skills/31-thalysandratos-claude-code-skills/_skills/writing/academic-paper-writer/SKILL.md
Draft economics papers with proper structure and academic style
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research academic-paper-writerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill helps economists draft, structure, and polish academic papers with proper conventions for economics journals. It provides templates for different paper types and guidance on academic writing style.
Ask the user:
For empirical papers, use:
\section{Introduction}
% Hook - Why does this matter?
[TOPIC] is a fundamental question in economics, with implications for
[POLICY AREA] and [BROADER RELEVANCE]. Despite extensive research,
we still lack clear evidence on [SPECIFIC GAP].
% Research question
This paper asks: [RESEARCH QUESTION IN PLAIN LANGUAGE]?
Specifically, we examine whether [PRECISE FORMULATION OF THE QUESTION].
% Preview of answer
We find that [MAIN RESULT IN ONE SENTENCE]. This effect is
[economically significant / modest / heterogeneous], with
[QUANTITATIVE SUMMARY: e.g., "a one standard deviation increase
in X associated with a Y percent increase in Z"].
% Methodology (brief)
To identify this effect, we exploit [IDENTIFICATION STRATEGY:
natural experiment / RCT / instrumental variable / RDD].
Our data come from [DATA SOURCE], covering [TIME PERIOD]
and [SAMPLE SIZE] observations.
% Contribution / Related literature
Our paper contributes to several strands of literature.
First, we extend the work of \citet{Author2020} by [EXTENSION].
Second, we provide new evidence on [MECHANISM/CHANNEL] that
complements \citet{OtherAuthor2019}. Finally, our findings
have implications for [POLICY/FUTURE RESEARCH].
% Roadmap
The remainder of the paper is organized as follows.
Section~\ref{sec:background} provides background and reviews
related literature. Section~\ref{sec:data} describes our data
and empirical strategy. Section~\ref{sec:results} presents our
main findings. Section~\ref{sec:robustness} discusses robustness
checks. Section~\ref{sec:conclusion} concludes.
\section{Results}
\label{sec:results}
% Lead with the main finding
Table~\ref{tab:main} presents our main results. Column (1) shows
the baseline OLS specification without controls. The coefficient
on [TREATMENT VARIABLE] is [POINT ESTIMATE] (s.e. = [SE]),
statistically significant at the [1/5/10] percent level.
% Add controls incrementally
In column (2), we add [CONTROL SET 1]. The point estimate
[increases/decreases slightly/remains stable] to [ESTIMATE].
Column (3) includes [CONTROL SET 2] and adds [FIXED EFFECTS].
Our preferred specification in column (4) includes [FULL CONTROLS]
and yields [FINAL ESTIMATE].
% Interpret magnitude
To gauge economic significance, note that [INTERPRETATION].
A one standard deviation increase in [X] is associated with
a [Y] percent [increase/decrease] in [OUTCOME], or roughly
[COMPARISON TO MEAN/OTHER BENCHMARK].
% Brief mention of mechanisms/heterogeneity if relevant
Table~\ref{tab:hetero} explores heterogeneity by [DIMENSION].
We find that the effect is [larger/concentrated among]
[SUBGROUP], suggesting that [INTERPRETATION].
\begin{table}[htbp]
\centering
\caption{Main Results: Effect of X on Y}
\label{tab:main}
\begin{tabular}{lcccc}
\hline\hline
& (1) & (2) & (3) & (4) \\
& OLS & + Controls & + FE & Preferred \\
\hline
Treatment & 0.052*** & 0.048*** & 0.041** & 0.039** \\
& (0.012) & (0.011) & (0.015) & (0.016) \\
\\
Controls & No & Yes & Yes & Yes \\
Fixed Effects & No & No & Yes & Yes \\
Cluster SE & No & No & No & Yes \\
\\
Observations & 10,000 & 9,850 & 9,850 & 9,850 \\
R-squared & 0.05 & 0.12 & 0.35 & 0.35 \\
\hline\hline
\multicolumn{5}{l}{\footnotesize Notes: * p<0.10, ** p<0.05, *** p<0.01.} \\
\multicolumn{5}{l}{\footnotesize Standard errors in parentheses.} \\
\end{tabular}
\end{table}
\section{Conclusion}
\label{sec:conclusion}
% Restate question and answer
This paper examined [RESEARCH QUESTION]. Using [METHOD/DATA],
we found that [MAIN FINDING]. This result is robust to
[ROBUSTNESS CHECKS].
% Implications
Our findings have several implications. For policy, they suggest
that [POLICY IMPLICATION]. For theory, they provide support for
[THEORETICAL MECHANISM] and challenge [ALTERNATIVE VIEW].
% Limitations (brief, honest)
Several limitations warrant mention. First, [LIMITATION 1:
e.g., external validity]. Second, [LIMITATION 2: e.g.,
data constraints]. Future research could address these by
[SUGGESTION].
% Future directions
This paper opens several avenues for future work.
[DIRECTION 1]. [DIRECTION 2]. We hope our findings
stimulate further research on [BROADER TOPIC].
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.