aejmac-replication-package
Use when assembling the data, code, and documentation package for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript to pass the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check.
Install / Use
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejmac-replication-packageInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Content & MediaSupported Platforms
Our assessment of aejmac-replication-package
aejmac-replication-package scores 85/100 on our quality scale, 666th of 1,179 Content & Media skills we index.
Its SKILL.md is 5.2 KB long, well organised into 10 sections with 1 code example: a solid amount of guidance for an agent.
With 1,158 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 18 days ago, so aejmac-replication-package is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
aejmac-replication-package compared with similar skills
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|---|---|---|---|---|
| aejmac-replication-package (this skill)by brycewang-stanford | 85 | 1.2k | 18d ago | SKILL.md |
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Frequently asked questions
- How do I install aejmac-replication-package?
- Run
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejmac-replication-package. The install tabs above show the steps for each supported agent. - Which AI agents does aejmac-replication-package work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is aejmac-replication-package safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is aejmac-replication-package still maintained?
- The repository was last updated 18 days ago, so aejmac-replication-package is actively maintained.
Skill content
View source on GitHubname: aejmac-replication-package description: Use when assembling the data, code, and documentation package for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript to pass the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check. Covers macro specifics (simulation/calibration code, restricted-access data); it does not write the analysis itself.
Replication Package (aejmac-replication-package)
When to trigger
- A paper is heading toward conditional acceptance and the AEA Data Editor check is next
- You have simulation/calibration code but have never packaged it for a reviewer to run
- The data include a restricted/proprietary source (confidential micro data, licensed series)
- You want to build the package as you go rather than scrambling at acceptance
The AEA reproducibility regime (verified 2026-06; re-confirm on the official AEA pages)
- Governed by the AEA Data and Code Availability Policy. Conditionally accepted papers undergo a review by the AEA Data Editor (Lars Vilhuber) before publication, including reproducibility checks and verification of the information provided.
- Deposit in the AEA Data and Code Repository on openICPSR (use is strongly encouraged; other trusted repositories may be allowed with Data Editor approval). Materials are posted with the article.
- Code scope is broad — and this is the macro-critical point: the policy covers data cleaning and "estimation, simulation, model solution, and visualization" code. For AEJ: Macro, the DSGE/HANK solver, the calibration/estimation routines, and the simulation code must all be in the package, not only the regression scripts.
- Restricted-access data: exceptions exist for confidential / copyrighted / agreement-restricted data; authors must preserve materials 5+ years, provide reasonable replication assistance, make the code public even when the data cannot be, and disclose data sources. State any such request to the Data Editor.
- Field experiments must be registered in the AEA RCT Registry.
Building a macro-grade package
Directory & master script
- A clear tree:
/data(raw + analysis),/code,/output(tables + figures),/docs. - One master script (
run_all) that regenerates every table and figure from raw inputs in order, including the model solution and simulation steps. - A README following the AEA template: data sources and access, software + versions, hardware, expected runtime, and a map from each exhibit to the script that makes it.
Macro-specific reproducibility
- Pin the toolchain: Stata version +
ssc/netpackage versions; Rrenv.lock; Pythonrequirements.txt/conda env; JuliaProject.toml/Manifest.toml; Dynare version for DSGE. - Seeds set and reported for every simulation, bootstrap, and randomization step.
- Long-running computations (global solutions, large HANK simulations, MCMC): provide a way to verify without a supercomputer — ship intermediate/cached outputs and a reduced-scale switch, and document expected full runtime.
- Numerical accuracy artifacts: include the diagnostics (Euler errors, grid checks) so the Data Editor can confirm the solution, not just rerun it.
Data documentation
- For each source: provider, exact extract/vintage, access date, license, and whether it is public or restricted.
- Real-time vs. revised macro vintages (e.g., ALFRED): document which you used.
- Restricted data: a clear access path and a public code subset that runs on synthetic/sample data where possible.
Checklist
- [ ] openICPSR (AEA Data and Code Repository) deposit planned; README on the AEA template
- [ ] Master
run_allregenerates every exhibit incl. model solution + simulation - [ ] Simulation, calibration/estimation, and solver code all included (not just regressions)
- [ ] Toolchain pinned (Stata/R/Python/Julia/Dynare versions); seeds set and reported
- [ ] Long-running steps: cached outputs + reduced-scale switch + runtime documented
- [ ] Restricted data: exception request stated; code public; access path documented; 5-year retention noted
- [ ] Every data source documented (provider, vintage, access date, license)
- [ ] Field experiments registered in the AEA RCT Registry
Anti-patterns
- Packaging only the regression scripts and omitting the DSGE solver / simulation code
- "Results available on request" instead of a deposited, runnable package
- Unpinned package versions, so the Data Editor cannot reproduce the numbers
- Unseeded simulations that do not reproduce
- A multi-day computation with no reduced-scale path or cached intermediates
- Discovering a data-license problem at acceptance instead of flagging it early
Output format
【Repository】openICPSR (AEA Data and Code Repository) deposit ready? [Y/N]
【Master script】run_all regenerates all exhibits incl. model+simulation? [Y/N]
【Code scope】solver + calibration/estimation + simulation + cleaning all included? [Y/N]
【Toolchain + seeds】versions pinned; seeds reported? [Y/N]
【Restricted data】exception stated; code public; access path documented? [Y/N / NA]
【Long runs】cached outputs + reduced-scale switch + runtime noted? [Y/N / NA]
【Next step】aejmac-referee-strategy
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
