sciagent-skill-creator
Scaffold a new SciAgent-Skills entry. Picks pipeline/toolkit/database/guide template, creates skills/{category}/{name}/SKILL.md with valid frontmatter, appends…
Scientific presentations for conferences, seminars, thesis defenses, and grant pitches. Slide design, talk structure, timing, data viz for slides, QA. PowerPoint and LaTeX Beamer. For posters use latex-research-posters.
$ npx -y skills add jaechang-hits/SciAgent-Skills --skill scientific-slides --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/scientific-slidesContext preview
The summary Claude sees to decide when to auto-load this skill.
Scientific presentations for conferences, seminars, thesis defenses, and grant pitches. Slide design, talk structure, timing, data viz for slides, QA. PowerPoint and LaTeX Beamer. For posters use latex-research-posters.
name: scientific-slides description: "Scientific presentations for conferences, seminars, thesis defenses, and grant pitches. Slide design, talk structure, timing, data viz for slides, QA. PowerPoint and LaTeX Beamer. For posters use latex-research-posters." license: CC-BY-4.0
Scientific presentations are a critical medium for communicating research at conferences, seminars, defenses, and professional talks. This knowhow covers end-to-end presentation development: structure and content planning, visual design principles, data visualization adaptation, timing and pacing, and quality assurance across PowerPoint and LaTeX Beamer formats.
| Talk Type | Duration | Slides | Focus | Key Finding Count | |-----------|----------|--------|-------|-------------------| | Conference talk | 10–20 min | 12–20 | 1–2 key findings | 1–2 | | Academic seminar | 45–60 min | 40–60 | Comprehensive coverage | 3–6 | | Thesis defense | 45–60 min | 45–65 | Full dissertation | All studies | | Grant pitch | 10–20 min | 12–18 | Significance + feasibility | Preliminary data | | Journal club | 20–45 min | 20–40 | Critical analysis | Paper's findings |
**Visual-first approach**: Start with visuals (figures, diagrams, images), then add text as support. Target 60–70% visual content, 30–40% text. Every slide should have a strong visual element.
**Typography**:
**Color**:
**Layout**:
Key differences from journal figures:
| Chart Type | Best For | Slide Adaptation | |-----------|----------|-----------------| | Bar chart | Category comparison | Max 6–8 bars, large labels | | Line graph | Trends over time | Bold lines, 2–3 series max | | Scatter plot | Correlations | Large points, trend line | | Heatmap | Matrix patterns | High contrast, annotate key cells | | Flowchart | Methodology | Build step-by-step with animations |
Every scientific talk follows this narrative structure:
1. **Hook** — grab attention (30–60 seconds) 2. **Context** — establish importance (5–10% of talk) 3. **Problem/Gap** — identify what's unknown (5–10%) 4. **Approach** — explain your solution (15–25%) 5. **Results** — present key findings (40–50%) 6. **Implications** — discuss meaning (15–20%) 7. **Closure** — memorable conclusion (1–2 minutes)
Start: What is your priority?
├── Mathematical content, equations, version control?
│ └── YES → LaTeX Beamer (see assets/beamer templates)
├── Editable slides, company templates, animations?
│ └── YES → PowerPoint (programmatic or template-based)
├── Fast creation, non-technical audience, visual impact?
│ └── YES → PowerPoint or image-based PDF
└── Not sure
└── PowerPoint (most flexible default)| Duration | Simple Topic | Average | Complex Topic | |----------|-------------|---------|---------------| | 5 min | 5–6 | 6–8 | 5–7 | | 10 min | 10–12 | 12–14 | 10–12 | | 15 min | 14–16 | 16–18 | 14–16 | | 30 min | 25–30 | 30–35 | 25–30 | | 45 min | 38–45 | 45–50 | 38–45 | | 60 min | 50–55 | 55–65 | 50–60 |
General rule: ~1 slide per minute. Complex slides (results, methodology) may take 2–3 minutes; simple slides (transitions, section dividers) take 15–30 seconds.
| Section | % of Time | 15-min Talk | 45-min Talk | |---------|-----------|------------|------------| | Introduction | 15–20% | 2–3 min | 7–9 min | | Methods | 15–20% | 2–3 min | 7–9 min | | Results | 40–50% | 6–7 min | 18–22 min | | Discussion | 15–20% | 2–3 min | 7–9 min | | Conclusion | 5% | 45 sec | 2 min |
1. **MANDATORY: Every slide must have a strong visual element** — figure, chart, diagram, image, or icon. Text-only bullet list slides fail to communicate science effectively. Target minimum 2 visual elements per content slide.
2. **MANDATORY: Practice with a timer at least 3 times before presenting.** Set timing checkpoints: for a 15-minute talk, check at 3–4 min (finishing intro), 7–8 min (midway through results), 12–13 min (starting conclusions).
3. **Use minimal text as visual support.** 3–4 bullets per slide, 4–6 words per bullet. Text is the supporting role; visuals are the stars. Never put full paragraphs on slides.
4. **Include proper citations.** Cite 3–5 papers in the introduction (establishing context) and 3–5 in the discussion (comparison). Use author-year format (Smith et al., 2023) for readability.
5. **Design section dividers with visual breaks.** Insert visually distinctive slides between major sections (intro → methods → results → conclusion). These help the audience reset and follow the narrative.
6. **Anti-pattern — using default templates without customization.** Default PowerPoint/Beamer themes signal "
Turn your AI coding agent into a life sciences expert — 199 bioinformatics skills for Claude Code covering RNA-seq, single-cell analysis, genomics, proteomics, drug discovery, and more. Boosted BixBench from 65% to 92%. Open source.
Scaffold a new SciAgent-Skills entry. Picks pipeline/toolkit/database/guide template, creates skills/{category}/{name}/SKILL.md with valid frontmatter, appends…
Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP…
Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent…
Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for…
Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference,…
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm…