academic-paper-writer▌
meleantonio/awesome-econ-ai-stuff · updated Apr 8, 2026
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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.
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:
- Is this empirical or theoretical?
- What is the target journal/audience?
- What stage is the paper at? (outline, first draft, revision)
- What sections need help?
Step 2: Follow the IMRAD Structure
For empirical papers, use:
- Introduction - Motivation, research question, contribution
- Literature Review - Related work and positioning
- Data & Methods - Sources, sample, empirical strategy
- Results - Main findings with tables/figures
- Discussion - Interpretation, mechanisms, limitations
- 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
\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
\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
\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
- Cochrane (2005) Writing Tips for PhD Students
- Shapiro (2019) How to Give an Applied Micro Talk
- Thomson (2011) A Guide for the Young Economist
Changelog
v1.0.0
- Initial release with introduction, results, and conclusion templates
How to use academic-paper-writer on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add academic-paper-writer
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches academic-paper-writer from GitHub repository meleantonio/awesome-econ-ai-stuff and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate academic-paper-writer. Access the skill through slash commands (e.g., /academic-paper-writer) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★31 reviews- ★★★★★Isabella Flores· Dec 20, 2024
Useful defaults in academic-paper-writer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Chaitanya Patil· Dec 8, 2024
academic-paper-writer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ira Nasser· Dec 4, 2024
Registry listing for academic-paper-writer matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Piyush G· Nov 27, 2024
Keeps context tight: academic-paper-writer is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Meera Singh· Nov 23, 2024
academic-paper-writer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ava Sethi· Nov 11, 2024
I recommend academic-paper-writer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aditi Smith· Nov 3, 2024
We added academic-paper-writer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★William White· Oct 22, 2024
Solid pick for teams standardizing on skills: academic-paper-writer is focused, and the summary matches what you get after install.
- ★★★★★Shikha Mishra· Oct 18, 2024
Registry listing for academic-paper-writer matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Camila Smith· Oct 14, 2024
academic-paper-writer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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