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Academic Paper Writer

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brycewang-stanford
academic-paper-writer

Draft economics papers with proper structure and academic style

Overview

Publisherbrycewang-stanford
RepositoryAuto-Empirical-Research-Skills
Skill nameacademic-paper-writer
Stars
3.8K
Forks
479
Bundled files
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Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Academic Paper Writer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git /tmp/Auto-Empirical-Research-Skills
mkdir -p .claude/skills
cp -r /tmp/Auto-Empirical-Research-Skills/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/writing/academic-paper-writer .claude/skills/academic-paper-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Paper Writer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Academic Paper Writer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Academic Paper Writer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Academic Paper Writer

Purpose

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.

When to Use

  • Starting a new research paper from scratch
  • Restructuring an existing draft
  • Writing specific sections (introduction, literature review, conclusion)
  • Preparing papers for journal submission

Instructions

Step 1: Identify Paper Type

Ask the user:

  1. Is this empirical or theoretical?
  2. What is the target journal/audience?
  3. What stage is the paper at? (outline, first draft, revision)
  4. What sections need help?

Step 2: Follow the IMRAD Structure

For empirical papers, use:

  1. Introduction - Motivation, research question, contribution
  2. Literature Review - Related work and positioning
  3. Data & Methods - Sources, sample, empirical strategy
  4. Results - Main findings with tables/figures
  5. Discussion - Interpretation, mechanisms, limitations
  6. Conclusion - Summary and implications

Step 3: Apply Economics Writing Conventions

  • First paragraph should state the research question and main finding
  • Use present tense for established facts, past tense for your findings
  • Be precise with causal language (effect vs. association)
  • Cite heavily in the literature review
  • Lead with results in the results section

Example Output: Introduction Template

latex
\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.

Example Output: Results Section Template

latex
\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}

Example Output: Conclusion Template

latex
\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].

Writing Tips

For Introductions

  • First sentence should grab attention - not "This paper examines..."
  • State your contribution clearly - what's new about this paper?
  • Be specific about magnitudes - don't just say "large effect"
  • Acknowledge limitations preemptively in the last paragraph

For Results

  • Lead with numbers - put the coefficient in the first sentence
  • Interpret economically - what does a 0.05 coefficient mean?
  • Guide the reader through tables column by column
  • Don't oversell - distinguish statistical from economic significance

For Conclusions

  • Don't introduce new results - synthesize what you've shown
  • Be honest about limitations - reviewers will find them anyway
  • End on the contribution - remind readers why this matters

Common Pitfalls

  • ❌ Burying the main result in the middle of the paper
  • ❌ Using "significant" without specifying statistical or economic
  • ❌ Over-claiming causality without proper identification
  • ❌ Literature review that's just a list of papers
  • ❌ Conclusion that's just a summary

References

Changelog

v1.0.0

  • Initial release with introduction, results, and conclusion templates

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Academic Paper Writer AI skill do?

Draft economics papers with proper structure and academic style

Why use Academic Paper Writer on TypingMind?

Because you install it once and use it with any model. Academic Paper Writer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Academic Paper Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/writing/academic-paper-writer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Academic Paper Writer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Academic Paper Writer?

As many as you like. As long as a model supports skills, you can use Academic Paper Writer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Academic Paper Writer AI skill free?

It is published on GitHub by brycewang-stanford. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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