin ectheory-skillsAI-first computer-science conference skill stack: 155 conference fit-and-submission profiles plus a CS/AI router. Covers top AI/ML, data mining, vision, NLP, robotics, HCI, systems, security, software engineering, programming languages, databases, and theory venues, with current-cycle CFP and author-kit re-check discipline.
Agent skill stack for submitting to 《经济研究》 (Economic Research Journal), the top economics journal in China. Eighteen skills across the manuscript lifecycle: China-context topic selection, introduction, bilingual literature review, theory & hypotheses, data & sample, modern causal identification (DID / IV / RDD / DML with heterogeneity-robust estimators), mechanism analysis (post-江艇 2022 paradigm), heterogeneity, robustness, three-line tables, policy implications, abstract, house style, reviewer-lens self-audit, reproducibility, submission preflight, and R&R rebuttals. Ships a runnable Stata + Python code library. Bilingual zh-CN / en docs.
Agent skill stack for articles and proposals targeted at the Journal of Economic Perspectives (JEP) — the open-access, non-technical synthesis journal of the American Economic Association, founded 1987, sister to the Journal of Economic Literature (technical surveys of record) and the AER/AEJ research journals. JEP is largely invited and organized in symposia, and rewards accessible writing readable by 90 percent of AEA members, not new identification or replication. Twelve role skills cover the JEP lifecycle: workflow routing, topic selection for a broad audience, the 2–5 page proposal and symposium pitch ([email protected]), narrative arc for a general economist reader, plain-language translation of technical results, presenting evidence with minimal equations, exhibits a non-specialist can read, the JEP voice, balance and objectivity over advocacy, working with the managing-editor-led editorial team, the pre-submission preflight, and revising for accessibility and balance. Bilingual en / zh-CN docs.
Agent skill stack for submitting to 《会计研究》 (Accounting Research) — the flagship journal of the Accounting Society of China and the only accounting title among CSSCI sources (monthly, founded 1980, ISSN 1003-2886, CN 11-1078/F). Built around the journal's defining bar: archival capital-market empirics with accurate institutional / standard-setting detail and an information mechanism, distinguished from generic corporate finance. Covers fit positioning, topic selection, literature review, institutional/standards background, accounting measurement (discretionary accruals, conservatism, disclosure indices), quasi-experimental identification, information mechanism, robustness, tables/figures, standard-setter/regulator implications, submission preflight, and R&R rebuttals.
Agent skill stack for manuscripts targeted at the Journal of Financial and Quantitative Analysis (JFQA) — empirical and quantitative financial economics (corporate finance, investments, capital and security markets, financial institutions, and finance-relevant quantitative methods), published by Cambridge University Press for the Michael G. Foster School of Business at the University of Washington. Built around the JFQA realities: submission via Editorial Manager with a text-searchable PDF, a $350 fee (only $275 refunded if not sent to a reviewer), double-anonymous review, a strict one-paragraph / 100-word abstract cap, prescriptive 8.5x11 / 1-inch / 12-pt Times New Roman double-spaced formatting, a one-year resubmission ban for undisclosed prior rejections, and the JFQA Code Sharing Policy with a dedicated JFQA Dataverse at the Harvard Dataverse. Covers topic fit, literature positioning, causal/identification design for finance, robustness, tables and figures, house style, the code/data archive, referee strategy, submission preflight, and R&R rebuttals. Bilingual en / zh-CN docs; Stata / R / Python conventions.
Agent skill stack for manuscripts targeted at The Econometrics Journal (EctJ) — an econometrics (theory + applied) journal established by the Royal Economic Society in 1998 and published by Oxford University Press. Built around the journal's distinctive norms: a hard ~20-page limit including the printed appendix, a 150-word summary, mandatory RES/EctJ LaTeX templates (separate template for the online appendix), a required empirical application even for theory, proofs kept out of the online appendix, a flat £75 (+20% VAT) submission fee with no RES-member discount, a one-week Editor-in-Chief desk screen with a ~3-month decision target via Editorial Express, and a conditional-on-acceptance replication package posted as OUP Supporting Information. Covers leading-case topic selection, literature positioning, regularity-condition / asymptotics identification, Monte Carlo and empirical-application analysis, compact tables and figures, contribution framing, house style, replication and data policy, referee strategy, submission preflight, and rebuttal. Bilingual en / zh-CN docs.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Develop, test, build, and deploy Godot 4.x games with Claude Code. Includes GdUnit4 testing, web/desktop exports, CI/CD pipelines, and deployment to Vercel/GitHub Pages/itch.io.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review