{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-samuraigpt--generative-media-skills","slug":"samuraigpt--generative-media-skills","name":"Generative-Media-Skills","type":"skill","url":"https://github.com/SamurAIGPT/Generative-Media-Skills","page_url":"https://unfragile.ai/samuraigpt--generative-media-skills","categories":["app-builders","testing-quality"],"tags":["agent-tools","ai-agents","ai-art","ai-music","ai-video","claude-code","flux","generative-ai","image-generation","kling","mcp","midjourney","muapi","multimodal-ai","skills","suno","text-to-audio","text-to-image","text-to-video","video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-samuraigpt--generative-media-skills__cap_0","uri":"capability://image.visual.schema.driven.multi.model.image.generation.with.unified.api.abstraction","name":"schema-driven multi-model image generation with unified api abstraction","description":"Exposes a unified JSON Schema interface to 30+ image generation models (Midjourney v7, Flux Kontext, DALL-E 3, Stable Diffusion XL) through the muapi-cli wrapper layer. The system maps high-level generation requests to model-specific API calls via schema_data.json lookup tables, handling authentication, parameter normalization, and async polling for result retrieval without requiring developers to learn individual model APIs.","intents":["I want to generate images from text prompts using the best available model without managing multiple API keys and SDKs","I need to switch between image generation models (Midjourney to Flux) without rewriting my agent's tool-calling logic","I want my AI agent to generate product mockups, UI designs, and branded assets programmatically"],"best_for":["AI agents (Claude Code, Cursor, Gemini CLI) needing multi-model image generation","Teams building creative automation workflows that require model flexibility","Developers prototyping generative UI/UX tools without vendor lock-in"],"limitations":["Async polling adds 5-60 second latency depending on model and queue depth","No built-in image caching or deduplication — repeated prompts trigger new generations","Model availability depends on muapi.ai upstream service status","Parameter compatibility varies across models — some accept style/quality flags others don't"],"requires":["muapi-cli installed and in PATH","MUAPI_API_KEY environment variable configured","Node.js 16+ or shell environment for MCP server","Network connectivity to muapi.ai API endpoints"],"input_types":["text (prompt string)","structured JSON (generation parameters: model, style, quality, dimensions)"],"output_types":["image file (PNG/JPG)","structured JSON (generation metadata, model used, seed, timing)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_1","uri":"capability://image.visual.reasoning.driven.image.generation.with.domain.specific.skill.templates","name":"reasoning-driven image generation with domain-specific skill templates","description":"The Nano-Banana skill encodes professional design reasoning into optimized prompt templates and multi-step generation workflows. When an agent requests a logo, UI mockup, or portrait pack, the system decomposes the creative intent into structured parameters (brand guidelines, design principles, identity constraints), executes generation with reasoning-aware prompts, and applies post-processing rules specific to the domain (e.g., identity-lock for portrait consistency).","intents":["I want to generate a logo that matches my brand guidelines without manually writing complex design prompts","I need to create a consistent set of 10 portrait variations locked to a single identity for a character","I want my agent to generate high-fidelity UI mockups that follow atomic design principles"],"best_for":["Non-technical founders and product teams automating brand asset creation","Design agencies using AI to accelerate mockup and prototype generation","Game/animation studios generating character portrait packs with identity consistency"],"limitations":["Identity-lock portrait generation requires 3-5 seed iterations to achieve consistency, adding 2-3 minute latency","Domain-specific skills are pre-built for logos/UI/portraits — extending to new domains requires manual skill authoring","Reasoning quality depends on underlying model capability — weaker models may ignore design constraints","No feedback loop for iterative refinement — users must manually re-prompt for variations"],"requires":["muapi-cli with Flux Kontext or Midjourney v7 backend","MUAPI_API_KEY with sufficient quota","Optional: brand guidelines JSON (colors, fonts, style descriptors)"],"input_types":["text (creative brief, brand name, design direction)","structured JSON (brand guidelines, design constraints, identity reference image)"],"output_types":["image file (logo, UI mockup, or portrait pack)","structured JSON (generation parameters used, reasoning trace, consistency metrics)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_10","uri":"capability://data.processing.analysis.file.upload.and.asset.management.with.cloud.storage.integration","name":"file upload and asset management with cloud storage integration","description":"The platform utilities handle file uploads to muapi.ai cloud storage, managing authentication, chunked uploads for large files, and result file retrieval. The system supports