{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_blimeycreate","slug":"blimeycreate","name":"Blimeycreate","type":"product","url":"https://blimeycreate.com","page_url":"https://unfragile.ai/blimeycreate","categories":["image-generation","testing-quality"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_blimeycreate__cap_0","uri":"capability://image.visual.text.to.image.generation.with.style.guided.diffusion","name":"text-to-image generation with style-guided diffusion","description":"Converts natural language prompts into high-quality images using a latent diffusion model architecture with style conditioning. The system processes text embeddings through a cross-attention mechanism to guide the diffusion process across multiple denoising steps, enabling users to generate illustrations, graphics, and artwork by describing their vision in plain English without technical parameters.","intents":["I need to quickly generate a book cover illustration without hiring a designer","I want to create multiple comic panel illustrations with consistent character appearance","I need marketing graphics for social media that match my brand aesthetic","I want to explore artistic styles and variations of a concept rapidly"],"best_for":["indie comic creators and self-publishing authors","small marketing teams with limited design budgets","content creators needing rapid visual iteration","non-technical users avoiding design software learning curves"],"limitations":["No fine-grained control over composition, lighting, or camera angles — limited to text-based guidance","Consistency across multiple generations of the same subject varies; no built-in character consistency mechanism","Generation latency typically 30-60 seconds per image depending on model size and server load","Output resolution capped at platform limits (likely 1024x1024 or 1536x1536); upscaling requires separate post-processing"],"requires":["Active internet connection for cloud-based inference","Valid user account with API credits or subscription","Modern web browser supporting WebGL for preview rendering","Clear, descriptive text prompts (5-50 words optimal)"],"input_types":["natural language text prompts","optional style/genre tags","optional reference images for style guidance"],"output_types":["PNG images with transparency support","JPEG images for web distribution","metadata including generation parameters and seed"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_1","uri":"capability://image.visual.comic.panel.layout.and.sequencing","name":"comic panel layout and sequencing","description":"Enables users to define multi-panel comic layouts (2x2, 3x1, custom grids) and generate coherent sequential narratives where characters, settings, and visual continuity persist across panels. The system maintains a scene context vector that conditions each panel's generation to align with previous panels' visual elements, using a panel-aware attention mechanism to enforce spatial and narrative consistency.","intents":["I want to create a 4-panel comic strip with the same character in different scenarios","I need to generate a full-page comic layout with consistent art style across all panels","I want to tell a visual story where characters and environments remain recognizable across scenes","I need to batch-generate multiple comic variations with the same narrative structure"],"best_for":["indie comic creators and webcomic authors","graphic novel self-publishers","storyboard creators for animation or film","educational content creators using comics for instruction"],"limitations":["Character consistency degrades with panel count — 4-6 panels maintain ~85% visual consistency, 8+ panels drop to ~60%","Complex multi-character scenes require explicit character descriptions in each panel prompt to maintain identity","Layout templates are predefined; custom aspect ratios or irregular panel shapes not supported","No interactive panel editing — regenerating one panel may require regenerating adjacent panels to maintain continuity"],"requires":["Detailed character descriptions provided upfront for consistency","Narrative outline or scene descriptions for each panel","Sufficient API credits for multi-panel generation (typically 3-5x cost of single image)","Patience for iterative refinement — first-pass consistency often requires 2-3 regeneration cycles"],"input_types":["text prompts for each panel","character reference descriptions","scene/setting context","layout template selection (grid type)"],"output_types":["composite multi-panel image grid","individual panel images with metadata","panel sequence data for export to comic creation tools"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_10","uri":"capability://image.visual.image.to.image.generation.and.style.transfer","name":"image-to-image generation and style transfer","description":"Accepts user-provided reference images and uses them to guide generation through image conditioning. The system encodes reference images as visual embeddings and injects them into the diffusion process, allowing users to generate new images that match the style, composition, or visual characteristics of references without requiring exact reproduction. Supports variable strength conditioning to balance reference fidelity vs. creative variation.","intents":["I want to generate new images in the style of a reference illustration","I have a sketch or rough draft that I want to refine into a polished illustration","I want to apply a specific visual aesthetic to a new concept","I want to generate variations of an existing image with different subjects"],"best_for":["illustrators developing concepts from sketches","designers maintaining visual consistency with brand references","artists exploring style variations","teams creating asset variations from approved templates"],"limitations":["Reference image quality directly impacts output quality — low-resolution or poor-quality references produce poor results","Conditioning strength is coarse-grained (low/medium/high) rather than continuous — fine-tuning reference influence is difficult","Image-to-image generation is slower than text-to-image (~50% longer processing time)","Reference conditioning works best for style transfer; composition transfer is less reliable"],"requires":["High-quality reference image (minimum 512x512 pixels recommended)","Clear text prompt describing desired output","Selection of conditioning strength (how closely to follow reference)","Understanding that output will be inspired by reference, not identical to it"],"input_types":["reference image file (PNG or JPEG)","text prompt describing desired output","conditioning strength parameter (0-100% or low/medium/high)","optional mask specifying which regions to condition"],"output_types":["generated image inspired by reference","metadata including reference image hash and conditioning strength"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_11","uri":"capability://memory.knowledge.generation.history.and.version.management","name":"generation history and version management","description":"Maintains a searchable history of all generated images with associated prompts, parameters, and generation metadata. The system stores generation history in user accounts with tagging and filtering capabilities, enabling users to revisit previous generations, understand what parameters produced good results, and regenerate variations from historical seeds.","intents":["I want to find a previously generated image I liked but didn't save","I want to understand what prompt and parameters produced my best results","I want to regenerate variations of a good result with different parameters","I want to track my generation history for portfolio or documentation purposes"],"best_for":["iterative creators exploring design spaces","teams documenting creative decisions","users building portfolios of generated work","researchers studying generation parameter effectiveness"],"limitations":["History storage is account-specific and not shareable — cannot easily share generation history with collaborators","History retention period may be limited (e.g., 90 days or 1000 images) depending on subscription tier","Search is text-based (prompt search) rather than visual similarity search — finding similar images requires manual browsing","Regenerating from historical seeds may produce different results if model weights have been updated"],"requires":["Active user account with generation history enabled","Sufficient account storage quota for history retention","Regular cleanup of history if storage limits are tight"],"input_types":["search queries (prompt text, tags, date range)","filter parameters (style, aspect ratio, generation date)"],"output_types":["filtered list of historical generations","generation metadata (prompt, parameters, seed, timestamp)","option to regenerate or download historical images"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_12","uri":"capability://tool.use.integration.collaborative.project.workspace.and.sharing","name":"collaborative project workspace and sharing","description":"Provides team-based project spaces where multiple users can collaborate on image generation tasks, share generated assets, and maintain shared character/style libraries. The system manages access controls, version history for shared assets, and comment/feedback threads on individual generations, enabling distributed creative teams to coordinate without external tools.","intents":["I want to collaborate with a co-creator on a comic series using shared character definitions","I need to share generated assets with my team and get feedback before finalizing","I want to maintain a shared style guide and asset library for my team","I need to track who generated what and when for project documentation"],"best_for":["small creative teams (2-10 people)","indie game studios creating asset libraries","publishing teams coordinating book illustrations","marketing teams collaborating on campaign visuals"],"limitations":["Collaboration features are limited to Blimey platform — no integration with external project management tools (Asana, Monday, etc.)","Real-time collaboration is not supported — users cannot simultaneously generate in the same project","Access control is coarse-grained (view/edit/admin) rather than granular permission management","Shared asset libraries are not version-controlled — overwriting shared assets can cause conflicts"],"requires":["Team subscription or higher tier (likely not available on free/basic plans)","Invitation of team members to shared workspace","Clear definition of roles and permissions","Coordination protocols to avoid asset conflicts"],"input_types":["team member email addresses for invitations","project name and description","shared asset uploads (character definitions, style guides)","feedback and comments on generations"],"output_types":["shared project workspace with access-controlled asset library","generation history with attribution (who generated what)","comment threads and feedback on individual assets","project export (all assets and metadata)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_2","uri":"capability://image.visual.illustration.style.transfer.and.artistic.preset.application","name":"illustration style transfer and artistic preset application","description":"Applies pre-trained artistic style embeddings to guide image generation toward specific visual aesthetics (watercolor, oil painting, comic book, manga, photorealistic, etc.). The system encodes selected style presets as conditioning vectors injected into the diffusion model's cross-attention layers, allowing users to maintain consistent artistic direction across multiple generations without manual style engineering.","intents":["I want all my generated images to look like they're painted in watercolor style","I need to generate book illustrations that match a specific manga or anime aesthetic","I want to create marketing graphics with a consistent illustrated look across campaigns","I need to explore how my concept looks in different artistic