latex-posters▌
K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026
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### Latex Posters
- ›name: "latex-posters"
- ›description: "Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design,..."
- ›allowed-tools: "Read Write Edit Bash"
| name | latex-posters |
| description | "Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication." |
| allowed-tools | Read Write Edit Bash |
| metadata | version: "1.0" |
LaTeX Research Posters
Overview
Research posters are a critical medium for scientific communication at conferences, symposia, and academic events. This skill provides comprehensive guidance for creating professional, visually appealing research posters using LaTeX packages. Generate publication-quality posters with proper layout, typography, color schemes, and visual hierarchy.
When to Use This Skill
This skill should be used when:
- Creating research posters for conferences, symposia, or poster sessions
- Designing academic posters for university events or thesis defenses
- Preparing visual summaries of research for public engagement
- Converting scientific papers into poster format
- Creating template posters for research groups or departments
- Designing posters that comply with specific conference size requirements (A0, A1, 36×48", etc.)
- Building posters with complex multi-column layouts
- Integrating figures, tables, equations, and citations in poster format
AI-Powered Visual Element Generation
STANDARD WORKFLOW: Generate ALL major visual elements using AI before creating the LaTeX poster.
This is the recommended approach for creating visually compelling posters:
- Plan all visual elements needed (title, intro, methods, results, conclusions)
- Generate each element using scientific-schematics or Nano Banana Pro
- Assemble generated images in the LaTeX template
- Add text content around the visuals
Target: 60-70% of poster area should be AI-generated visuals, 30-40% text.
CRITICAL: Preventing Content Overflow
⚠️ POSTERS MUST NOT HAVE TEXT OR CONTENT CUT OFF AT EDGES.
Common Overflow Problems:
- Title/footer text extending beyond page boundaries
- Too many sections crammed into available space
- Figures placed too close to edges
- Text blocks exceeding column widths
Prevention Rules:
1. Limit Content Sections (MAXIMUM 5-6 sections for A0):
✅ GOOD - 5 sections with room to breathe:
- Title/Header
- Introduction/Problem
- Methods
- Results (1-2 key findings)
- Conclusions
❌ BAD - 8+ sections crammed together:
- Overview, Introduction, Background, Methods,
- Results 1, Results 2, Discussion, Conclusions, Future Work
2. Set Safe Margins in LaTeX:
% tikzposter - add generous margins
\documentclass[25pt, a0paper, portrait, margin=25mm]{tikzposter}
% baposter - ensure content doesn't touch edges
\begin{poster}{
columns=3,
colspacing=2em, % Space between columns
headerheight=0.1\textheight, % Smaller header
% Leave space at bottom
}
3. Figure Sizing - Never 100% Width:
% Leave margins around figures
\includegraphics[width=0.85\linewidth]{figure.png} % NOT 1.0\linewidth
4. Check for Overflow Before Printing:
# Compile and check PDF at 100% zoom
pdflatex poster.tex
# Look for:
# - Text cut off at any edge
# - Content touching page boundaries
# - Overfull hbox warnings in .log file
grep -i "overfull" poster.log
5. Word Count Limits:
- A0 poster: 300-800 words MAXIMUM
- Per section: 50-100 words maximum
- If you have more content: Cut it or make a handout
CRITICAL: Poster-Size Font Requirements
⚠️ ALL text within AI-generated visualizations MUST be poster-readable.
When generating graphics for posters, you MUST include font size specifications in EVERY prompt. Poster graphics are viewed from 4-6 feet away, so text must be LARGE.
⚠️ COMMON PROBLEM: Content Overflow and Density
The #1 issue with AI-generated poster graphics is TOO MUCH CONTENT. This causes:
- Text overflow beyond boundaries
- Unreadable small fonts
- Cluttered, overwhelming visuals
- Poor white space usage
SOLUTION: Generate SIMPLE graphics with MINIMAL content.
