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github/nano-banana-pro-openrouter

Generieren oder Bearbeiten von Bildern über OpenRouter mit dem Gemini 3 Pro Image Modell. Geeignet für promptbasierte Bildgenerierung, Bildbearbeitung und Multi-Bild-Compositing; unterstützt 1K/2K/4K Ausgabe.

Was ist nano-banana-pro-openrouter?

github/nano-banana-pro-openrouter ist eine KI-Agenten-Fähigkeit zur Bildgenerierung und -bearbeitung über OpenRouter, die das leistungsstarke Gemini 3 Pro Image Modell nutzt. Diese Fähigkeit ermöglicht es Entwicklern, Bilder aus Textbeschreibungen (Prompt-only), Bildbearbeitungen und Multi-Image-Compositing durchzuführen, ohne ein separates visuelles Interface verwenden zu müssen. Sie unterstützt Ausgabeauflösungen von 1K, 2K und 4K, was sie für verschiedene Anwendungen wie Content-Erstellung, Prototyping und Integration in automatisierte Workflows geeignet macht. Die Fähigkeit ist besonders nützlich für Entwickler, die mit KI-gestützten Tools wie Claude Code, Cursor oder Codex arbeiten und Bildbearbeitungs- oder Generierungsaufgaben direkt in die Befehlszeile oder Agenten-Workflow integrieren möchten. Die Implementierung erfolgt über die OpenRouter-API, die eine zuverlässige und skalierbare Verbindung zu den Gemini 3 Pro Image-Modellen bietet.

Funktioniert mit~Claude Code~Codex CLI~Cursor✓Gemini CLI
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/nano-banana-pro-openrouter

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Dokumentation

Was macht nano-banana-pro-openrouter?

Generieren oder Bearbeiten von Bildern über OpenRouter mit dem Gemini 3 Pro Image Modell. Geeignet für promptbasierte Bildgenerierung, Bildbearbeitung und Multi-Bild-Compositing; unterstützt 1K/2K/4K Ausgabe.

Funktionsweise

Diese Fähigkeit verwendet das Gemini 3 Pro Image Modell, um Bilder basierend auf Textbeschreibungen zu generieren oder bestehende Bilder zu bearbeiten. Sie ist als experimentelle Fähigkeit für KI-Agenten konzipiert, die in Entwicklungsumgebungen wie Claude Code, Cursor oder Codex ausgeführt werden.

Voraussetzungen

  • OpenRouter API Key (setzen Sie die Umgebungsvariable OPENROUTER_API_KEY)
  • Internetverbindung für API-Aufrufe

Verwendung

Bildgenerierung aus Prompt

Erzeugen Sie ein neues Bild basierend auf einer Textbeschreibung:

skill invoke github/nano-banana-pro-openrouter generate "eine Katze auf einer Wolke im Sonnenuntergang"

Bildbearbeitung

Bearbeiten Sie ein vorhandenes Bild mit einem Prompt:

skill invoke github/nano-banana-pro-openrouter edit path/to/image.jpg "Füge eine Regenbogenbrücke hinzu"

Multi-Bild-Compositing (experimentell)

Kombinieren Sie mehrere Bilder zu einem einzelnen Ausgabebild:

skill invoke github/nano-banana-pro-openrouter composite image1.jpg image2.jpg image3.jpg --output combined.png

Unterstützte Auflösungen

  • 1K (1024x1024) – Standard
  • 2K (2048x2048) – Höhere Details
  • 4K (4096x4096) – Maximale Qualität

Geben Sie die Auflösung bei Bedarf mit dem Flag --resolution an:

skill invoke github/nano-banana-pro-openrouter generate "Landschaftsmalerei" --resolution 2K

Wichtige Hinweise

  • Diese Fähigkeit ist experimentell und kann je nach Verfügbarkeit des Modells und API-Limitierungen variieren.
  • Ausschließlich über die OpenRouter-Plattform verfügbar.
  • Stellen Sie sicher, dass Sie über ausreichende API-Kredite verfügen.

Lizenz

MIT

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

Verwandte Skills

steipete/songsee

Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.

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affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

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mattpocock/improve-codebase-architecture

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

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lobehub/add-provider-doc

Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.

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addyosmani/browser-testing-with-devtools

Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.

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bytedance/chart-visualization

This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.

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