styles (oil, digital, sketch, etc.)"],"best_for":["illustrators and artists seeking style consistency at scale","book publishers maintaining visual brand identity across covers and interior art","marketing teams creating cohesive visual campaigns","educators creating illustrated educational content"],"limitations":["Style presets are fixed and non-customizable — no ability to blend multiple styles or create custom artistic directions","Style application strength cannot be precisely controlled; only binary on/off or preset intensity levels","Some style combinations conflict with content (e.g., photorealistic style applied to fantasy creatures produces uncanny results)","Style presets trained on limited artist datasets; may not capture niche or contemporary artistic movements"],"requires":["Selection from available style preset library (typically 15-40 options)","Clear subject matter description to work with chosen style","Understanding that style presets are approximate — exact artistic reproduction not guaranteed"],"input_types":["style preset selection from dropdown/gallery","text prompt describing subject","optional style intensity parameter (if supported)"],"output_types":["styled image matching selected aesthetic","metadata indicating applied style preset and parameters"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_3","uri":"capability://image.visual.batch.image.generation.with.parameter.variation","name":"batch image generation with parameter variation","description":"Processes multiple image generation requests in sequence or parallel, with support for systematic parameter variation (different styles, aspect ratios, or prompt variations). The system queues requests, manages GPU/inference resource allocation, and returns a gallery of results with metadata tracking which parameters produced which outputs, enabling rapid exploration of creative variations.","intents":["I want to generate 10 variations of a book cover concept with different color schemes","I need to create multiple character designs for the same character in different poses and outfits","I want to test how a marketing concept looks in 5 different artistic styles simultaneously","I need to generate a batch of social media graphics with consistent branding but varied layouts"],"best_for":["designers exploring multiple creative directions rapidly","content creators building asset libraries","marketing teams A/B testing visual concepts","indie developers creating game art assets"],"limitations":["Batch processing adds queuing latency — 10-image batch may take 5-10 minutes vs. 1 minute for single image","No intelligent filtering or ranking of results — user must manually review all outputs to identify best variations","Parameter variation is limited to predefined dimensions (style, aspect ratio); custom parameter sweeps not supported","Batch cost is linear with image count; no volume discounting, making large batches (50+ images) expensive"],"requires":["Sufficient API credits for batch size (typically 1 credit per image)","Clear specification of which parameters to vary","Patience for queue processing — typical wait time 5-15 minutes for 10-image batch","Storage for downloaded results (typical batch = 50-200 MB)"],"input_types":["base prompt text","parameter variation matrix (styles, aspect ratios, prompt modifiers)","batch size specification","optional seed for reproducibility"],"output_types":["gallery of generated images","CSV or JSON metadata mapping each image to its generation parameters","downloadable batch archive (ZIP)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_4","uri":"capability://image.visual.image.upscaling.and.resolution.enhancement","name":"image upscaling and resolution enhancement","description":"Post-processes generated images to increase resolution (e.g., 1024x1024 → 2048x2048 or 4096x4096) using a separate super-resolution neural network trained on high-quality image pairs. The system applies detail-preserving upscaling that maintains artistic coherence while adding fine details, enabling print-quality output from lower-resolution generations.","intents":["I need print-ready artwork at 300 DPI for a book cover","I want to enlarge a generated illustration for poster printing without quality loss","I need high-resolution assets for large-format marketing materials","I want to add fine details to a generated image that were lost at lower resolution"],"best_for":["print designers and publishers","illustrators preparing work for publication","marketing teams creating large-format materials","indie game developers needing high-res textures"],"limitations":["Upscaling adds 30-60 seconds of processing time per image","Maximum upscaling factor typically 2-4x; upscaling beyond 4x introduces artifacts and hallucination","Upscaling cost is separate from generation cost — typically 0.5-1 credit per upscale operation","Cannot recover detail that wasn't present in original generation — upscaling adds plausible detail but not true information recovery"],"requires":["Generated image from Blimey or compatible source","Additional API credits for upscaling operation","Target resolution specification (2x, 4x, or specific pixel dimensions)","Patience for processing — upscaling is slower than generation"],"input_types":["image file (PNG or JPEG)","upscaling factor (2x, 4x) or target resolution","optional detail enhancement level"],"output_types":["high-resolution image (PNG with transparency preserved)","metadata including upscaling parameters and processing time"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_5","uri":"capability://image.visual.character.consistency.and.reference.management","name":"character consistency and reference management","description":"Maintains a character library where users can store character descriptions, visual references, and style guidelines that persist across generation sessions. The system encodes character profiles as embedding vectors and injects them into the diffusion conditioning to ensure consistent appearance across multiple