MANDATORY prompt requirements for EVERY poster graphic:
POSTER FORMAT REQUIREMENTS (STRICTLY ENFORCE):
- ABSOLUTE MAXIMUM 3-4 elements per graphic (3 is ideal)
- ABSOLUTE MAXIMUM 10 words total in the entire graphic
- NO complex workflows with 5+ steps (split into 2-3 simple graphics instead)
- NO multi-level nested diagrams (flatten to single level)
- NO case studies with multiple sub-sections (one key point per case)
- ALL text GIANT BOLD (80pt+ for labels, 120pt+ for key numbers)
- High contrast ONLY (dark on white OR white on dark, NO gradients with text)
- MANDATORY 50% white space minimum (half the graphic should be empty)
- Thick lines only (5px+ minimum), large icons (200px+ minimum)
- ONE SINGLE MESSAGE per graphic (not 3 related messages)
⚠️ BEFORE GENERATING: Review your prompt and count elements
- If your description has 5+ items → STOP. Split into multiple graphics
- If your workflow has 5+ stages → STOP. Show only 3-4 high-level steps
- If your comparison has 4+ methods → STOP. Show only top 3 or Our vs Best Baseline
Content limits per graphic type (STRICT):
| Graphic Type | Max Elements | Max Words | Reject If | Good Example |
|---|---|---|---|---|
| Flowchart | 3-4 boxes MAX | 8 words | 5+ stages, nested steps | "DISCOVER → VALIDATE → APPROVE" (3 words) |
| Key findings | 3 items MAX | 9 words | 4+ metrics, paragraphs | "95% ACCURATE" "2X FASTER" "FDA READY" (6 words) |
| Comparison chart | 3 bars MAX | 6 words | 4+ methods, legend text | "OURS: 95%" "BEST: 85%" (4 words) |
| Case study | 1 case, 3 elements | 6 words | Multiple cases, substories | Logo + "18 MONTHS" + "to discovery" (2 words) |
| Timeline | 3-4 points MAX | 8 words | Year-by-year detail | "2020 START" "2022 TRIAL" "2024 APPROVED" (6 words) |
Example - WRONG (7-stage workflow - TOO COMPLEX):
# ❌ BAD - This creates tiny unreadable text like the drug discovery poster
python scripts/generate_schematic.py "Drug discovery workflow showing: Stage 1 Target Identification, Stage 2 Molecular Synthesis, Stage 3 Virtual Screening, Stage 4 AI Lead Optimization, Stage 5 Clinical Trial Design, Stage 6 FDA Approval. Include success metrics, timelines, and validation steps for each stage." -o figures/workflow.png
# Result: 7+ stages with tiny text, unreadable from 6 feet - POSTER FAILURE
Example - CORRECT (simplified to 3 key stages):
# ✅ GOOD - Same content, split into ONE simple high-level graphic
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-box workflow: 'DISCOVER' → 'VALIDATE' → 'APPROVE'. Each word in GIANT bold (120pt+). Thick arrows (10px). 60% white space. NO substeps, NO details. 3 words total. Readable from 10 feet." -o figures/workflow_overview.png
# Result: Clean, impactful, readable - can add detail graphics separately if needed
Example - WRONG (complex case studies with multiple sections):
# ❌ BAD - Creates cramped unreadable sections
python scripts/generate_schematic.py "Case studies: Insilico Medicine (drug candidate, discovery time, clinical trials), Recursion Pharma (platform, methodology, results), Exscientia (drug candidates, FDA status, timeline). Include company logos, metrics, and outcomes." -o figures/cases.png
# Result: 3 case studies with 4+ elements each = 12+ total elements, tiny text
Example - CORRECT (one case study, one key metric):
# ✅ GOOD - Show ONE case with ONE key number
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case study card: Company logo (large), '18 MONTHS' in GIANT text (150pt), 'to discovery' below (60pt). 3 elements total: logo + number + caption. 50% white space. Readable from 10 feet." -o figures/case_single.png
