Guides authors through the full lifecycle of preparing and submitting a manuscript to the INFORMS Operations Research journal, from topic selection and model formulation to contribution statements, exhibits, reproducibility checks, and multi-round revisions.
Use when articulating the contribution of an Operations Research (OR) manuscript — especially the mandatory cover-letter contribution statement (since 1 June 2023, fewer than 500 words) and the discussion's significance claims. Frames why the work matters to OR; it does not position against specific prior papers (ors-literature-positioning) or run experiments (ors-data-analysis).
Use when running and reporting the computational study for an Operations Research (OR) manuscript — benchmark instances, baselines, reproducible experiments, statistical care for stochastic output, and the ORJournal code-and-data reproducibility workflow. Executes and reports the numerical evidence; it does not prove the results (ors-methods) or lay out the exhibits (ors-tables-figures).
Use when placing an Operations Research (OR) manuscript against the OR/MS literature — separating your model, results, and algorithmic guarantees from the closest prior work so the novelty is unambiguous. Positions the contribution; it does not formulate the model (ors-theory-development) or write the contribution statement (ors-contribution-framing).
Use when designing the proof technique, algorithm, or simulation protocol for an Operations Research (OR) manuscript — choosing the right machinery (duality, dynamic programming, probabilistic coupling, convergence analysis, simulation output analysis) to actually establish the claimed results. Establishes the results; it does not state the model (ors-theory-development) or run the experiments (ors-data-analysis).
Use when responding to an Operations Research (OR) decision letter — writing the point-by-point response, closing proof gaps, adding baselines/instances, and passing the ORJournal code/data reproducibility review across a revision cycle. Drafts the revision and response; it does not parse the decision in the first place (ors-review-process) or run the final preflight (ors-submission).
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin ors-skillsAgent skill stack for manuscripts targeted at Management Science, the INFORMS flagship that publishes both rigorous analytical/quantitative modeling (operations research, optimization, stochastic processes, game and economic theory) and empirical research across every functional business area (accounting, finance, marketing, operations, information systems, strategy, entrepreneurship, organizations, behavioral economics). Covers department/area-editor routing and cross-department fit, the dual-methodology rigor bar, double-anonymized review with high desk-reject screening, ScholarOne submission and the $79 submission fee with waivers, the INFORMS Data and Code Disclosure Policy verified by a dedicated Data Editor, author-year house style, invited-revision page limits, and R&R rebuttals. 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.
Harness-native ECC plugin for engineering teams - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
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.
AI-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.
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