skills/43-wentorai-research-plugins/skills/writing/latex/latex-templates-collection/SKILL.md
Collection of LaTeX templates for papers, presentations, and CVs
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research latex-templates-collectionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A skill providing a curated collection of LaTeX templates for academic papers, conference presentations, CVs, cover letters, and other documents commonly needed by researchers. Based on the latex-templates repository (655 stars), this skill helps researchers quickly start professionally formatted documents without spending hours on layout configuration.
Researchers spend significant time formatting documents to meet the requirements of different journals, conferences, and institutions. A well-organized template library eliminates this overhead by providing ready-to-use starting points that comply with common formatting standards. This skill catalogs templates by document type and provides guidance on selection, customization, and best practices for each category.
The templates cover the full range of academic document needs: research papers, technical reports, conference slides, academic CVs, cover letters, grant proposals, and poster presentations. Each template is designed for professional quality output with clean typography and proper academic formatting.
Journal Article Templates
Conference Paper Templates
Template Structure
Beamer Templates
Presentation Best Practices
Animation and Overlays
Academic CV Templates
Resume Templates
Cover Letter Templates
Thesis and Dissertation
Grant Proposals
Posters
Font Selection
mathptmx or newtxtext/newtxmathhelvet or sourcesanspro for modern appearanceinconsolata for code listingsColor Schemes
xcolor package with named colors for maintainabilityPage Layout
geometry package for margin and page size configurationsetspace package (single, 1.5, double)fancyhdr packageparskip or manual settingsThis skill supports the Research-Claw document preparation workflow:
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.