mksc-methods
Use when the empirical/analytical approach is the bottleneck for a Marketing Science manuscript — choosing among structural econometrics, analytical modeling, and model-disciplined causal/ML methods, and making the model estimable and identified. Designs the approach; it does not execute the estimation and counterfactuals (mksc-data-analysis).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-methods --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Methods & Identification (mksc-methods) ## When to trigger - You must choose between a structural, analytical, or reduced-form/causal-ML approach - The model is written but not yet estimable (parameters, moments, normalization) - Identification is hand-waved ("we use instruments") without specifics - A reviewer says "the design cannot identify the structural parameters" ## Choose the genre that fits the claim Marketing Science is methodologically plural around a modeling core: structural econometrics, analytical models, econometric/statistical analysis, ML tools, surveys, and experiments — all judged by whether they develop, test, or rigorously apply a formal model. | Claim / goal | Approach that earns it | |------------------------------------------------|-------------------------------------------------------------------| | Quantify demand and simulate a policy | Structural demand (BLP/mixed logit), supply FOCs, counterfactual | | Forward-looking behavior, adoption, churn | Dynamic discrete choice / dynamic games (Rust, BBL, CCP) | | Strategic-interaction insight, comparative statics | Analytical (game-theoretic) model | | Bidding, sponsored search, marketplaces | Auction/struc
- When to trigger
- Choose the genre that fits the claim
- Make the model estimable and identified
- For analytical papers
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Methods pass for Marketing Science
- Output format
What does the mksc-methods skill do?
Use when the empirical/analytical approach is the bottleneck for a Marketing Science manuscript — choosing among structural econometrics, analytical modeling, and model-disciplined causal/ML methods, and making the model estimable and identified. Designs the approach; it does not execute the estimation and counterfactuals (mksc-data-analysis).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-methods --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.