skills/29-quarcs-lab-project20XXy/dot-claude/skills/new-slide-deck/SKILL.md
Creates a Quarto revealjs slide deck in slides/ with the project style guide. Use when a presentation is needed.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research new-slide-deckInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Create a new Quarto revealjs slide deck in slides/ with the project's style guide applied.
$ARGUMENTS — the presentation title and optional subtitle (e.g., "Regional Disparities in GDP" or "Job Market Talk: Regional Disparities")Parse the title (and subtitle, if separated by a colon) from the arguments.
Check slides/ for existing files to avoid name collisions. Generate a filename using the naming convention from slides/README.md (e.g., analysis-results.qmd or 01-seminar-talk.qmd).
Create the .qmd file in slides/ with this YAML front matter (from the style guide in slides/README.md):
---
title: "<title>"
subtitle: "<subtitle if provided>"
author: "<from index.qmd author field, or [FILL: Author name]>"
date: today
format:
revealjs:
theme: simple
slide-number: true
css: |
.reveal h1, .reveal h2, .reveal h3 {
color: #2874A6;
}
.reveal strong {
color: #229954;
}
---
Pre-populate the body with a standard academic talk structure:
## Motivation
- [Key question or puzzle]
- [Why it matters]
- [What we do about it]
## Related Literature
- [Strand 1: ...]
- [Strand 2: ...]
- **This paper:** [Contribution]
## Data
- [Source and sample]
- [Key variables]
- [Summary statistics]
## Empirical Strategy
- [Identification approach]
- [Model specification]
## Main Results
- [Finding 1]
- [Finding 2]
## Robustness
- [Alternative specifications]
- [Placebo tests or falsification]
## Conclusion
- [Summary of findings]
- [Policy implications]
- [Future work]
## Thank You {.center}
Contact: [email]
Render the slide deck to verify it compiles:
quarto render slides/<filename>.qmd
Report the file path and the command to re-render.
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.