AI导哈

文档办公

scientific-slides

k-dense-ai/scie…t-skills

Builds slide decks and presentations for research talks. Used for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.

  • 2232 次安装
  • 5.5万 星标
  • 收录于

安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill scientific-slides

SKILL.md

Scientific Slides

Overview

Scientific presentations are a critical medium for communicating research, sharing findings, and engaging with academic and professional audiences. This skill provides comprehensive guidance for creating effective scientific presentations, from structure and content development to visual design and delivery preparation.

Key Focus: Oral presentations for conferences, seminars, defenses, and professional talks.

CRITICAL DESIGN PHILOSOPHY: Scientific presentations should be VISUALLY ENGAGING and RESEARCH-BACKED. Avoid dry, text-heavy slides at all costs. Great scientific presentations combine:

  • Compelling visuals: High-quality figures, images, diagrams (not just bullet points)
  • Research context: Proper citations from research-lookup establishing credibility
  • Minimal text: Bullet points as prompts, YOU provide the explanation verbally
  • Professional design: Modern color schemes, strong visual hierarchy, generous white space
  • Story-driven: Clear narrative arc, not just data dumps

Remember: Boring presentations = forgotten science. Make your slides visually memorable while maintaining scientific rigor through proper citations.

When to Use This Skill

相关 Skills

scientific-writingk-dense-ai/scie…t-skillsDrafts, revises, and audits scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.营销与写作2480scientific-visualizationk-dense-ai/scie…t-skillsCreates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.数据与研究2447scientific-critical-thinkingk-dense-ai/scie…t-skillsEvaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.测试与安全2404literature-reviewk-dense-ai/scie…t-skillsConducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.测试与安全2382scientific-brainstormingk-dense-ai/scie…t-skillsFacilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.智能体工作流2335statistical-analysisk-dense-ai/scie…t-skillsGuided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.数据与研究2318