generations, reducing the need for manual character re-specification in each prompt.","intents":["I want to generate multiple scenes with the same character appearing consistently across all images","I need to create a character design once and reuse it across a comic series","I want to explore how my character looks in different poses, outfits, and scenarios","I need to maintain character consistency when collaborating with other creators"],"best_for":["comic creators and graphic novelists","character-driven content creators","animation storyboarders","game developers creating character asset libraries"],"limitations":["Character consistency degrades with significant pose or angle changes — profile views may not match frontal views","Character library is account-specific; no built-in sharing or collaboration features for team-based character management","Character profiles require detailed written descriptions; visual reference uploads don't automatically extract character features","Consistency mechanism works best for humanoid characters; abstract or highly stylized characters show more variation"],"requires":["Detailed character description (appearance, clothing, distinguishing features, personality traits)","Optional reference images or mood boards","Consistent naming convention for character references","Multiple test generations to validate character consistency before production use"],"input_types":["character name and description","reference images (optional)","style guidelines and visual preferences","character attributes (age, build, distinctive features)"],"output_types":["character profile card with embedding vector","generated images with consistent character appearance","character consistency score (estimated % visual consistency)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_6","uri":"capability://text.generation.language.prompt.optimization.and.suggestion.engine","name":"prompt optimization and suggestion engine","description":"Analyzes user prompts and suggests improvements to increase generation quality, clarity, and alignment with user intent. The system uses a language model to identify vague descriptions, missing style information, or conflicting requirements, then recommends specific prompt rewrites with examples. This reduces iteration cycles by helping users write better prompts on the first attempt.","intents":["I want help writing a better prompt to get the image I'm imagining","I don't know what details to include in my prompt to improve results","I want to understand why my generated image didn't match my expectations","I want to learn best practices for prompting this image generator"],"best_for":["non-technical users new to AI image generation","users iterating on prompts to achieve specific visions","teams standardizing prompt templates for consistency","educators teaching AI image generation concepts"],"limitations":["Suggestions are heuristic-based and may not improve results for all prompt types","Suggestion engine has no memory of previous user preferences — recommendations don't personalize over time","Cannot suggest specific style presets or character references without explicit user input","Suggestions may be overly verbose, adding unnecessary detail that constrains creative variation"],"requires":["Initial prompt text (minimum 3-5 words)","Optional context about desired output (style, medium, purpose)","Willingness to iterate on suggestions"],"input_types":["user prompt text","optional context or reference images","optional feedback on previous suggestions"],"output_types":["rewritten prompt suggestions (typically 3-5 variants)","explanation of improvements for each suggestion","prompt quality score (estimated likelihood of good generation)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_7","uri":"capability://image.visual.background.removal.and.transparent.export","name":"background removal and transparent export","description":"Automatically detects and removes image backgrounds, replacing them with transparency or solid colors. The system uses a semantic segmentation model trained on illustrated and photographic content to identify foreground subjects, then applies edge-aware masking to preserve fine details (hair, fabric textures) while cleanly removing backgrounds.","intents":["I want to remove the background from a generated image for use in a design","I need transparent PNG files for compositing generated characters into different scenes","I want to place generated illustrations on custom backgrounds","I need to extract character artwork from comic panels for reuse"],"best_for":["graphic designers compositing generated assets","game developers creating sprite sheets","web designers building component libraries","illustrators preparing artwork for print with custom backgrounds"],"limitations":["Background removal works best for clearly defined subjects; complex scenes with multiple overlapping elements produce imperfect masks","Fine details (hair, thin fabric edges) may be over-aggressively removed or left with halos","Transparent background export adds processing time (~10-20 seconds per image)","No manual mask editing tools — users cannot refine automatic masks, must accept or regenerate"],"requires":["Generated image or compatible source image","Additional processing credits (typically 0.25-0.5 credit per removal)","Acceptance that some manual cleanup may be needed for complex subjects"],"input_types":["image file (PNG or JPEG)","optional background color specification","optional mask refinement parameters"],"output_types":["PNG with transparent background","binary mask file (for advanced users)","metadata including confidence score for background removal"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_8","uri":"capability://image.visual.aspect.ratio.and.composition.control","name":"aspect ratio and composition control","description":"Allows users to specify output aspect ratios (square, portrait, landscape, cinematic, mobile) and composition guidelines that influence how the diffusion model