reference image uploads (for style transfer, inpainting), source video uploads (for extension), and audio uploads (for voice cloning). Files are stored with expiration policies and accessed via signed URLs returned in generation results.","intents":["I want to upload a reference image for style transfer without managing cloud storage","I need to upload a source video for frame interpolation and extension","I want my agent to upload voice samples for voice cloning without manual file management"],"best_for":["Agents and workflows requiring reference assets (images, videos, audio)","Systems with limited local storage needing cloud-based asset management","Teams automating asset pipelines with external file sources"],"limitations":["File upload latency depends on file size and network bandwidth — 100MB video may take 30+ seconds","Uploaded files expire after 24-48 hours — long-running workflows may lose access to intermediate assets","No built-in file versioning or deduplication — uploading the same file multiple times creates duplicates","Signed URL expiration is fixed — no mechanism to extend access to expired files"],"requires":["muapi-cli with file upload support","MUAPI_API_KEY with upload quota","Network connectivity to muapi.ai upload endpoints"],"input_types":["file (image, video, audio in supported formats)"],"output_types":["structured JSON (file ID, signed URL, expiration timestamp)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_11","uri":"capability://automation.workflow.batch.generation.with.parallel.execution.and.result.aggregation","name":"batch generation with parallel execution and result aggregation","description":"The system supports batch generation of multiple media assets in parallel through async task submission and result polling. Agents submit a batch of generation requests (e.g., 10 image variations, 5 video clips), receive task IDs immediately, and poll for results asynchronously. The system aggregates results as they complete and returns a batch result object with per-item status and metadata.","intents":["I want to generate 100 product images with different backgrounds in parallel","I need to create multiple video variations of a scene with different camera angles","I want my agent to generate a batch of voiceovers in different languages simultaneously"],"best_for":["Content production teams generating large volumes of assets","E-commerce platforms automating product image generation at scale","Agents and workflows requiring multiple variations of the same asset"],"limitations":["Batch execution is limited by muapi.ai concurrent request limits — typically 5-10 parallel tasks","Result aggregation adds complexity — agents must handle partial failures and retry logic","No built-in deduplication — generating the same asset twice creates separate results","Batch size is not optimized — very large batches (100+ items) may timeout or exceed quota"],"requires":["muapi-cli with batch submission support","MUAPI_API_KEY with sufficient quota for all batch items","Batch definition in JSON array format"],"input_types":["structured JSON (array of generation requests)"],"output_types":["structured JSON (batch results with per-item status, task IDs, media files)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_2","uri":"capability://image.visual.cinematography.driven.video.generation.with.directorial.intent.encoding","name":"cinematography-driven video generation with directorial intent encoding","description":"The Cinema Director skill translates high-level cinematic direction (shot type, camera movement, mood, pacing) into optimized prompts for video generation models (Seedance 2.0, Kling 3.0). The system maps directorial concepts (e.g., 'Dutch angle establishing shot') to model-specific parameter sets, manages multi-shot composition, and handles async video rendering with progress polling and result validation.","intents":["I want to generate a cinematic video sequence from a script without learning video model APIs","I need to create product demo videos with specific camera movements and transitions","I want my agent to generate music video sequences that match a song's mood and pacing"],"best_for":["Content creators and filmmakers automating video asset generation","Marketing teams producing product demo and explainer videos at scale","Game studios generating cinematic cutscenes and in-engine cinematics"],"limitations":["Video generation latency is 30-120 seconds per clip depending on model and resolution","No built-in shot composition validation — poorly specified directorial intent may produce incoherent sequences","Multi-shot workflows require manual sequencing and transition logic — no automatic storyboard generation","Video quality and consistency varies significantly across models; Kling 3.0 excels at motion but struggles with text overlays"],"requires":["muapi-cli with Seedance 2.0 or Kling 3.0 backend","MUAPI_API_KEY with video generation quota","Minimum 2GB free disk space for video file storage"],"input_types":["text (scene description, directorial direction, mood/pacing descriptors)","structured JSON (shot parameters: type, duration, camera movement, aspect ratio)"],"output_types":["video file (MP4, WebM, variable resolution 720p-4K)","structured JSON (generation metadata, model used, duration, frame