{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lykosai--stabilitymatrix","slug":"lykosai--stabilitymatrix","name":"StabilityMatrix","type":"cli","url":"https://lykos.ai","page_url":"https://unfragile.ai/lykosai--stabilitymatrix","categories":["image-generation"],"tags":["ai","automatic1111","avalonia","comfyui","dotnet","python","sd-webui","stable-diffusion","text2image"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lykosai--stabilitymatrix__cap_0","uri":"capability://automation.workflow.multi.package.installation.and.lifecycle.management","name":"multi-package installation and lifecycle management","description":"Manages installation, updates, and execution of 10+ Stable Diffusion UI packages (ComfyUI, AUTOMATIC1111, InvokeAI, Fooocus, etc.) through a polymorphic BasePackage architecture with Git-based version control. Each package type (BaseGitPackage, BasePackage subclasses) implements platform-specific installation logic, dependency resolution, and launch configurations. The system handles package discovery, version tracking, and isolated execution environments per package instance.","intents":["Install multiple Stable Diffusion UIs without manual Git cloning and dependency management","Switch between different SD packages (ComfyUI vs AUTOMATIC1111) without environment conflicts","Update packages to latest versions while preserving model and configuration state","Launch packages with correct Python environments and hardware-specific PyTorch builds"],"best_for":["AI artists and researchers experimenting with multiple SD implementations","Teams managing shared infrastructure for image generation workloads","Developers building SD-based applications who need reproducible package environments"],"limitations":["Package polymorphism adds abstraction overhead — each new package type requires BasePackage subclass implementation","Git-based updates can be slow on poor connections; no delta/incremental update support","Shared model folder symlinks may fail on Windows with non-admin permissions","No built-in package rollback — requires manual Git history navigation"],"requires":["Windows 10+, macOS 10.13+, or Linux with glibc 2.29+","Git installed and in PATH","Python 3.8+ (for target packages)","Administrator/sudo access for virtual environment creation"],"input_types":["package selection (enum)","installation path (directory)","version specifier (git tag/branch)"],"output_types":["installed package directory structure","launch configuration (JSON)","Python venv with dependencies"],"categories":["automation-workflow","package-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_1","uri":"capability://automation.workflow.hardware.aware.python.virtual.environment.provisioning","name":"hardware-aware python virtual environment provisioning","description":"Detects GPU hardware (NVIDIA CUDA, AMD ROCm, Intel Arc, Apple Metal) and automatically provisions Python virtual environments with matching PyTorch builds and CUDA/ROCm toolchain versions. Implements platform prerequisite detection (CUDA 11.8/12.1 availability, cuDNN versions) and selects optimal PyTorch wheel variants (CPU, CUDA 11.8, CUDA 12.1, ROCm 5.7, etc.) based on detected hardware. Uses Python subprocess isolation and venv module for environment creation.","intents":["Automatically install correct PyTorch variant without manual CUDA/ROCm version matching","Detect GPU capabilities and warn users if hardware doesn't meet package requirements","Provision separate venvs per package to avoid PyTorch version conflicts","Handle Windows DirectML and Apple Metal GPU acceleration without user configuration"],"best_for":["Non-technical users running SD packages on diverse hardware (gaming GPUs, workstations, laptops)","Teams managing heterogeneous hardware pools with different GPU generations","Developers building SD applications who need reproducible GPU-accelerated environments"],"limitations":["Hardware detection relies on nvidia-smi, rocm-smi, or system APIs — may fail in containerized/WSL environments without GPU passthrough","PyTorch wheel selection is static per detected GPU type — no runtime fallback if wheel installation fails","CUDA/cuDNN prerequisite detection is heuristic-based (registry checks on Windows, LD_LIBRARY_PATH on Linux) and may miss non-standard installations","No support for multi-GPU load balancing or distributed inference setup"],"requires":["Python 3.8+ installed system-wide","NVIDIA CUDA 11.8 or 12.1 (for NVIDIA GPUs) OR AMD ROCm 5.7+ (for AMD GPUs) OR Apple Silicon with macOS 12+","pip and venv modules available in Python installation","Write permissions to package installation directory"],"input_types":["package type (determines PyTorch version requirements)","installation directory path"],"output_types":["Python venv directory with PyTorch installed","hardware capability report (GPU model, VRAM, supported precision)","launch script with environment variables (CUDA_VISIBLE_DEVICES, etc.)"],"categories":["automation-workflow","dependency-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_10","uri":"capability://data.processing.analysis.local.model.folder.organization.and.symlink.management","name":"local