From jru-skills
Assembles data, code, and experiment materials for a Journal of Risk and Uncertainty (JRU) manuscript and writes its Data Availability Statement. Builds a transparent, reproducible package for experimental or structural estimation papers.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jru-skills:jru-replication-packageThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The paper has experimental or field results and you need a Data Availability Statement for the Springer submission
JRU requires a Data Availability Statement on original research articles, and Springer strongly encourages sharing the underlying research data (deposit in a recognized repository, with a citable DOI where possible). For this journal the package has two faces that generic econ replication advice misses: the experiment must be reproducible as a procedure (instructions, screens, incentive rules — not just the resulting dataset), and the structural estimation must be re-runnable (code that recovers the reported parameters). Exact policy wording and any mandatory-deposit details are 待核实 — verify on the official submission guidelines.
| Component | Experimental paper | Structural/empirical paper |
|---|---|---|
| Materials | full instructions, decision screens, comprehension checks, the incentive/payment protocol | data source + access terms, construction of the risk-exposure variable |
| Code | z-Tree/oTree/Qualtrics source + analysis scripts | estimation code (MLE/GMM/MSM), from raw data to every reported parameter |
| Data | subject-level choices (de-identified), session metadata, randomization seeds | analysis dataset or a clear access path if proprietary (e.g., admin/insurer data) |
| Reproducibility | a stranger can re-run the experiment AND re-derive the estimates | one script regenerates every table/figure from raw inputs |
A clean package for a JRU elicitation or estimation paper has a predictable shape:
/instructions — participant-facing text, decision screens, comprehension checks, payment protocol/experiment — z-Tree/oTree/Qualtrics source, with the random-incentive rule visible in code/data — de-identified subject choices, session metadata, randomization seeds, codebook/code — a single master script that runs raw → cleaned → every table/figure, plus the structural estimationREADME — software versions, run order, expected outputs, and the data-access path for any restricted inputsThe test is blunt: a colleague with the repository and nothing else should be able to (a) re-run the experiment and (b) reproduce every reported parameter.
A quick self-test: clone the package into a fresh directory, run the master script end to end, and confirm it reproduces the headline parameter without manual intervention.
An ambiguity-elicitation paper deposits only the cleaned choice matrix. A referee cannot tell whether the matching-probabilities task was incentive-compatible as run. The JRU-ready package adds the oTree source, the on-screen instructions and comprehension checks, the random-incentive payment rule, the session seeds, and a single script that goes from raw choices to the reported α-MEU estimate — so the elicitation and the estimate are both reproducible.
【Journal】Journal of Risk and Uncertainty
【Skill】jru-replication-package
【Verdict】ready / complete materials / fix access
【DAS drafted】matches deposit [Y/N]
【Experiment reproducible】instructions+screens+incentives+seeds [Y/N]
【Code reproducible】master script regenerates all exhibits [Y/N]
【Ethics/data】IRB documented; de-identified; access terms stated [Y/N]
【Policy status】verified / 待核实
【Next skill】jru-referee-strategy
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin jru-skillsAssembles data, code, and experimental materials for a JEBO manuscript reproducibility deposit, including z-Tree/oTree code, instructions, raw/analysis data, and an Elsevier Data statement.
Assembles data and code deposit for European Economic Review (EER) manuscripts under Elsevier's mandatory replication policy. Builds reproducible package and README.
Assembles the data and code deposit for an accepted REStud manuscript, writes the README, and audits reproducibility before the journal's Data Editor runs the pre-publication check.