# Result: Clear, readable, impactful. Make 3 separate graphics if you need 3 cases.
Example - WRONG (key findings too complex):
# BAD - too many items, too much detail
python scripts/generate_schematic.py "Key findings showing 8 metrics: accuracy 95%, precision 92%, recall 94%, F1 0.93, AUC 0.97, training time 2.3 hours, inference 50ms, model size 145MB with comparison to 5 baseline methods" -o figures/findings.png
# Result: Cramped graphic with tiny numbers
Example - CORRECT (key findings simple):
# GOOD - only 3 key items, giant numbers
python scripts/generate_schematic.py "POSTER FORMAT for A0. KEY FINDINGS with ONLY 3 large cards. Card 1: '95%' in GIANT text (120pt) with 'ACCURACY' below (48pt). Card 2: '2X' in GIANT text with 'FASTER' below. Card 3: checkmark icon with 'VALIDATED' in large text. 50% white space. High contrast colors. NO other text or details." -o figures/findings.png
# Result: Bold, readable impact statement
Font size reference for poster prompts:
| Element | Minimum Size | Prompt Keywords |
|---|---|---|
| Main numbers/metrics | 72pt+ | "huge", "very large", "giant", "poster-size" |
| Section titles | 60pt+ | "large bold", "prominent" |
| Labels/captions | 36pt+ | "readable from 6 feet", "clear labels" |
| Body text | 24pt+ | "poster-readable", "large text" |
Always include in prompts:
- "POSTER FORMAT" or "for A0 poster" or "readable from 6 feet"
- "VERY LARGE TEXT" or "huge bold fonts"
- Specific text that should appear (so it's baked into the image)
- "minimal text, maximum impact"
- "high contrast" for readability
- "generous margins" and "no text near edges"
CRITICAL: AI-Generated Graphic Sizing
⚠️ Each AI-generated graphic should focus on ONE concept with MINIMAL content.
Problem: Generating complex diagrams with many elements leads to small text.
Solution: Generate SIMPLE graphics with FEW elements and LARGE text.
Example - WRONG (too complex, text will be small):
# BAD - too many elements in one graphic
python scripts/generate_schematic.py "Complete ML pipeline showing data collection,
preprocessing with 5 steps, feature engineering with 8 techniques, model training
with hyperparameter tuning, validation with cross-validation, and deployment with
monitoring. Include all labels and descriptions." -o figures/pipeline.png
Example - CORRECT (simple, focused, large text):
# GOOD - split into multiple simple graphics with large text
# Graphic 1: High-level overview (3-4 elements max)
python scripts/generate_schematic.py "POSTER FORMAT for A0: Simple 4-step pipeline.
Four large boxes: DATA → PROCESS → MODEL → RESULTS.
GIANT labels (80pt+), thick arrows, lots of white space.
Only 4 words total. Readable from 8 feet." -o figures/overview.png
# Graphic 2: Key result (1 metric highlighted)
python scripts/generate_schematic.py "POSTER FORMAT for A0: Single key metric display.
Giant '95%' text (150pt+) with 'ACCURACY' below (60pt+).
Checkmark icon. Minimal design, high contrast.
Readable from 10 feet." -o figures/accuracy.png
Rules for AI-generated poster graphics:
| Rule | Limit | Reason |
|---|---|---|
| Elements per graphic | 3-5 maximum | More elements = smaller text |
| Words per graphic | 10-15 maximum | Minimal text = larger fonts |
| Flowchart steps | 4-5 maximum | Keeps labels readable |
| Chart categories | 3-4 maximum | Prevents crowding |
| Nested levels | 1-2 maximum | Avoids complexity |
Split complex content into multiple simple graphics:
Instead of 1 complex diagram with 12 elements:
→ Create 3 simple diagrams with 4 elements each
→ Each graphic can have LARGER text
→ Arrange in poster with clear visual flow
Step 0: MANDATORY Pre-Generation Review (DO THIS FIRST)
⚠️ BEFORE generating ANY graphics, review your content plan:
For EACH planned graphic, ask these questions:
-
Element count: Can I describe this in 3-4 items or less?
- ❌ NO → Simplify or split into multiple graphics
- ✅ YES → Continue
-
Complexity check: Is this a multi-stage workflow (5+ steps) or nested diagram?
- ❌ YES → Flatten to 3-4 high-level steps only
- ✅ NO → Continue
-
Word count: Can I describe all text in 10 words or less?
- ❌ NO → Cut text, use single-word labels
- ✅ YES → Continue
-
Message clarity: Does this graphic convey ONE clear message?