model folder organization and symlink management","description":"Organizes downloaded models into package-specific folders (models/Stable-diffusion, models/Lora, models/VAE, etc.) with automatic subdirectory creation. Implements symlink strategy to share models across multiple package instances without duplication (e.g., symlink models/Stable-diffusion → shared-models/Stable-diffusion). Handles platform-specific symlink creation (Windows junction points vs Unix symlinks) and validates symlink integrity on startup.","intents":["Organize downloaded models into logical folders by type without manual folder creation","Share models across multiple SD packages without duplicating multi-GB files","Validate model folder integrity and repair broken symlinks on startup","Support both single-package and multi-package model sharing strategies"],"best_for":["Users running multiple SD packages (ComfyUI + AUTOMATIC1111) with large model libraries","Teams managing shared model storage across multiple machines","Developers building SD applications with model folder organization requirements"],"limitations":["Symlinks may fail on Windows without administrator privileges or with certain filesystems (FAT32)","Symlink validation is one-time on startup — broken symlinks created after startup are not detected","Model folder organization is package-specific — no unified model browser across packages","No built-in model deduplication (e.g., detecting duplicate models with different names)","Symlink repair requires manual intervention if target folder is moved/deleted"],"requires":["Administrator/sudo access for symlink creation on Windows","Filesystem supporting symlinks (NTFS on Windows, ext4 on Linux, APFS on macOS)","Sufficient disk space for model files (or shared storage for symlink targets)"],"input_types":["model file (downloaded from CivitAI or manual upload)","model type (checkpoint, LoRA, VAE, embedding)","package instance (determines target folder)"],"output_types":["organized model folder structure","symlink to shared model storage","model metadata JSON (name, type, size)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_11","uri":"capability://automation.workflow.package.launch.configuration.and.environment.variable.injection","name":"package launch configuration and environment variable injection","description":"Generates platform-specific launch scripts (batch files on Windows, shell scripts on Linux/macOS) with environment variable injection for GPU acceleration, Python paths, and package-specific settings. Implements launch configuration templates per package type (ComfyUI requires specific port configuration, AUTOMATIC1111 requires specific API flags, etc.). Executes launch scripts in isolated subprocess with real-time output streaming to UI.","intents":["Launch installed packages with correct environment variables and configuration flags","Stream package output (logs, errors) to UI in real-time for debugging","Support package-specific launch configurations without manual script editing","Handle package startup failures with error messages and recovery suggestions"],"best_for":["Users launching SD packages without manual command-line configuration","Developers debugging package startup issues with real-time log streaming","Teams managing package deployments with standardized launch configurations"],"limitations":["Launch script generation is package-specific — new packages require template updates","Environment variable injection is static — no runtime configuration changes","Output streaming is text-based only — no structured logging or log filtering","Subprocess isolation may prevent some package features (e.g., interactive CLI prompts)","No built-in process monitoring or auto-restart on crash"],"requires":["Installed Python virtual environment with package dependencies","Package-specific launch configuration template","Write permissions to package installation directory for script generation"],"input_types":["package type (determines launch template)","environment variables (GPU device, port, etc.)","launch flags (package-specific options)"],"output_types":["launch script (batch/shell file)","subprocess output stream (logs, errors)","process exit code and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_12","uri":"capability://automation.workflow.dependency.resolution.and.platform.prerequisite.validation","name":"dependency resolution and platform prerequisite validation","description":"Validates platform prerequisites (Python version, CUDA/ROCm availability, Git installation) before package installation and provides remediation guidance. Implements prerequisite detection via system API calls (registry on Windows, environment variables on Linux, system frameworks on macOS). Generates installation guides for missing prerequisites (e.g., 'Download CUDA 12.1 from nvidia.com'). Supports multiple Python versions and validates compatibility with package requirements.","intents":["Detect