Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› psychrev-argument-development
psychrev-argument-development
GitHub用于心理评论期刊理论文章的论证构建，从模型假设推导预测并与现有数据及竞争模型对比。替代实证结果部分，强调逻辑严谨性、区分签名预测与数据拟合，并提供可证伪的新颖预测以确立理论优势。
Trigger Scenarios
模型已建立但未展示其具体预测内容
仅断言理论解释现象而未进行推导
未将预测与竞争模型在诊断性案例上进行对比
需要回应‘该理论是否可能错误’的质疑
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill psychrev-argument-development -g -y
SKILL.md
Frontmatter
{
"name": "psychrev-argument-development",
"description": "Use when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results section. Develops the argument; it does NOT build the model (psychrev-theory-construction) or set its scope and identifiability limits (psychrev-boundary-conditions)."
}
Argument Development: Deriving & Confronting Predictions (psychrev-argument-development)
When to trigger
- The model is built but you have not shown what it predicts
- You assert the theory "explains" phenomena without deriving them
- You have not compared your predictions to rival models on diagnostic cases
- A reviewer will ask "could this theory have been wrong?"
What replaces a results section here
Psychological Review has no experiment of its own as the contribution. The work that an empirical paper does with data, a Review paper does with derivation and confrontation: you derive predictions from the model's assumptions, then confront them with already- existing evidence and with what rival models predict. Logical and quantitative soundness is the rigor standard, exactly as statistical inference is at empirical journals.
The derivation discipline
- Derive, do not assert. For each phenomenon in the explanandum, show how it follows from the assumptions — analytically, or by simulation that traces assumptions → behavior. "The model can explain X" is worthless without the derivation that it does.
- Separate signature from accommodation. A strong prediction is a signature — a pattern the theory entails and rivals do not, ideally a parameter-free qualitative ordering or a novel pattern not used to build the model. Accommodating known data with fitted parameters is weaker; label it honestly as accommodation, not prediction.
- Make at least one risky, novel prediction. Falsifiability is the journal's currency: name a pattern that, if observed, would disconfirm the theory, and ideally one not yet tested so future work can adjudicate.
The confrontation discipline
- Confront existing data. Use published datasets (yours or others') to show the model reproduces the diagnostic phenomena. Report fit honestly: degrees of freedom, number of free parameters, and whether parameters were estimated or set a priori.
- Confront rival models head-to-head. On each diagnostic phenomenon, show what your model and the rival each predict, and why the data favor yours. A nested or formal model comparison (e.g., information criteria, parameter recovery) beats a verbal contrast.
- Address alternative explanations. For every prediction your model gets right, ask whether a simpler rival gets it right too; if so, the case is not diagnostic — find one that is.
- Probe robustness. Show the key results do not depend on a fragile parameter setting or an arbitrary functional form (sensitivity over a plausible range).
Quantitative honesty (for formal models)
- State the number of free parameters and what each was fit to.
- Distinguish fit (reproducing data used to build the model) from prediction (data the model was not tuned on).
- Prefer generalization tests (fit on one set, predict another) over in-sample fit.
- Beware flexibility: a model that can fit any pattern predicts nothing — show what it cannot do.
Checklist
- Each explanandum phenomenon is derived, not merely asserted, from the assumptions
- At least one risky, novel, falsifiable prediction is stated
- Signatures (rival-distinguishing) are separated from accommodations (fitted)
- Existing data are used to confront the model; free-parameter count is disclosed
- Head-to-head comparison with rival models on diagnostic phenomena is shown
- Alternative simpler explanations are ruled out on each diagnostic case
- Robustness to parameter/functional-form choices is demonstrated
Anti-patterns
- "The model can explain X" with no derivation that it does
- Fitting known data and calling accommodation a prediction
- A model so flexible it could fit any result (and therefore predicts nothing)
- Verbal hand-waving where a rival has a formal, quantitative account
- Hiding the number of free parameters or which data were used to fit them
- Picking only phenomena where all theories agree (non-diagnostic)
- Introducing a brand-new experiment as the deciding evidence (data only constrain here)
Output format
【Derivations】[phenomenon → how it follows from assumptions] for each
【Signatures vs. accommodations】[risky/novel predictions] | [fitted accommodations]
【Confrontation】existing data used; free-parameter count; fit vs. generalization
【Head-to-head】[diagnostic phenomenon → your prediction vs. rival's vs. data]
【Robustness】key results stable over parameter/form range: yes / fix
【Next step】psychrev-boundary-conditions (scope, identifiability, what it does NOT explain)
Version History
- 1839142 Current 2026-07-05 14:14