- ❌ NO → Split into multiple focused graphics
- ✅ YES → Continue to generation
Common patterns that ALWAYS fail (reject these):
- "Show stages 1 through 7..." → Split into high-level overview (3 stages) + detail graphics
- "Multiple case studies..." → One case per graphic
- "Timeline from 2015 to 2024 with annual milestones..." → Show only 3-4 key years
- "Comparison of 6 methods..." → Show only top 3 or Our method vs Best baseline
- "Architecture with all layers and connections..." → High-level only (3-4 components)
Step 1: Plan Your Poster Elements
After passing the pre-generation review, identify visual elements needed:
- Title Block - Stylized title with institutional branding (optional - can be LaTeX text)
- Introduction Graphic - Conceptual overview (3 elements max)
- Methods Diagram - High-level workflow (3-4 steps max)
- Results Figures - Key findings (3 metrics max per figure, may need 2-3 separate figures)
- Conclusion Graphic - Summary visual (3 takeaways max)
- Supplementary Icons - Simple icons, QR codes, logos (minimal)
Step 2: Generate Each Element (After Pre-Generation Review)
⚠️ CRITICAL: Review Step 0 checklist before proceeding.
Use the appropriate tool for each element type:
For Schematics and Diagrams (scientific-schematics):
# Create figures directory
mkdir -p figures
# Drug discovery workflow - HIGH-LEVEL ONLY, 3 stages
# BAD: "Stage 1: Target ID, Stage 2: Molecular Synthesis, Stage 3: Virtual Screening, Stage 4: AI Lead Opt..."
# GOOD: Collapse to 3 mega-stages
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-box workflow: 'DISCOVER' (120pt bold) → 'VALIDATE' (120pt bold) → 'APPROVE' (120pt bold). Thick arrows (10px). 60% white space. ONLY these 3 words. NO substeps. Readable from 12 feet." -o figures/workflow_simple.png
# System architecture - MAXIMUM 3 components
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-component stack: 'DATA' box (120pt) → 'AI MODEL' box (120pt) → 'PREDICTION' box (120pt). Thick vertical arrows. 60% white space. 3 words only. Readable from 12 feet." -o figures/architecture.png
# Timeline - ONLY 3 key milestones (not year-by-year)
# BAD: "2018, 2019, 2020, 2021, 2022, 2023, 2024 with events"
# GOOD: Only 3 breakthrough moments
python scripts/generate_schematic.py "POSTER FORMAT for A0. Timeline with ONLY 3 points: '2018' + icon, '2021' + icon, '2024' + icon. GIANT years (120pt). Large icons. 60% white space. NO connecting lines or details. Readable from 12 feet." -o figures/timeline.png
# Case study - ONE case, ONE key metric
# BAD: "3 case studies: Insilico (details), Recursion (details), Exscientia (details)"
# GOOD: ONE case with ONE number
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case study: Large logo + '18 MONTHS' (150pt bold) + 'to discovery' (60pt). 3 elements total. 60% white space. Readable from 12 feet." -o figures/case1.png
# If you need 3 cases → make 3 separate simple graphics (not one complex graphic)
For Stylized Blocks and Graphics (Nano Banana Pro):
# Title block - SIMPLE
python scripts/generate_schematic.py "POSTER FORMAT for A0. Title block: 'ML FOR DRUG DISCOVERY' in HUGE bold text (120pt+). Dark blue background. ONE subtle icon. NO other text. 40% white space. Readable from 15 feet." -o figures/title_block.png
# Introduction visual - SIMPLE, 3 elements only
python scripts/generate_schematic.py "POSTER FORMAT for A0. SIMPLE problem visual with ONLY 3 icons: drug icon, arrow, target icon. ONE label per icon (80pt+). 50% white space. NO detailed text. Readable from 8 feet." -o figures/intro_visual.png
# Conclusion/summary - ONLY 3 items, GIANT numbers
python scripts/generate_schematic.py "POSTER FORMAT for A0. KEY FINDINGS with EXACTLY 3 cards only. Card 1: '95%' (150pt font) with 'ACCURACY' (60pt). Card 2: '2X' (150pt) with 'FASTER' (60pt). Card 3: checkmark icon with 'READY' (60pt). 50% white space. NO other text. Readable from 10 feet." -o figures/conclusions_graphic.png
# Background visual - SIMPLE, 3 icons only
python scripts/generate_schematic.py "POSTER FORMAT for A0. SIMPLE visual with ONLY 3 large icons in a row: problem icon → challenge icon → impact icon. ONE word label each (80pt+). 50% white space. NO detailed text. Readable from 8 feet." -o figures/background_visual.png
For Data Visualizations - SIMPLE, 3 bars max:
# SIMPLE chart with ONLY 3 bars, GIANT labels
python scripts/generate_schematic.py "POSTER FORMAT for A0. SIMPLE bar chart with ONLY 3 bars: BASELINE (70%), EXISTING (85%), OURS (95%). GIANT percentage labels ON the bars (100pt+). NO axis labels, NO legend, NO gridlines. Our bar highlighted in different color. 40% white space. Readable from 8 feet." -o figures/comparison_chart.png
Step 2b: MANDATORY Post-Generation Review (Before Assembly)
⚠️ CRITICAL: Review EVERY generated graphic before adding to poster.