missing platform prerequisites before package installation fails","Provide clear remediation guidance for missing dependencies","Validate Python version compatibility with package requirements","Support multiple Python installations and version selection"],"best_for":["Non-technical users installing SD packages without manual prerequisite checking","Teams managing heterogeneous hardware with different prerequisite configurations","Developers building SD applications with automated prerequisite validation"],"limitations":["Prerequisite detection is heuristic-based — may miss non-standard installations","Remediation guidance is text-based only — no automated prerequisite installation","Python version detection relies on PATH and registry — may fail with non-standard installations","No support for containerized or WSL environments with special prerequisite handling","Prerequisite validation is one-time at installation — runtime changes are not detected"],"requires":["System API access for prerequisite detection (registry on Windows, environment variables on Linux)","Internet connection for remediation guidance (optional, can be offline)"],"input_types":["package type (determines prerequisite requirements)","target platform (Windows, macOS, Linux)"],"output_types":["prerequisite validation report (pass/fail per prerequisite)","remediation guidance (installation instructions, download links)","Python version compatibility report"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_2","uri":"capability://search.retrieval.civitai.model.browser.and.discovery.integration","name":"civitai model browser and discovery integration","description":"Integrates CivitAI API for browsing, searching, and filtering 100k+ community-trained Stable Diffusion models (checkpoints, LoRAs, VAEs, embeddings) with metadata caching and local model import. Implements paginated API queries with filtering by model type, base model version, and rating. Downloaded models are automatically organized into local model folders (models/Stable-diffusion, models/Lora, etc.) with metadata JSON for UI display. Supports direct model download from CivitAI URLs with progress tracking.","intents":["Browse and search CivitAI model catalog without leaving the application","Filter models by type (checkpoint, LoRA, VAE) and base model (SD 1.5, SDXL, etc.)","Download models directly into package-specific model folders with automatic organization","View model metadata (description, rating, download count) before downloading"],"best_for":["Artists and creators discovering community models without browser context switching","Teams managing shared model libraries with centralized CivitAI discovery","Developers building SD applications with integrated model marketplace access"],"limitations":["CivitAI API rate limiting (likely 100-500 requests/hour) may throttle rapid model searches","Model metadata caching is in-memory only — cache is lost on application restart","No support for private/gated models on CivitAI (requires manual authentication)","Download speed limited by user's internet connection and CivitAI CDN availability","Model compatibility validation is metadata-based only — no runtime compatibility checking"],"requires":["Internet connection for CivitAI API access","CivitAI API endpoint availability (no local fallback)","Sufficient disk space for model downloads (checkpoints: 2-7GB, LoRAs: 10-500MB)"],"input_types":["search query (string)","filter criteria (model type, base model, sort order)","pagination parameters (page, limit)"],"output_types":["model metadata list (JSON with name, description, rating, download count)","model download URL","downloaded model file in local model folder"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_3","uri":"capability://image.visual.text.to.image.inference.workflow.orchestration","name":"text-to-image inference workflow orchestration","description":"Orchestrates end-to-end text-to-image generation workflows by translating UI parameter cards (prompt, negative prompt, sampler, steps, CFG scale, seed) into package-specific API calls (AUTOMATIC1111 txt2img endpoint, ComfyUI node graph execution). Implements parameter validation, preset management, and result caching. Supports batch generation with parameter sweeps (e.g., multiple seeds, CFG scales). Results are saved to local output folders with metadata JSON (prompt, model, parameters) for later retrieval.","intents":["Generate images from text prompts using installed SD packages without manual API calls","Save generation parameters and results for reproducibility and iteration","Run batch generations with parameter sweeps (multiple seeds, samplers, CFG values)","Switch between packages (ComfyUI vs AUTOMATIC1111) without re-entering parameters"],"best_for":["Artists and creators iterating on image generation with parameter exploration","Researchers running batch experiments with parameter sweeps","Teams building SD-based applications with inference pipeline requirements"],"limitations":["Parameter translation between packages is lossy — some AUTOMATIC1111 