count)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_3","uri":"capability://image.visual.advanced.video.extension.and.frame.interpolation.with.temporal.coherence","name":"advanced video extension and frame interpolation with temporal coherence","description":"The Seedance 2 skill extends existing video clips by generating additional frames while maintaining temporal coherence and motion continuity. The system accepts a source video, target duration, and motion direction parameters, then uses Seedance 2.0's frame interpolation engine to synthesize intermediate frames that preserve object trajectories and scene consistency. Async polling monitors generation progress and validates output frame count and quality metrics.","intents":["I want to extend a 5-second video to 15 seconds without visible motion discontinuities","I need to slow down a video by interpolating frames while maintaining smooth motion","I want to generate additional frames for a video sequence to match a specific duration requirement"],"best_for":["Video editors and post-production teams extending footage without re-shooting","Content creators generating slow-motion effects from standard frame rate video","AI agents automating video duration normalization for streaming platforms"],"limitations":["Frame interpolation quality degrades with complex motion or occlusions — fast-moving objects may blur or ghost","Extension is limited to 2-3x original duration before temporal coherence breaks down","Requires source video in specific codec/resolution range — transcoding adds 5-10 second overhead","No motion direction control — system infers motion from source frames, may fail on static scenes"],"requires":["muapi-cli with Seedance 2.0 backend","MUAPI_API_KEY with video processing quota","Source video file (MP4, WebM, 720p minimum)"],"input_types":["video file (source clip)","structured JSON (target duration, frame rate, motion parameters)"],"output_types":["video file (extended clip with interpolated frames)","structured JSON (interpolation metrics, frame count, quality scores)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_4","uri":"capability://image.visual.text.to.audio.generation.with.voice.cloning.and.music.composition","name":"text-to-audio generation with voice cloning and music composition","description":"Integrates Suno AI and other text-to-audio models through muapi-cli to generate music, voiceovers, and sound effects from text descriptions. The system supports voice cloning (map text to specific speaker identity), style control (genre, mood, instrumentation), and async audio rendering with format conversion. Audio files are polled asynchronously and returned with metadata (duration, sample rate, codec).","intents":["I want to generate background music for a video that matches a specific mood and genre","I need to create voiceovers in multiple languages without hiring voice actors","I want my agent to generate podcast intros and sound effects programmatically"],"best_for":["Content creators automating voiceover and background music generation","Podcast and audiobook producers scaling production without studio overhead","Game developers generating dynamic audio assets and ambient soundscapes"],"limitations":["Audio generation latency is 20-60 seconds depending on duration and model","Voice cloning quality depends on reference audio quality — poor source audio produces robotic output","Music composition is generative and non-deterministic — same prompt produces different compositions","No real-time audio generation — all requests are async with polling overhead"],"requires":["muapi-cli with Suno or equivalent audio backend","MUAPI_API_KEY with audio generation quota","Optional: reference audio file for voice cloning (WAV, MP3, 16-bit 44.1kHz minimum)"],"input_types":["text (lyrics, voiceover script, music description)","structured JSON (voice parameters, style, duration, language)"],"output_types":["audio file (MP3, WAV, variable sample rates)","structured JSON (duration, sample rate, codec, generation metadata)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_5","uri":"capability://tool.use.integration.mcp.server.based.tool.exposure.with.json.schema.validation","name":"mcp server-based tool exposure with json schema validation","description":"Exposes 19 structured generation and editing tools through the Model Context Protocol (MCP) server interface. Running `muapi mcp serve` starts an MCP server that publishes JSON Schema definitions for each tool, enabling AI agents (Claude Code, Cursor, Gemini) to discover, validate, and call generation functions directly without shell script execution. The system handles schema validation, async polling orchestration, and result streaming back to the agent.","intents":["I want my AI agent to call image/video generation functions with full schema validation and type safety","I need to expose generation capabilities to Claude Code or Cursor without manual API integration","I want agents to discover available generation tools and their parameters automatically"],"best_for":["AI agent developers integrating Generative Media Skills into Claude Code, Cursor, or Gemini workflows","Teams building MCP-compatible agent frameworks","Developers automating creative workflows through agent tool calling"],"limitations":["MCP server adds ~100-200ms