alterlab-infographics

Name: alterlab-infographics
Author: AlterLab-IEU/AlterLab-Academic-Skills

$npx mdskill add AlterLab-IEU/AlterLab-Academic-Skills/alterlab-infographics

Creates professional infographics with AI-driven design and automated quality review.

Solves the need for polished infographics without manual design skills.
Depends on OpenRouter API key for Nano Banana Pro and Gemini 3 Pro.
Uses Gemini 3 Pro for automated quality review and optional Perplexity Sonar research.
Delivers a ready-to-use infographic image via Nano Banana Pro generation.

SKILL.md

.github/skills/alterlab-infographicsView on GitHub ↗

---
name: alterlab-infographics
description: "Creates professional infographics with Nano Banana Pro AI and smart iterative refinement, using Gemini 3 Pro for automated quality review and an optional Perplexity Sonar research phase for accurate, sourced data — supports 10 infographic types, 8 industry styles, and colorblind-safe palettes. Use when the request is for an infographic, data-story graphic, statistical poster, comparison chart, timeline, process/how-to visual, or list/social graphic that pairs a designed layout with figures. Use alterlab-scientific-schematics instead for technical flowcharts, CONSORT/PRISMA, pathways, or architecture diagrams; alterlab-generate-image for non-infographic illustrations. Part of the AlterLab Academic Skills suite."
license: MIT
metadata:
    skill-author: AlterLab
    version: "1.0.0"
allowed-tools: Read Write Edit Bash
compatibility: Requires an OpenRouter API key (OPENROUTER_API_KEY) for Nano Banana Pro generation and Gemini 3 Pro quality review
---

# Infographics

## Overview

Infographics are visual representations of information, data, or knowledge designed to present complex content quickly and clearly. **This skill uses Nano Banana Pro AI for infographic generation with Gemini 3 Pro quality review and Perplexity Sonar for research.**

**How it works:**
- (Optional) **Research phase**: Gather accurate facts and statistics using Perplexity Sonar
- Describe your infographic in natural language
- Nano Banana Pro generates publication-quality infographics automatically
- **Gemini 3 Pro reviews quality** against document-type thresholds
- **Smart iteration**: Only regenerates if quality is below threshold
- Professional-ready output in minutes; no design skills required

**Quality Thresholds by Document Type:**
| Document Type | Threshold | Description |
|---------------|-----------|-------------|
| marketing | 8.5/10 | Marketing materials - must be compelling |
| report | 8.0/10 | Business reports - professional quality |
| presentation | 7.5/10 | Slides, talks - clear and engaging |
| social | 7.0/10 | Social media content |
| internal | 7.0/10 | Internal use |
| draft | 6.5/10 | Working drafts |
| default | 7.5/10 | General purpose |

## When to Use This Skill

Use the **infographics** skill when:
- Presenting data or statistics in a visual format
- Creating timeline visualizations for project milestones or history
- Explaining processes, workflows, or step-by-step guides
- Comparing options, products, or concepts side-by-side
- Summarizing key points in an engaging visual format
- Creating geographic or map-based data visualizations
- Building hierarchical or organizational charts
- Designing social media content or marketing materials

**Use scientific-schematics instead for:**
- Technical flowcharts and circuit diagrams
- Biological pathways and molecular diagrams
- Neural network architecture diagrams
- CONSORT/PRISMA methodology diagrams

## Quick Start

Generate any infographic by describing it; the script handles generation, review, and iteration.

```bash
# Generate a list infographic (default threshold 7.5/10)
python scripts/generate_infographic.py \
  "5 benefits of regular exercise" \
  -o figures/exercise_benefits.png --type list

# Marketing (highest threshold: 8.5/10)
python scripts/generate_infographic.py \
  "Product features comparison" \
  -o figures/product_comparison.png --type comparison --doc-type marketing

# Corporate style + colorblind-safe palette
python scripts/generate_infographic.py \
  "Company milestones 2010-2025" \
  -o figures/timeline.png --type timeline --style corporate

# WITH RESEARCH for accurate, up-to-date data
python scripts/generate_infographic.py \
  "Global AI market size and growth projections" \
  -o figures/ai_market.png --type statistical --research
```