For each generated figure, open at 25% zoom and check:
-
✅ PASS criteria (all must be true):
- Can read ALL text clearly at 25% zoom
- Count elements: 3-4 or fewer
- White space: 50%+ of image is empty
- Simple enough to understand in 2 seconds
- NOT a complex workflow with 5+ stages
- NOT multiple nested sections
-
❌ FAIL criteria (regenerate if ANY are true):
- Text is small or hard to read at 25% zoom → REGENERATE with "150pt+" fonts
- More than 4 elements → REGENERATE with "ONLY 3 elements"
- Less than 50% white space → REGENERATE with "60% white space"
- Complex multi-stage workflow → SPLIT into 2-3 simple graphics
- Multiple case studies cramped together → SPLIT into separate graphics
- Takes more than 3 seconds to understand → SIMPLIFY and regenerate
Common failures and fixes:
- "7-stage workflow with tiny text" → Regenerate as "3 high-level stages only"
- "3 case studies in one graphic" → Generate 3 separate simple graphics
- "Timeline with 8 years" → Regenerate with "ONLY 3 key milestones"
- "Comparison of 5 methods" → Regenerate with "ONLY Our method vs Best baseline (2 bars)"
DO NOT PROCEED to assembly if ANY graphic fails the checks above.
Step 3: Assemble in LaTeX Template
After all figures pass the post-generation review, include them in your poster template:
tikzposter example:
\documentclass[25pt, a0paper, portrait]{tikzposter}
\begin{document}
\maketitle
\begin{columns}
\column{0.5}
\block{Introduction}{
\centering
\includegraphics[width=0.85\linewidth]{figures/intro_visual.png}
\vspace{0.5em}
Brief context text here (2-3 sentences max).
}
\block{Methods}{
\centering
\includegraphics[width=0.9\linewidth]{figures/methods_flowchart.png}
}
\column{0.5}
\block{Results}{
\begin{minipage}{0.48\linewidth}
\centering
\includegraphics[width=\linewidth]{figures/result_1.png}
\end{minipage}
\hfill
\begin{minipage}{0.48\linewidth}
\centering
\includegraphics[width=\linewidth]{figures/result_2.png}
\end{minipage}
\vspace{0.5em}
Key findings in 3-4 bullet points.
}
\block{Conclusions}{
\centering
\includegraphics[width=0.8\linewidth]{figures/conclusions_graphic.png}
}
\end{columns}
\end{document}
baposter example:
\headerbox{Methods}{name=methods,column=0,row=0}{
\centering
\includegraphics[width=0.95\linewidth]{figures/methods_flowchart.png}
}
\headerbox{Results}{name=results,column=1,row=0}{
\includegraphics[width=\linewidth]{figures/comparison_chart.png}
\vspace{0.3em}
Key finding: Our method achieves 92% accuracy.