latency per tool call due to serialization and validation overhead","No built-in rate limiting or quota management — agents can exhaust API quota without safeguards","Schema validation is strict — agents must match exact parameter types or calls fail","Async polling results are not streamed — agents must wait for full completion before receiving output"],"requires":["muapi-cli installed and in PATH","MUAPI_API_KEY environment variable configured","MCP-compatible agent framework (Claude Code, Cursor, Gemini CLI)","Node.js 16+ for MCP server runtime"],"input_types":["structured JSON (tool parameters matching published JSON Schema)"],"output_types":["structured JSON (generation results, metadata, polling status)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_6","uri":"capability://automation.workflow.async.polling.and.result.retrieval.with.exponential.backoff","name":"async polling and result retrieval with exponential backoff","description":"Implements a robust async polling pattern for long-running media generation tasks. When a generation request is submitted, the system returns a task ID immediately and polls the muapi.ai backend at exponential backoff intervals (1s, 2s, 4s, 8s...) until the result is ready. The check-result.sh script handles polling orchestration, timeout management, and result validation, enabling agents to submit batch generation requests without blocking.","intents":["I want to submit multiple image/video generation requests and retrieve results asynchronously without blocking","I need to handle long-running video generation (60+ seconds) without connection timeouts","I want my agent to poll for results intelligently with exponential backoff to reduce API load"],"best_for":["Agents and workflows generating multiple media assets in parallel","Systems with strict timeout constraints (serverless functions, HTTP request limits)","High-volume generation pipelines requiring efficient resource utilization"],"limitations":["Polling adds 5-120 second latency depending on model and queue depth","Exponential backoff may miss result readiness by several seconds — no webhook/push notification support","No built-in result caching — polling the same task ID multiple times re-fetches from API","Timeout handling is configurable but defaults to 5 minutes — very long generations may timeout"],"requires":["muapi-cli with check-result.sh script","MUAPI_API_KEY for API access","Network connectivity to muapi.ai polling endpoints"],"input_types":["task ID (returned from initial generation request)","structured JSON (polling parameters: max_wait, backoff_multiplier)"],"output_types":["structured JSON (result status, media file path, generation metadata)","media file (image/video/audio when ready)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_7","uri":"capability://image.visual.prompt.based.image.editing.with.semantic.understanding","name":"prompt-based image editing with semantic understanding","description":"The edit-image.sh script enables semantic image editing through natural language prompts. Users describe desired edits (e.g., 'change the sky to sunset orange', 'remove the person from the background') and the system uses vision-language models to understand the edit intent, apply targeted modifications, and preserve unrelated image regions. Editing is performed through inpainting or outpainting depending on the edit scope.","intents":["I want to edit an image by describing changes in natural language without learning Photoshop","I need to remove unwanted objects from images programmatically","I want to extend image backgrounds or change specific regions while preserving the rest"],"best_for":["Content creators and marketers automating image post-processing","E-commerce teams removing product backgrounds and editing product photos at scale","Designers using AI to accelerate iterative design refinement"],"limitations":["Semantic understanding is imperfect — complex edits may require multiple iterations","Inpainting quality degrades with large edit regions — full image rewrites often look artificial","No layer-based editing — all edits are destructive and cannot be undone","Edit consistency across multiple images is not guaranteed — same prompt produces different results per image"],"requires":["muapi-cli with image editing backend","MUAPI_API_KEY with image editing quota","Source image file (PNG, JPG, 512x512 minimum)"],"input_types":["image file (source image)","text (edit description in natural language)"],"output_types":["image file (edited image)","structured JSON (edit metadata, regions modified, confidence scores)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_8","uri":"capability://automation.workflow.workflow.skill.composition.with.ai.architect.node.graphs","name":"workflow skill composition with ai architect node graphs","description":"The Workflow skill enables agents to compose complex multi-step generation pipelines as directed acyclic graphs (DAGs). Agents define nodes (generation tasks), edges (data flow), and execution parameters, then submit the workflow for orchestration. The system executes nodes in dependency order, handles intermediate result passing, and manages async polling across all nodes. Workflow results are aggregated and returned with execution traces.","intents":["I want to create a complex