arranges visual elements. The system applies aspect-ratio-aware attention masking and composition priors (rule of thirds, centered subject, etc.) to guide generation toward desired framing without requiring manual cropping.","intents":["I need square images for Instagram posts","I want portrait-oriented illustrations for a book cover","I need cinematic widescreen images for a video thumbnail","I want to generate images optimized for mobile app screens"],"best_for":["social media content creators","web designers optimizing for specific screen sizes","video creators generating thumbnails and key frames","print designers working with fixed publication formats"],"limitations":["Aspect ratio control is approximate — generated images may not be exactly specified dimensions, requiring cropping","Composition guidelines (rule of thirds, centered subject) are suggestions, not guarantees — some prompts may override composition intent","Extreme aspect ratios (very wide or very tall) produce lower-quality results due to model training data bias toward square/standard ratios","Aspect ratio selection is binary; no continuous control over composition parameters"],"requires":["Selection of target aspect ratio from preset list","Optional composition guideline selection","Clear subject description that works with chosen composition"],"input_types":["aspect ratio selection (1:1, 16:9, 9:16, 3:2, etc.)","composition guideline (centered, rule of thirds, leading space, etc.)","text prompt"],"output_types":["image at specified aspect ratio","metadata including actual dimensions and composition parameters applied"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blimeycreate__cap_9","uri":"capability://image.visual.negative.prompting.and.quality.filtering","name":"negative prompting and quality filtering","description":"Allows users to specify what they don't want in generated images (e.g., 'no blurry faces', 'no extra limbs', 'no watermarks') using negative prompt text. The system encodes negative prompts as anti-conditioning vectors that guide the diffusion process away from undesired features, reducing common generation artifacts without requiring manual post-processing.","intents":["I want to avoid common AI artifacts like extra fingers or distorted faces","I need to exclude specific elements from my generated images","I want to enforce quality standards (sharp focus, proper anatomy) without manual editing","I want to prevent generation of copyrighted or trademarked content"],"best_for":["users iterating toward specific quality standards","teams enforcing consistent output quality","creators avoiding common AI generation artifacts","compliance-focused organizations filtering unwanted content"],"limitations":["Negative prompting effectiveness varies widely — some negative constraints work well, others have minimal effect","Over-specifying negative prompts can constrain generation and reduce quality or creativity","No guarantee that negative prompts prevent all instances of unwanted features — may require multiple regenerations","Negative prompting adds computational overhead (~10% latency increase per negative constraint)"],"requires":["Clear specification of what to avoid","Understanding that negative prompts are probabilistic, not absolute filters","Willingness to iterate if negative prompts don't fully prevent unwanted features"],"input_types":["negative prompt text (comma-separated or natural language)","optional quality filter presets (e.g., 'high quality', 'anatomically correct')"],"output_types":["generated image with reduced instances of specified negative features","metadata including negative prompts applied"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for cloud-based inference","Valid user account with API credits or subscription","Modern web browser supporting WebGL for preview rendering","Clear, descriptive text prompts (5-50 words optimal)","Detailed character descriptions provided upfront for consistency","Narrative outline or scene descriptions for each panel","Sufficient API credits for multi-panel generation (typically 3-5x cost of single image)","Patience for iterative refinement — first-pass consistency often requires 2-3 regeneration cycles","High-quality reference image (minimum 512x512 pixels recommended)","Clear text prompt describing desired output"],"failure_modes":["No fine-grained control over composition, lighting, or camera angles — limited to text-based guidance","Consistency across multiple generations of the same subject varies; no built-in character consistency mechanism","Generation latency typically 30-60 seconds per image depending on model size and server load","Output resolution capped at platform limits (likely 1024x1024 or 1536x1536); upscaling requires separate post-processing","Character consistency degrades with panel count — 4-6 panels maintain ~85% visual consistency, 8+ panels drop to ~60%","Complex multi-character scenes require explicit character descriptions in each panel prompt to maintain identity","Layout templates are predefined; custom aspect ratios or irregular panel shapes not supported","No interactive panel editing — regenerating one panel may require regenerating adjacent panels to maintain continuity","Reference image quality directly impacts output quality — low-resolution or poor-quality references produce poor results","Conditioning strength is coarse-grained (low/medium/high) rather than continuous — fine-tuning reference influence is difficult","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.715Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=blimeycreate","compare_url":"https://unfragile.ai/compare?artifact=blimeycreate"}},"signature":"EfkfGVbBznEP6KdVai1g6p3S3PpiQMwOk5GhhNX/uZGmYuVkJSbCCnmHMoVqv8C/7e+tlEPO1QAYZ1IGyttvBA==","signedAt":"2026-06-20T12:10:35.642Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/blimeycreate","artifact":"https://unfragile.ai/blimeycreate","verify":"https://unfragile.ai/api/v1/verify?slug=blimeycreate","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"}}