**What happens behind the scenes:**
1. **(Optional) Research**: Perplexity Sonar gathers accurate facts, statistics, and data
2. **Generation 1**: Nano Banana Pro creates the initial infographic
3. **Review 1**: **Gemini 3 Pro** evaluates quality against the document-type threshold
4. **Decision**: quality >= threshold → **DONE** (early stop); below threshold → improve prompt and regenerate
5. **Repeat** until quality meets threshold OR max iterations reached

**Output**: Versioned images plus a detailed JSON review log with quality scores, critiques, and early-stop info.

## Core Workflow

1. **Pick the type** (`--type`) — one of 10 presets (statistical, timeline, process, comparison, list, geographic, hierarchical, anatomical, resume, social). Full catalog with examples in `references/type_catalog.md`.
2. **Pick a style and (optionally) palette** — 8 industry `--style` presets, 3 colorblind-safe `--palette` presets. Tables in `references/type_catalog.md`.
3. **Set `--doc-type`** to choose the quality threshold (see table above).
4. **Add `--research`** when accurate sourced data matters.
5. **Configure the API key** (`OPENROUTER_API_KEY`) and run; review the JSON log and regenerate with a more specific prompt if needed.

## Configuration

Set the OpenRouter API key (read from the environment):

```bash
export OPENROUTER_API_KEY='your_api_key_here'   # https://openrouter.ai/keys
```

**Data & privacy:** prompts and research/infographic content are sent to a third-party API (OpenRouter) for generation and quality review. Avoid sending confidential, clinical, or unpublished material.

## Reference Index

| File | Contents |
| ---- | -------- |
| [`references/type_catalog.md`](references/type_catalog.md) | All 10 `--type` presets with runnable examples; `--style` and `--palette` tables |
| [`references/cli_and_workflow.md`](references/cli_and_workflow.md) | Full CLI options, smart-refinement loop + Gemini review criteria + review-log schema, `--research` integration, prompt-engineering tips, troubleshooting, cross-skill integration, checklist |
| [`references/design_principles.md`](references/design_principles.md) | Visual hierarchy, layout, typography |
| [`references/color_palettes.md`](references/color_palettes.md) | Full palette specifications |
| [`references/infographic_types.md`](references/infographic_types.md) | Extended layout templates for all types |

Use this skill to create professional, accessible, and visually compelling infographics using Nano Banana Pro AI with intelligent Gemini 3 Pro quality review.

