aeja-rebuttal

aeja-rebuttal is a skill for Claude Code from brycewang-stanford/Awesome-Journal-Skills. It costs 0 tokens per session (1,124 once invoked), scanned A, original, MIT.

A guide for responding to a decision letter from the American Economic Journal: Applied Economics. It structures a response-to-referees letter, which answers reviewer comments, and turns the requested changes into a revision plan.

In plain words
What is it for?
Use it after a revise-and-resubmit or conditional-acceptance decision to triage comments, plan revisions, and organize point-by-point answers. It does not run new analysis.
Why use it?
It helps separate the editor's required changes from optional suggestions and prevents important concerns from being buried in a long reply. It also keeps the response tied to concrete changes in the paper.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the aej-applied-economics-skills plugin — 12 skills shipped together

Good fit Use it after a revise-and-resubmit or conditional-acceptance decision to triage comments, plan revisions, and organize point-by-point answers. It does not run new analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal
About the project

Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.

brycewang-stanford/Awesome-Journal-Skills · 1,259 stars · on GitHub · copaper.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aeja-rebuttal
Clone the repo
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills

Made for: Claude Code.

Or install aej-applied-economics-skills, the plugin that ships this one along with the rest of its 12 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for aeja-rebuttal

README.md
[![agentmods](https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal/github.svg)](https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal)
Your own site
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for aeja-rebuttal

Your own site · 80×15
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-rebuttal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,124 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01124
Opus 5.5 $0.00000 $0.00450
Sonnet 5.5 $0.00000 $0.00225
Haiku 4.5 $0.00000 $0.00112

Measured 1mo ago against content hash ef2bff253540, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

aeja-rebuttal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AEJ-Applied-Economics-Skills/skills/aeja-rebuttal/SKILL.md · 77 lines

How it starts

The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Rebuttal & Response Letter (aeja-rebuttal)

When to trigger

  • An AEJ: Applied R&R or conditional acceptance decision letter has arrived
  • You need to triage referee comments into must-do / argue / defer
  • You are drafting the response-to-referees document and revision plan
  • A reject-and-resubmit invitation needs a strategy before re-engaging

How to read an AEJ: Applied decision

The editor's letter is the priority signal: it tells you which referee concerns are binding and which are optional. At AEJ: Applied the binding asks are almost always about design credibility (the identification, robustness, inference) before exposition. Address the editor's stated priorities first and in full; treat the referee reports as the menu the editor has already filtered. A revision is judged on whether the identification concerns are genuinely resolved, not on volume of new tables.

Response-letter structure

  1. Opening summary — thank the editor/referees; state the 3–5 substantive changes that define the revision (lead with the identification/robustness changes).
  2. Point-by-point — quote each comment, then respond with: what you did, where in the paper (section/table/page), and the result. Use a consistent format per point.
  3. Tone — gracious, concrete, non-defensive. Concede valid points fully; where you disagree, argue with evidence, not assertion.
  4. Triage discipline — every comment gets one of: done (changed + located), partially done (with reason), or respectfully declined (with an evidence-based argument). No comment is ignored.
  5. Highlight the decisive evidence — if a referee doubted the design, show the new heterogeneity-robust estimate / honest-DID bound / falsification result explicitly and quote the new number with its SE.

Triage table

Referee ask Default response
Core identification doubt (binding per editor) do it in full; show the new estimate + SE; update the paper
Robustness/inference request run it; report stability of the point estimate
External-validity / estimand add a calibrated scope statement; do not overclaim
Additional outcome/subgroup run with MHT adjustment; or argue why it is out of scope
Exposition / framing revise; cheap to do, builds goodwill
Out-of-scope new paper respectfully decline with a one-line reason

Read the full file on GitHub · 77 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 77 lines · 0 tokens per session scan A ef2bff253540

Subscribe to this mod's changes

aeja-rebuttal is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,259 stars, last pushed 19d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,124 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.

Related

Other skills, from other repositories

alterlab-qiime2-amplicon

Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier…

AlterLab-IEU/AlterLab-Academic-Skills · 270 tokens

research-manager

Use this when starting a new research project or managing a complex, multi-step research workflow.

poemswe/co-researcher · 21 tokens

alterlab-pyhealth

Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals…

AlterLab-IEU/AlterLab-Academic-Skills · 132 tokens

alterlab-citation-verifier

Verifies that every bibliography entry actually exists by cross-checking Crossref, OpenAlex, Semantic Scholar, and arXiv (no API key required) plus doi.org DOI registration, fuzzy-matching title and authors (difflib ratio >= 0.70), flagging retractions recorded by Crossref (including Retraction Watch data) or…

AlterLab-IEU/AlterLab-Academic-Skills · 219 tokens

alterlab-timesfm

Forecasts time series zero-shot with Google's TimesFM foundation models — TimesFM 2.5 (200M, Apache-2.0 weights; ForecastConfig API, XReg covariates) and TimesFM 3.0 (330M, multivariate with native past/future covariates; non-commercial weights) — producing point forecasts and quantile prediction intervals from…

AlterLab-IEU/AlterLab-Academic-Skills · 150 tokens

alterlab-anndata

Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…

AlterLab-IEU/AlterLab-Academic-Skills · 133 tokens