### Latex Posters
Works with
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"
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlatex-postersExecute the skills CLI command in your project's root directory to begin installation:
Fetches latex-posters from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate latex-posters. Access via /latex-posters in your agent's command palette.
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| 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" |
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.
This skill should be used when:
STANDARD WORKFLOW: Generate ALL major visual elements using AI before creating the LaTeX poster.
This is the recommended approach for creating visually compelling posters:
Target: 60-70% of poster area should be AI-generated visuals, 30-40% text.
⚠️ POSTERS MUST NOT HAVE TEXT OR CONTENT CUT OFF AT EDGES.
Common Overflow Problems:
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:
⚠️ 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:
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
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:
⚠️ 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
⚠️ 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?
Complexity check: Is this a multi-stage workflow (5+ steps) or nested diagram?
Word count: Can I describe all text in 10 words or less?
Message clarity: Does this graphic convey ONE clear message?
Common patterns that ALWAYS fail (reject these):
After passing the pre-generation review, identify visual elements needed:
⚠️ 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
⚠️ 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):
❌ FAIL criteria (regenerate if ANY are true):
Common failures and fixes:
DO NOT PROCEED to assembly if ANY graphic fails the checks above.
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.
}
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:
⚠️ CRITICAL: Each graphic must have ONE message and MAXIMUM 3-4 elements.
ABSOLUTE LIMITS - These are NOT guidelines, these are HARD LIMITS:
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 |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
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💡 Pro Tips
✓ 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.
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latex-posters is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
latex-posters reduced setup friction for our internal harness; good balance of opinion and flexibility.
latex-posters fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
latex-posters has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for latex-posters matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: latex-posters is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added latex-posters from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend latex-posters for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: latex-posters is focused, and the summary matches what you get after install.
We added latex-posters from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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