Agent Skills
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pom-data-analysis
GitHub用于POM论文的分析与结果报告。涵盖优化模型的证明、数值模拟及均衡检查，以及实证、行为和运筹数据科学的识别策略、实验设计和预测-优化价值验证。旨在确保分析严谨性并突出管理启示。
Trigger Scenarios
模型构建或数据收集完成，需生成结果时
不确定数值、识别或验证是否满足审稿要求时
审稿人质疑分析不支持推论或效应大小不明确时
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pom-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "pom-data-analysis",
"description": "Use when executing and reporting the analysis for a Production and Operations Management (POM) manuscript — proving and numerically illustrating an analytical model, or estimating and validating an empirical \/ behavioral \/ operations-data-science study. Executes and reports; it does not pick the method (pom-methods) or frame the contribution (pom-contribution-framing)."
}
Analysis & Results (pom-data-analysis)
When to trigger
- The model is built or the data are collected and it is time to produce results
- You are unsure your numerics, identification, or validation will satisfy reviewers
- A reviewer says "the analysis does not support the inference" or "magnitude is unclear"
Analytical / modeling papers (POM's anchor track)
For optimization, stochastic, and game-theoretic work, the "analysis" is proof plus numerical illustration:
- Proofs. State each result as a numbered proposition/theorem; give clean, complete proofs. Per POM's format, push full proofs and supporting lemmas to the unlimited online e-companion, leaving intuition and the key steps in the main text.
- Structural insight. Report the structure of the optimal policy (base-stock, threshold, (s, S)) and comparative statics — how the decision moves with cost, lead time, or competition.
- Numerical study. Calibrate to realistic operational parameters; report sensitivity across plausible ranges; show the managerial magnitude of the effect, not just its sign.
- Game-theoretic checks. Confirm equilibrium existence/uniqueness; report off-equilibrium robustness where relevant.
Empirical, behavioral, and data-science papers
- Identification (empirical OM). Make the causal logic explicit; report the design (DiD/IV/RD/matching), parallel-trends or instrument validity, placebo tests, and clustered/robust standard errors matched to the operational sampling.
- Experiments (behavioral OM). Report randomization checks, power, manipulation and attention checks, and effect sizes; tie the result to the operational decision (e.g., order quantity, not just a rating).
- Operations data science. Report validation design, guard against leakage, and — decisively — the operational value: does the prediction improve a feasible policy or reduce a real operating cost (predict-then-optimize)?
- Simulation. Document parameter sources, seeds, warm-up, replications with confidence intervals, and sensitivity.
POM-specific reporting risks
- Operational variables named but measured in units a manager cannot act on.
- Statistical significance reported in place of managerial magnitude.
- ML accuracy reported with no link to an operations policy or cost.
- Same data used in prior work without the required cover-letter disclosure.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. POM spans analytical and empirical OM; apply the chain below to its empirical-OM papers, and note when a contribution is analytical / optimization.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Checklist
- Analytical: proofs complete (in e-companion), structural results + calibrated numerics + sensitivity
- Empirical: identification stated; placebo/robustness; SE clustering matches sampling
- Experiment: randomization, power, manipulation checks, effect sizes
- Data science: validation, leakage checks, operational value demonstrated
- Results expressed in decision-relevant operational magnitude
- Same-data disclosure prepared for the cover letter
Evidence pass for Production and Operations Management
Treat this skill as an executable review pass, not a prose hint. First lock the operational decision, the performance metric, and the implementable lever; then judge whether the current manuscript answers the venue's real reader: POM reviewers who want operational insight tied to production, service, supply-chain, or platform decisions.
- Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
- Return a ledger: give
claim / evidence / risk / manuscript locationrows, so the next agent can edit rather than rediscover the issue. - Sibling guard: compare against Management Science for broader OR/MS theory, Operations Research for method-first optimization, MSOM for manufacturing/service operations depth; if a sibling owns the contribution, recommend re-routing before polishing format.
- Stop condition: do not give submission-ready advice until the pack's
resources/official-source-map.mdhas been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.
Output format
【Analysis type】analytical-proof / causal / experiment / simulation / predictive
【Core result】policy structure / estimate / treatment effect / decision gain
【Main threat】proof gap / identification / leakage / measurement / power
【Managerial magnitude】effect in operational units (cost, fill rate, wait time)
【e-companion】proofs / extra analyses moved online
【Next step】pom-contribution-framing
Version History
- 1839142 Current 2026-07-05 14:13
