mksc-workflow
GitHub作为路由助手，指导用户按顺序选择Marketing Science论文写作各阶段对应的专用技能。涵盖从选题、建模、文献定位到投稿及R&R回复的全流程，确保符合该期刊对数学模型的核心要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-workflow -g -y
SKILL.md
Frontmatter
{
"name": "mksc-workflow",
"description": "Use when deciding which mksc-* sub-skill to invoke next, or when sequencing a Marketing Science manuscript from modeling-driven topic selection through R&R rebuttal. Routes — it does not replace — the specialized skills."
}
Marketing Science Workflow (mksc-workflow)
Overview
This is the router. It does not replace any specialized skill; it tells you which mksc- skill to use right now* for your Marketing Science manuscript.
Default assumption: unless told otherwise, the target is Marketing Science, the flagship quantitative-marketing journal of the INFORMS Society for Marketing Science (ISMS), published by INFORMS. Its editorial statement says it "focuses primarily on articles that answer important research questions in marketing using mathematical modeling." The dominant genres are structural econometric models and analytical (game-theoretic) models; econometrics, ML, surveys, and experiments are welcome only when they develop, test, or rigorously apply a formal model. The non-negotiable bar is therefore: an important marketing question answered through a model, not a reduced-form correlation. Senior Editors and Associate Editors (not standing departmental Area Editors) route the paper; the editor and reviewers will press equally on "does the model identify the effect?" and "what do we learn about marketing?"
Editor-in-Chief: Puneet Manchanda (Michigan Ross), term Jan 1 2025–Dec 31 2027 (renewable through 2030), succeeding Olivier Toubia. Verify the masthead at the editorial-board page; fees and limits change.
Routing table
| Current symptom | Next skill |
|---|---|
| Question is vague; unsure it needs a formal model or fits MKSC | mksc-topic-selection |
| No model yet; mechanism/identification logic missing | mksc-theory-development |
| Front end ignores structural/analytical precedents | mksc-literature-positioning |
| Unsure: structural vs. analytical vs. causal-ML; estimable? | mksc-methods |
| Have a model and data; estimation, fit, counterfactuals unsettled | mksc-data-analysis |
| Results exist but "primary contribution dimension" is unclear | mksc-contribution-framing |
| Estimate/comparative-statics/counterfactual exhibits are weak | mksc-tables-figures |
| Notation-heavy, buries model intuition, off INFORMS style | mksc-writing-style |
| Ready to submit; need ScholarOne + blinding + replication package | mksc-submission |
| Want to understand double-anonymous SE/AE review | mksc-review-process |
| Received an R&R; need to plan and draft the response | mksc-rebuttal |
Default order
mksc-topic-selection— lock a modeling-worthy marketing questionmksc-theory-development— build the analytical/structural model + identificationmksc-literature-positioning— engage the modeling conversation you joinmksc-methods— pick the genre and make the model estimablemksc-data-analysis— estimate, assess fit, run counterfactuals, robustnessmksc-contribution-framing— name the primary contribution dimensionmksc-tables-figures— finalize estimate/counterfactual exhibits in INFORMS stylemksc-writing-style— front-load intuition; INFORMS author-year prosemksc-submission— ScholarOne preflight + replication-ready packagemksc-review-process— set expectations for double-anonymous multi-round reviewmksc-rebuttal— after an R&R, revise then draft the response
Decide the Frontiers vs. regular-article track early: Frontiers is a strict 6,000-word total cap and rewards one dominant contribution dimension. It changes scope, not polish.
Anti-patterns
- Jumping to
mksc-data-analysisbefore a model exists — MKSC rejects model-free correlation. - Polishing exhibits (
mksc-tables-figures) before identification is settled. - Treating a consumer-psychology experiment with no formal model as MKSC-ready — that is a JCR paper.
Stage ledger (keep it live in the conversation)
Marketing Science gates on a formal model that identifies an important marketing effect and yields a managerial counterfactual. The load-bearing rows are therefore model genre, identification, and counterfactual — track them explicitly, plus the Frontiers word cap if that track is in play:
MARKETING SCIENCE MANUSCRIPT STAGE LEDGER
=========================================
Model genre : structural econometric | analytical/game-theoretic | causal-ML-in-service-of-model
Identification : instrument | exclusion restriction | functional form | exogenous shock | (FIX: none)
Counterfactual : managerial decision margin the model makes answerable: ______
Track : regular (no fixed cap) | Frontiers (6,000-word TOTAL cap) | Database | Practice
Stage gates (✓ / ✗ / n/a):
[ ] mksc-topic-selection modeling-worthy marketing question (not reduced-form)
[ ] mksc-theory-development analytical/structural model + identification logic built
[ ] mksc-literature-positioning modeling conversation engaged
[ ] mksc-methods genre chosen; model estimable
[ ] mksc-data-analysis estimation, fit, counterfactuals, robustness
[ ] mksc-contribution-framing primary contribution dimension named
[ ] mksc-tables-figures estimate/counterfactual exhibits, INFORMS style
[ ] mksc-writing-style model intuition front-loaded; INFORMS author-year
[ ] mksc-submission ScholarOne preflight; blinded; replication package
[ ] mksc-review-process double-anonymous SE/AE expectations set
[ ] mksc-rebuttal (R&R only) revise + re-estimate first, then respond
Word count (if Frontiers): ___ / 6000 (counts refs, tables, appendices)
Current bottleneck : ______ Next skill: mksc-______
Refresh after each sub-skill. A ✗ on model genre or identification blocks mksc-data-analysis — MKSC rejects model-free correlation regardless of how clean the data work is.
Router pass for Marketing Science
Use this as a second-pass capability check. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then test whether the manuscript addresses quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.
- Primary move: Run fit gate, evidence gate, writing gate, source-map gate, and output contract; stop when a gate lacks evidence.
- Decision ledger: return
claim / evidence / blocker / next editrows so the next pass can patch the manuscript directly. - Neighbor test: compare against Journal of Marketing Research for empirical marketing breadth, Management Science for wider OR/MS reach, Quantitative Marketing and Economics for specialist modeling; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
- Verification floor: before submission-ready advice, re-open
resources/official-source-map.mdfor volatile rules and name the one unresolved fact that could change the recommendation.
Version History
- 1839142 Current 2026-07-05 14:04