parameters (e.g., specific samplers) may not map to ComfyUI nodes","Batch generation is sequential, not parallel — multiple images generated one-at-a-time","No built-in image post-processing (upscaling, inpainting) — requires separate workflow","Result caching is file-based only — no deduplication of identical parameter sets across generations","Inference latency depends entirely on package implementation and hardware"],"requires":["Installed and running SD package (ComfyUI, AUTOMATIC1111, etc.)","Package API endpoint accessible (localhost:7860 for AUTOMATIC1111, localhost:8188 for ComfyUI)","Sufficient VRAM for model inference (typically 4GB+ for SD 1.5, 8GB+ for SDXL)"],"input_types":["prompt (string)","negative prompt (string)","model checkpoint (string, model name)","sampler (enum: Euler, DPM++, etc.)","steps (integer: 1-150)","CFG scale (float: 1.0-30.0)","seed (integer or -1 for random)","width/height (integer, multiple of 8)"],"output_types":["generated image (PNG with embedded metadata)","generation metadata JSON (prompt, model, parameters, timestamp)","batch results directory with indexed images"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_4","uri":"capability://image.visual.comfyui.node.graph.builder.and.execution","name":"comfyui node graph builder and execution","description":"Provides visual node graph builder for ComfyUI workflows with drag-and-drop node creation, connection validation, and serialization to ComfyUI JSON format. Implements node type registry with input/output type matching to prevent invalid connections. Executes workflows by sending JSON to ComfyUI API and polling for completion. Supports workflow templates, parameter overrides, and result streaming with progress callbacks.","intents":["Build ComfyUI workflows visually without manual JSON editing","Validate node connections before execution to catch configuration errors early","Execute workflows and stream results back to UI with progress updates","Save and reuse workflow templates with parameter overrides"],"best_for":["ComfyUI users building complex multi-step workflows (e.g., img2img + upscaling + inpainting)","Developers integrating ComfyUI into applications with programmatic workflow generation","Teams sharing workflow templates across projects"],"limitations":["Node graph UI complexity increases with workflow size — large graphs (50+ nodes) may be difficult to navigate","Node type registry must be manually updated when ComfyUI adds new node types","Workflow validation is static (connection types only) — no runtime semantic validation (e.g., model compatibility)","No built-in workflow optimization or node deduplication","Execution is synchronous — UI blocks during long-running workflows"],"requires":["ComfyUI package installed and running","ComfyUI API endpoint accessible (localhost:8188)","Valid ComfyUI node types registered in node type registry"],"input_types":["node type (string, e.g., 'CheckpointLoader')","node parameters (JSON object with input values)","connection specification (source node output → target node input)"],"output_types":["ComfyUI workflow JSON","execution results (images, metadata)","execution progress events (node completion, error messages)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_5","uri":"capability://image.visual.inference.parameter.card.ui.with.preset.management","name":"inference parameter card ui with preset management","description":"Provides dynamic parameter card UI for text-to-image generation with input validation, preset saving/loading, and real-time parameter preview. Cards include controls for prompt, negative prompt, model selection, sampler, steps, CFG scale, seed, and resolution. Implements preset serialization to JSON files in user config directory, enabling parameter reuse across sessions. Supports parameter history and quick-access favorite presets.","intents":["Configure text-to-image parameters with visual UI instead of manual API calls","Save parameter combinations as presets for quick reuse","Access parameter history and favorite presets without manual file management","Validate parameters before inference to catch invalid values early"],"best_for":["Artists and creators iterating on image generation with parameter exploration","Teams sharing parameter presets across projects","Non-technical users generating images without API knowledge"],"limitations":["Preset storage is local file-based only — no cloud sync or team sharing","Parameter validation is client-side only — server-side validation happens at inference time","No parameter recommendation system based on model or hardware","UI layout is fixed — no customization of visible parameters per package"],"requires":["Write permissions to user config directory for preset storage","Installed SD package with accessible API endpoint"],"input_types":["parameter values (text, numbers, selections)","preset name (string)"],"output_types":["validated parameter object (JSON)","preset file (JSON in config directory)","parameter history