workflow: generate UI mockup → extend to video → add voiceover → compose music","I need to orchestrate multi-step creative pipelines without manual sequencing","I want my agent to compose reusable workflow templates for common creative tasks"],"best_for":["Agents and teams automating complex multi-step creative workflows","Content production studios with standardized creative pipelines","Developers building workflow automation platforms on top of Generative Media Skills"],"limitations":["DAG execution adds orchestration overhead — complex workflows may take 5-10 minutes total","No built-in error recovery — single node failure halts entire workflow","Intermediate result passing requires manual format conversion — no automatic type coercion","Workflow composition is code-based — no visual workflow builder UI"],"requires":["muapi-cli with workflow orchestration support","MUAPI_API_KEY with sufficient quota for all workflow steps","Workflow definition in JSON DAG format"],"input_types":["structured JSON (workflow DAG: nodes, edges, parameters)"],"output_types":["structured JSON (workflow results, execution trace, timing per node)","media files (all generated assets from workflow nodes)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--generative-media-skills__cap_9","uri":"capability://tool.use.integration.multi.provider.function.calling.with.native.api.bindings","name":"multi-provider function calling with native api bindings","description":"The system abstracts function calling across multiple AI model providers (OpenAI, Anthropic, Ollama) through a unified schema-based registry. Each generation tool is registered with JSON Schema definitions that are compatible with OpenAI function calling, Anthropic tool_use, and Ollama native bindings. The system automatically translates between provider-specific function calling formats and executes the underlying muapi-cli commands.","intents":["I want to use the same generation tools across Claude, GPT-4, and local Ollama models","I need to switch AI providers without rewriting my agent's tool-calling logic","I want my agent to call generation functions with full schema validation across all providers"],"best_for":["Multi-model agent frameworks supporting Claude, GPT-4, and local LLMs","Teams evaluating different AI providers without vendor lock-in","Developers building provider-agnostic agent tooling"],"limitations":["Schema translation adds ~50-100ms latency per function call","Provider-specific features (e.g., OpenAI parallel function calling) are not exposed","Function calling reliability varies by provider — weaker models may fail to invoke tools correctly","No automatic fallback if primary provider fails — requires manual provider switching"],"requires":["muapi-cli installed","MUAPI_API_KEY configured","API keys for target providers (OpenAI, Anthropic, Ollama endpoint)"],"input_types":["structured JSON (function call parameters matching JSON Schema)"],"output_types":["structured JSON (function results, execution metadata)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["muapi-cli installed and in PATH","MUAPI_API_KEY environment variable configured","Node.js 16+ or shell environment for MCP server","Network connectivity to muapi.ai API endpoints","muapi-cli with Flux Kontext or Midjourney v7 backend","MUAPI_API_KEY with sufficient quota","Optional: brand guidelines JSON (colors, fonts, style descriptors)","muapi-cli with file upload support","MUAPI_API_KEY with upload quota","Network connectivity to muapi.ai upload endpoints"],"failure_modes":["Async polling adds 5-60 second latency depending on model and queue depth","No built-in image caching or deduplication — repeated prompts trigger new generations","Model availability depends on muapi.ai upstream service status","Parameter compatibility varies across models — some accept style/quality flags others don't","Identity-lock portrait generation requires 3-5 seed iterations to achieve consistency, adding 2-3 minute latency","Domain-specific skills are pre-built for logos/UI/portraits — extending to new domains requires manual skill authoring","Reasoning quality depends on underlying model capability — weaker models may ignore design constraints","No feedback loop for iterative refinement — users must manually re-prompt for variations","File upload latency depends on file size and network bandwidth — 100MB video may take 30+ seconds","Uploaded files expire after 24-48 hours — long-running workflows may lose access to intermediate assets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2906555496411575,"quality":0.49,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2026-05-02T18:35:59Z"},"community":{"stars":3164,"forks":347,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=samuraigpt--generative-media-skills","compare_url":"https://unfragile.ai/compare?artifact=samuraigpt--generative-media-skills"}},"signature":"CMMr8WuqfTcFqPlfcBuBWvjPjNWD7yPe9o7a920apmggB1uYKcgDJhrBPYkk3ebbY1msDvGKbysi0zv9uhNSDQ==","signedAt":"2026-06-23T05:32:43.906Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/samuraigpt--generative-media-skills","artifact":"https://unfragile.ai/samuraigpt--generative-media-skills","verify":"https://unfragile.ai/api/v1/verify?slug=samuraigpt--generative-media-skills","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}