Replication Package (expecon-replication-package)
SkillDev toolsUse when assembling the data, code, instructions, and experiment software for an Experimental Economics (ExpEcon) manuscript to meet the ESA reproducibility standard. Builds the deposit; it does not run the analysis or draft prose.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Replication Package (expecon-replication-package) skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Experimental-Economics-Skills/skills/expecon-replication-package/SKILL.md and read by ahel’s review.
When to trigger
- You are preparing to submit and must attach participant instructions (required at submission) and a data/code appendix
- The ESA Data and Replication Policy deposit (trusted repository) is not yet assembled
- z-Tree / oTree code, raw session data, and analysis scripts are scattered and not runnable end-to-end
- A referee or editor asks whether someone could reproduce your numbers and re-run your experiment
What ExpEcon reproducibility actually requires
Experimental Economics is an ESA journal, and since 2021 the ESA Data and Replication Policy requires authors to deposit, in a trusted online repository, the materials needed to reproduce or replicate the study (检索于 2026-06;以官网为准). Reproducibility here is stronger than at most economics journals because it has two layers:
- Reproduce the analysis — raw data + cleaning + analysis code regenerate every table and figure.
- Replicate the experiment — instructions + experiment software let another lab re-run the study.
Treat the package as a deliverable engineered for both.
The deposit, component by component
- Instructions — the exact instructions subjects received, per treatment, in the original language (translation if relevant). These are required at submission, not just at acceptance; reviewers read them to check for deception and comprehension.
- Experiment software — the z-Tree
.ztttreatment files or the oTree app (full project,settings.py, requirements pinned). Include screenshots or the comprehension quiz as run. This is what makes re-running possible. - Raw data — session-level exports as collected (z-Tree
.xls/.sbj, oTree CSV), with a codebook for every variable and the session/treatment/matching-group identifiers. - Analysis code — scripts (Stata/R/Python) that run from raw to results with a single master file; set and record the random seed for any simulation/permutation test.
- README — repository map, software versions, run order, expected runtime, and a table mapping each exhibit in the paper to the script that produces it.
- Pre-registration / PAP link — the registry entry and timestamp; for a Registered Report, the in-principle-acceptance Stage-1 protocol.
- Ethics / consent — IRB approval reference and the consent procedure (and the explicit no-deception statement).
Repository and hygiene
- Deposit in a trusted, persistent repository (OSF, Harvard Dataverse, Zenodo, or OpenICPSR are commonly used by ESA authors) and cite the DOI in the paper.
- Anonymize subject identifiers; never include payment records with identifying info.
- Pin every dependency and software version; a package that does not run on a clean machine fails the policy.
- Match repository contents to the paper exactly — no stale scripts, no figures the code cannot produce.
A workable directory layout
/instructions treatment_A.pdf, treatment_B.pdf (+ translations)
/software ztree/ *.ztt OR otree/ (full app, requirements.txt)
/data/raw session exports as collected (.xls/.sbj or .csv)
/data/clean analysis-ready files built by /code
/code 00_master.* , 01_clean.* , 02_analysis.* , 03_figures.*
/output tables + figures regenerated by /code
README.md map, versions, run order, exhibit→script table
ETHICS.md IRB ref, consent text, no-deception statement
The single rule the policy enforces in spirit: a stranger with a clean machine runs 00_master and gets your paper's exact numbers, and another lab opens /software and /instructions and re-runs your experiment.
The two-layer self-test
- Reproduce: delete
/data/cleanand/output, run the master script, confirm every table/figure regenerates byte-for-byte (or value-for-value for stochastic steps with a fixed seed). - Replicate: hand
/software+/instructionsto a colleague who was not on the project and confirm they can launch a session and understand what subjects faced.
Checklist
- Participant instructions (all treatments, original language) included at submission
- z-Tree
.ztt/ oTree app deposited so the experiment can be re-run - Raw session data + codebook + session/group/treatment IDs present
- Master analysis script runs raw→results; seeds set for simulation/permutation
- README maps every table/figure to the script that generates it; versions pinned
- Pre-registration / PAP (or Stage-1 RR protocol) linked with timestamp
- Trusted-repository DOI cited; data anonymized; IRB + no-deception statement included
Anti-patterns
- Promising the package "on request" or only at acceptance — ESA expects a real deposit, and instructions are due at submission
- Depositing data but not the z-Tree/oTree code, so the experiment cannot be replicated
- A "replication package" whose scripts do not reproduce the paper's exact numbers
- Unpinned software versions / no seed, so permutation tests and figures are not reproducible
- Identifiable subject data or payment records left in the repository
Output format
【Journal】Experimental Economics (ESA method flagship)
【Skill】expecon-replication-package
【Verdict】deposit-ready / incomplete
【Instructions】all treatments, at submission? [Y/N]
【Software】z-Tree .ztt / oTree app deposited (re-runnable)? [Y/N]
【Data + code】raw + codebook + master script (seeded) reproduce all exhibits? [Y/N]
【Repository】trusted-repo DOI; versions pinned; anonymized? [Y/N]
【Pre-reg / ethics】PAP/RR link + IRB + no-deception statement
【Next skill】expecon-referee-strategy
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
expecon-replication-package- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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