}
Example: Complete Poster Generation Workflow
Full workflow with ALL quality checks:
# STEP 0: Pre-Generation Review (MANDATORY)
# Content plan: Drug discovery poster
# - Workflow: 7 stages → ❌ TOO MANY → Reduce to 3 mega-stages ✅
# - 3 case studies → ❌ TOO MANY → One case per graphic (make 3 graphics) ✅
# - Timeline 2018-2024 → ❌ TOO DETAILED → Only 3 key years ✅
# STEP 1: Create figures directory
mkdir -p figures
# STEP 2: Generate ULTRA-SIMPLE graphics with strict limits
# Workflow - HIGH-LEVEL ONLY (collapsed from 7 stages to 3)
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-box workflow: 'DISCOVER' → 'VALIDATE' → 'APPROVE'. Each word 120pt+ bold. Thick arrows (10px). 60% white space. ONLY 3 words total. Readable from 12 feet." -o figures/workflow.png
# Case study 1 - ONE case, ONE metric (will make 3 separate graphics)
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case: Company logo + '18 MONTHS' (150pt bold) + 'to drug discovery' (60pt). 3 elements only. 60% white space. Readable from 12 feet." -o figures/case1.png
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case: Company logo + '95% SUCCESS' (150pt bold) + 'in trials' (60pt). 3 elements only. 60% white space." -o figures/case2.png
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case: Company logo + 'FDA APPROVED' (150pt bold) + '2024' (60pt). 3 elements only. 60% white space." -o figures/case3.png
# Timeline - ONLY 3 key years (not 7 years)
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONLY 3 years: '2018' (150pt) + icon, '2021' (150pt) + icon, '2024' (150pt) + icon. Large icons. 60% white space. NO lines or details. Readable from 12 feet." -o figures/timeline.png
# Results - ONLY 2 bars (our method vs best baseline, not 5 methods)
python scripts/generate_schematic.py "POSTER FORMAT for A0. TWO bars only: 'BASELINE 70%' and 'OURS 95%' (highlighted). GIANT percentages (150pt) ON bars. NO axis, NO legend. 60% white space. Readable from 12 feet." -o figures/results.png
# STEP 2b: Post-Generation Review (MANDATORY)
# Open each figure at 25% zoom:
# ✅ workflow.png: 3 elements, text readable, 60% white - PASS
# ✅ case1.png: 3 elements, giant numbers, clean - PASS
# ✅ case2.png: 3 elements, giant numbers, clean - PASS
# ✅ case3.png: 3 elements, giant numbers, clean - PASS
# ✅ timeline.png: 3 elements, readable, simple - PASS
# ✅ results.png: 2 bars, giant percentages, clear - PASS
# ALL PASS → Proceed to assembly
# STEP 3: Compile LaTeX poster
pdflatex poster.tex
# STEP 4: PDF Overflow Check (see Section 11)
grep "Overfull" poster.log
# Open at 100% and check all 4 edges
If ANY graphic fails Step 2b review:
- Too many elements → Regenerate with "ONLY 3 elements"
- Small text → Regenerate with "150pt+" or "GIANT BOLD (150pt+)"
- Cluttered → Regenerate with "60% white space" and "ULTRA-SIMPLE"
- Complex workflow → SPLIT into multiple simple 3-element graphics
Visual Element Guidelines
⚠️ CRITICAL: Each graphic must have ONE message and MAXIMUM 3-4 elements.
ABSOLUTE LIMITS - These are NOT guidelines, these are HARD LIMITS:
- MAXIMUM 3-4 elements per graphic (3 is ideal)
- MAXIMUM 10 words total per graphic
- MINIMUM 50% white space (60% is better)
- MINIMUM 120pt for key numbers/metrics
- MINIMUM 80pt for labels
For each poster section - STRICT requirements:
| Section | Max Elements | Max Words | Example Prompt (REQUIRED PATTERN) |
|---|---|---|---|
| Introduction | 3 icons | 6 words | "POSTER FORMAT for A0: ULTRA-SIMPLE 3 icons: [icon1] [icon2] [icon3]. ONE WORD labels (100pt bold). 60% white space. 3 words total." |
| Methods | 3 boxes | 6 words |
How to use latex-posters 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 latex-posters
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches latex-posters from GitHub repository K-Dense-AI/scientific-agent-skills 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 latex-posters. Access the skill through slash commands (e.g., /latex-posters) 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.
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
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Ratings
4.7★★★★★70 reviews- ★★★★★Kofi Agarwal· Dec 16, 2024
latex-posters is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ira Choi· Dec 16, 2024
latex-posters reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kiara Srinivasan· Dec 12, 2024
latex-posters fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hassan Smith· Dec 12, 2024
latex-posters has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Daniel Martin· Dec 12, 2024
Registry listing for latex-posters matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Layla Khanna· Nov 7, 2024
Keeps context tight: latex-posters is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zaid Farah· Nov 7, 2024
We added latex-posters from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kaira Johnson· Nov 3, 2024
I recommend latex-posters for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Yusuf Bhatia· Nov 3, 2024
Solid pick for teams standardizing on skills: latex-posters is focused, and the summary matches what you get after install.
- ★★★★★Yusuf Desai· Oct 26, 2024
We added latex-posters from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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