More from AlterLab-IEU/AlterLab-Academic-Skills

Skill	Description
alterlab-abm-mesa	Builds agent-based models of social systems with Mesa 3 — the current AgentSet API (model.agents.shuffle_do('step'), auto-assigned unique_id, mandatory super().__init__(seed=...)), cell spaces (mesa.discrete_space OrthogonalMooreGrid / classic mesa.space grids), the DataCollector, batch_run parameter sweeps, and SolaraViz — for emergence, segregation, diffusion, opinion dynamics, and cooperation models. It uses the Mesa 3.x API (the old mesa.time schedulers like RandomActivation are removed) and treats the model as a generative theory to be validated, not just run. Use when the request mentions an agent-based model, Mesa, simulating interacting agents, or emergent macro behavior from micro rules. For discrete-event (queueing/process) simulation prefer alterlab-simpy; for reinforcement learning prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.
alterlab-academic-career	Prepares academic career documents and professional-development materials for faculty and researchers. Use when drafting academic CVs, research statements, teaching philosophies, diversity statements, faculty-position cover letters, tenure dossiers, promotion narratives, academic portfolios, or mentorship statements, and when planning conference-networking strategy, building academic web presence, setting up ORCID profiles, or understanding impact metrics. Part of the AlterLab Academic Skills suite.
alterlab-accreditation-aol	Scaffolds program-level Assurance-of-Learning (AoL) documentation for AACSB (2020 Standard 5) and ABET (Criterion 3 Student Outcomes, Criterion 4 Continuous Improvement) accreditation — program learning outcomes / competency goals, curriculum-to-outcome mapping matrices, direct- and indirect-assessment plans, rubric design, and closing-the-loop continuous-improvement narratives — and validates the structure of an outcome-mapping matrix with scripts/aol_matrix.py. Use when the user needs AACSB or ABET assurance-of-learning material, a program-learning-outcomes set, a curriculum/outcome map or coverage matrix, a direct/indirect assessment plan, a closing-the-loop report, or accreditation self-study text. For single-course design or course rubrics prefer alterlab-teaching-design; for post-award grant reports prefer alterlab-grant-reporting. Part of the AlterLab Academic Skills suite.
alterlab-adaptyv	Submits and tracks protein-testing experiments on the Adaptyv Bio Foundry cloud lab (wet-lab validation), and optimizes protein sequences before submission with computational tools (NetSolP, SoluProt, SolubleMPNN, ESM). Use when designing proteins that need wet-lab validation - binding/affinity screening, expression testing, thermostability, or fluorescence assays - or when submitting experiments to the Foundry API, browsing the target catalog, tracking experiment status, retrieving results, or pre-screening sequences for solubility/expression. Triggers on "Adaptyv", "Foundry API", "cloud lab", "biolayer interferometry / BLI", "wet-lab validation". Part of the AlterLab Academic Skills suite.
alterlab-aeon	Runs time series machine learning with the aeon library — classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search via scikit-learn compatible APIs. Use when working with temporal data, sequential patterns, or time-indexed observations (univariate or multivariate) that need specialized algorithms beyond standard ML approaches. Part of the AlterLab Academic Skills suite.
alterlab-akademik-tesvik	Computes the annual Turkish academic-incentive (akademik teşvik) score for Devlet (state) university öğretim elemanı (academic staff) from a classified activity list, applying the verified Akademik Teşvik Ödeneği Yönetmeliği (2018/11834) Faaliyet Hesaplama Tablosu headline puanları (Proje 20, Araştırma 15, Yayın 30, Patent 30, Atıf 30, Tebliğ 20, Ödül 20) with the k (author-count), p (Q1 1/Q2 0.8/Q3 0.5/Q4 0.25), and r (project-role) coefficients via türü puanı = Σ(faaliyet oranları) × headline (MADDE 8/2), then enforcing the per-type headline ceilings, the 100 cap (MADDE 8/3), and the net-30 payment gate (MADDE 10/3) in a deterministic scripts/tesvik_score.py. Use when the user wants to calculate akademik teşvik puanı, check the en az 30 (net-30) threshold, apply TABLO 4 ceilings, or estimate teşvik eligibility; keeps its coefficients separate from doçentlik scoring. For doçentlik point eligibility prefer alterlab-docentlik-eligibility. Part of the AlterLab Academic Skills suite.
alterlab-alpha-vantage	Accesses real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API (requires a free API key from alphavantage.co). Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Part of the AlterLab Academic Skills suite.
alterlab-alphafold	Predict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA, generating a predicted structure with confidence metrics, ranking models, or checking self-consistency of a design. For co-folding a protein WITH a small-molecule ligand or predicting binding affinity prefer alterlab-boltz; for antibody–antigen or one-FASTA multi-entity complexes prefer alterlab-chai; to LOOK UP an already-computed structure prefer alterlab-alphafold-db; for ESM embeddings or inverse folding prefer alterlab-esm. Part of the AlterLab Academic Skills suite.
alterlab-alphafold-db	Access the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.
alterlab-anndata	Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the QC, normalization, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for RNA velocity from spliced/unspliced layers prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.