list"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_6","uri":"capability://automation.workflow.cross.platform.mvvm.ui.framework.with.avalonia","name":"cross-platform mvvm ui framework with avalonia","description":"Implements cross-platform desktop UI using Avalonia (XAML-based, .NET framework) with MVVM pattern for separation of concerns. ViewModels (e.g., PackageManagerViewModel, LaunchPageViewModel) handle business logic and state management, while Views (XAML) handle rendering. Uses dependency injection for service integration (IPackageManager, IModelManager, etc.). Supports design-time data for XAML preview and hot-reload during development.","intents":["Build consistent UI across Windows, macOS, and Linux without platform-specific code","Manage complex application state (package list, inference parameters) with reactive ViewModels","Enable rapid UI development with XAML preview and hot-reload","Integrate services (package management, model discovery) into UI without tight coupling"],"best_for":["Cross-platform desktop application developers using .NET ecosystem","Teams building data-driven UIs with complex state management","Developers familiar with WPF/XAML patterns transitioning to cross-platform"],"limitations":["Avalonia is less mature than WPF — some platform-specific features may require workarounds","MVVM pattern adds abstraction overhead — simple UIs may be over-engineered","Design-time data support requires manual ViewModel setup — not automatic","Hot-reload is development-only feature — not available in production builds","Performance on low-end hardware may be impacted by Avalonia rendering overhead"],"requires":[".NET 6.0+ runtime","Avalonia NuGet package (included in project dependencies)","XAML knowledge for UI customization"],"input_types":["XAML markup (UI definition)","ViewModel classes (business logic)","Service implementations (dependency injection)"],"output_types":["rendered UI (platform-native rendering via Avalonia)","user interaction events (button clicks, text input)","application state updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_7","uri":"capability://automation.workflow.application.lifecycle.and.navigation.service","name":"application lifecycle and navigation service","description":"Manages application startup, shutdown, and navigation between pages (PackageManager, LaunchPage, Settings, etc.) using a navigation service pattern. Implements startup sequence: detect platform prerequisites, initialize settings, load installed packages, initialize services. Uses MainWindowViewModel as root navigation hub with page-specific ViewModels. Handles graceful shutdown with cleanup (stopping running packages, saving state).","intents":["Initialize application with platform-specific prerequisites and configuration","Navigate between application pages without manual ViewModel instantiation","Persist application state across sessions (settings, installed packages)","Gracefully shutdown running packages and save state on application exit"],"best_for":["Desktop application developers managing complex initialization and state persistence","Teams building multi-page applications with centralized navigation","Developers implementing graceful shutdown with resource cleanup"],"limitations":["Navigation service adds abstraction overhead — simple single-page apps may be over-engineered","State persistence is file-based only — no database or cloud sync","Startup sequence is synchronous — long-running initialization may block UI","No built-in undo/redo for navigation history"],"requires":[".NET 6.0+ runtime","Avalonia framework","Configuration files in user data directory"],"input_types":["page name (string, e.g., 'PackageManager')","navigation parameters (optional, page-specific)"],"output_types":["rendered page UI","application state (settings, installed packages)","startup/shutdown logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_8","uri":"capability://data.processing.analysis.settings.persistence.and.localization.system","name":"settings persistence and localization system","description":"Persists application settings (installation paths, UI preferences, inference parameters) to JSON files in user config directory using a Settings model with serialization/deserialization. Implements localization system with language resource files (RESX or JSON) supporting multiple languages (English, Chinese, Japanese, etc.). Language selection is persisted in settings and applied at runtime via resource binding in XAML.","intents":["Persist user preferences across application sessions without manual file management","Support multiple languages with runtime language switching","Store inference parameter presets and package configurations","Enable team collaboration with shared settings export/import"],"best_for":["Desktop application developers managing user preferences and localization","Teams building multi-language applications for international users","Developers implementing settings UI with persistence"],"limitations":["Settings storage is file-based only — no encryption or access control","Localization is static (resource files) — no dynamic language addition at runtime","Settings migration between versions requires manual code — no automatic schema evolution","No settings validation — invalid values may cause runtime errors"],"requires":["Write permissions to user config directory","Localization resource files (RESX or JSON) for each supported language"],"input_types":["setting key (string)","setting value (any type)","language code (string, e.g., 'en-US')"],"output_types":["settings JSON file","localized UI strings","settings export file (for backup/sharing)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lykosai--stabilitymatrix__cap_9","uri":"capability://automation.workflow.platform.specific.build.and.auto.update.distribution","name":"platform-specific build and auto-update distribution","description":"Implements CI/CD workflows (GitHub Actions) for building platform-specific installers (Windows MSIX, macOS DMG, Linux AppImage) and distributing updates via auto-update system. Builds are triggered on release tags and produce platform-specific artifacts. Auto-update system checks for new versions at startup and downloads/installs updates in background without interrupting user workflow. Uses semantic versioning for version comparison.","intents":["Build and distribute platform-specific installers for Windows, macOS, and Linux","Automatically notify users of new versions and install updates in background","Maintain version compatibility and enable rollback to previous versions","Reduce manual update burden for end users"],"best_for":["Open-source desktop application maintainers distributing to multiple platforms","Teams managing end-user deployments with automatic update requirements","Developers implementing CI/CD for cross-platform builds"],"limitations":["Auto-update requires internet connection — offline users cannot update","Update downloads are full application bundles — no delta updates (large download size)","Rollback requires manual version selection — no automatic rollback on update failure","Platform-specific build configuration must be maintained for each OS","Update distribution relies on GitHub releases — no custom update server support"],"requires":["GitHub repository with Actions enabled","Platform-specific build tools (MSIX SDK for Windows, Xcode for macOS, AppImage tools for Linux)","Semantic versioning in release tags (e.g., v1.2.3)"],"input_types":["release tag (string, e.g., 'v1.2.3')","build configuration (platform-specific)"],"output_types":["platform-specific installer (MSIX, DMG, AppImage)","update manifest (version, download URL, changelog)","update package (downloaded by auto-update system)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Windows 10+, macOS 10.13+, or Linux with glibc 2.29+","Git installed and in PATH","Python 3.8+ (for target packages)","Administrator/sudo access for virtual environment creation","Python 3.8+ installed system-wide","NVIDIA CUDA 11.8 or 12.1 (for NVIDIA GPUs) OR AMD ROCm 5.7+ (for AMD GPUs) OR Apple Silicon with macOS 12+","pip and venv modules available in Python installation","Write permissions to package installation directory","Administrator/sudo access for symlink creation on Windows","Filesystem supporting symlinks (NTFS on Windows, ext4 on Linux, APFS on macOS)"],"failure_modes":["Package polymorphism adds abstraction overhead — each new package type requires BasePackage subclass implementation","Git-based updates can be slow on poor connections; no delta/incremental update support","Shared model folder symlinks may fail on Windows with non-admin permissions","No built-in package rollback — requires manual Git history navigation","Hardware detection relies on nvidia-smi, rocm-smi, or system APIs — may fail in containerized/WSL environments without GPU passthrough","PyTorch wheel selection is static per detected GPU type — no runtime fallback if wheel installation fails","CUDA/cuDNN prerequisite detection is heuristic-based (registry checks on Windows, LD_LIBRARY_PATH on Linux) and may miss non-standard installations","No support for multi-GPU load balancing or distributed inference setup","Symlinks may fail on Windows without administrator privileges or with certain filesystems (FAT32)","Symlink validation is one-time on startup — broken symlinks created after startup are not detected","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6265245390120663,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2026-05-02T02:33:50Z"},"community":{"stars":8100,"forks":551,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lykosai--stabilitymatrix","compare_url":"https://unfragile.ai/compare?artifact=lykosai--stabilitymatrix"}},"signature":"1mThVrKTKbAL+f+PbEmK+lsCLuQMZCK4t8eTZfGyTNeePyeui6IBUc7Gs0tRwusrSkNOOWhOwLta5K5wex4nDQ==","signedAt":"2026-06-20T05:12:09.637Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lykosai--stabilitymatrix","artifact":"https://unfragile.ai/lykosai--stabilitymatrix","verify":"https://unfragile.ai/api/v1/verify?slug=lykosai--stabilitymatrix","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"}}