{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-aidc-ai--comfyui-copilot","slug":"aidc-ai--comfyui-copilot","name":"ComfyUI-Copilot","type":"agent","url":"https://github.com/AIDC-AI/ComfyUI-Copilot","page_url":"https://unfragile.ai/aidc-ai--comfyui-copilot","categories":["automation","code-editors"],"tags":["agent","ai","comfy-ui","comfyui","comfyui-nodes","copilot","deepseek","deepseek-v3","flux","gpt-4","llm-agent","rag","stable-diffusion"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-aidc-ai--comfyui-copilot__cap_0","uri":"capability://planning.reasoning.natural.language.to.comfyui.node.recommendation","name":"natural-language-to-comfyui-node-recommendation","description":"Converts natural language queries into ComfyUI node recommendations by leveraging LLM reasoning over a 60,000+ model knowledge base (LoRA and Checkpoint models). The system uses multi-provider LLM backends (OpenAI, DeepSeek, Qwen-plus) with RAG-style context injection to understand user intent and map it to appropriate node selections, then renders interactive node cards in the chat interface that users can directly insert into their workflow canvas.","intents":["I want to ask the AI what nodes I need to build a specific image generation workflow","I need help finding the right LoRA or checkpoint model for my use case without manually browsing thousands of options","I want to get step-by-step node recommendations as natural language suggestions I can click to add to my canvas"],"best_for":["ComfyUI users new to node-based workflows who prefer conversational discovery","teams building custom image generation pipelines who want AI-assisted node selection","users working with large model libraries who need semantic search over model metadata"],"limitations":["Recommendation accuracy depends on LLM's training data cutoff; newer models may not be recognized","No real-time model availability checking — recommends models that may not be installed locally","Context window limits prevent comprehensive analysis of very large existing workflows (>100 nodes)"],"requires":["ComfyUI installation with custom node support","API key for at least one supported LLM provider (OpenAI, DeepSeek, or Qwen)","Network connectivity to external LLM services"],"input_types":["natural language text queries","current ComfyUI workflow context (node graph structure)"],"output_types":["structured node recommendations with metadata","interactive UI cards with node insertion actions","model identifiers and parameter suggestions"],"categories":["planning-reasoning","tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_1","uri":"capability://text.generation.language.conversational.workflow.chat.with.context.awareness","name":"conversational-workflow-chat-with-context-awareness","description":"Implements a React-based chat interface that maintains conversation history through ChatContext state management while maintaining awareness of the user's current ComfyUI workflow state (selected nodes, canvas configuration, loaded models). The system sends workflow context to LLM backends as part of each query, enabling the AI to provide advice that's specific to the user's current setup rather than generic guidance. Messages are rendered with specialized formatting for different response types (text, node recommendations, parameter suggestions).","intents":["I want to ask questions about my current workflow and get advice specific to what I've already built","I need help debugging why my current node configuration isn't producing expected results","I want a conversational AI assistant that understands my ComfyUI setup without me having to manually describe it"],"best_for":["individual creators iterating on ComfyUI workflows who benefit from real-time AI guidance","teams collaborating on shared workflows who want centralized AI assistance accessible from the UI","power users building complex multi-stage pipelines who need context-aware troubleshooting"],"limitations":["Context window size limits how much workflow history can be included per query (~4k-8k tokens depending on provider)","No persistent conversation storage across ComfyUI sessions — history is lost on restart unless manually exported","Latency of 2-5 seconds per LLM response may feel slow for rapid back-and-forth debugging sessions"],"requires":["React 16.8+ (hooks support for ChatContext)","Active LLM API connection (OpenAI, DeepSeek, or Qwen)","ComfyUI instance with workflow state accessible to the plugin"],"input_types":["natural language text messages","current workflow graph (node IDs, connections, parameters)","selected node information"],"output_types":["formatted chat messages with markdown support","interactive node recommendation cards","parameter adjustment suggestions","code snippets for custom nodes"],"categories":["text-generation-language","planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_10","uri":"capability://data.processing.analysis.performance.profiling.and.optimization.recommendations","name":"performance-profiling-and-optimization-recommendations","description":"Profiles workflow execution performance by tracking node execution times, memory usage, and bottlenecks, then uses LLM reasoning to suggest optimizations. The system identifies slow nodes, high-memory operations, and suggests alternatives (e.g., 'replace this upscaler with a faster model', 'reduce batch size to fit in VRAM'). Performance data is collected from ComfyUI's execution logs and correlated with node configurations to provide actionable recommendations.","intents":["I want to know which nodes in my workflow are slowest and how to speed them up","I need suggestions for reducing memory usage without sacrificing output quality","I want to understand the performance impact of different parameter choices"],"best_for":["users running inference on resource-constrained hardware who need optimization guidance","teams running production workflows who want to minimize latency and cost","researchers benchmarking different node configurations"],"limitations":["Performance profiling requires multiple workflow executions — cannot predict performance without running the workflow","Optimization recommendations are heuristic-based and may not apply to all hardware configurations","No access to actual hardware specs (GPU model, VRAM) — recommendations are generic","Performance improvements from suggestions are not guaranteed — actual speedup depends on specific hardware and model"],"requires":["ComfyUI execution logs with timing and memory data","LLM API access for optimization suggestion generation","Optional: hardware specification data for hardware-aware recommendations"],"input_types":["workflow execution logs (node times, memory usage)","node configurations (model, parameters)","performance targets (latency, memory limits)"],"output_types":["performance profile (per-node timing and memory)","bottleneck identification","optimization suggestions with estimated impact","alternative node recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_11","uri":"capability://text.generation.language.ai.assisted.workflow.documentation.generation","name":"ai-assisted-workflow-documentation-generation","description":"Automatically generates documentation for ComfyUI workflows by analyzing the node graph, parameter configurations, and conversation history to create human-readable descriptions of what the workflow does and how to use it. The system generates documentation in multiple formats (markdown, HTML, interactive guides) and can include screenshots, parameter explanations, and usage examples. Documentation can be exported for sharing with team members or publishing.","intents":["I want to document my workflow so team members can understand how to use it","I need to create a user guide for a custom workflow that explains each node's purpose","I want to generate documentation automatically instead of writing it manually"],"best_for":["teams sharing workflows who need clear documentation for onboarding","researchers publishing workflows who need to explain their methodology","content creators building reusable workflow templates"],"limitations":["Generated documentation quality depends on node metadata quality — poorly documented nodes result in poor documentation","LLM may misinterpret node purpose or parameters, resulting in inaccurate documentation","Documentation generation requires additional LLM API calls, adding latency and cost","No built-in version control — documentation updates are not tracked"],"requires":["Node metadata (descriptions, parameter explanations)","Workflow graph structure","LLM API access for documentation generation","Optional: conversation history for context"],"input_types":["workflow JSON (node graph)","node metadata and descriptions","optional conversation history","documentation format preference (markdown, HTML, PDF)"],"output_types":["formatted documentation (markdown, HTML, PDF)","parameter reference guides","usage examples and screenshots","troubleshooting guides"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_2","uri":"capability://planning.reasoning.genlab.parameter.optimization.and.batch.debugging","name":"genlab-parameter-optimization-and-batch-debugging","description":"Implements an advanced parameter exploration interface (GenLab) that uses LLM reasoning to suggest parameter variations and batch configurations for ComfyUI nodes. The system analyzes current node parameters, generates systematic variations (e.g., different seed values, model weights, sampling steps), and allows users to queue batch executions. Results are tracked in a history interface showing parameter combinations and their outputs, enabling systematic experimentation and optimization workflows without manual parameter tweaking.","intents":["I want to systematically test different parameter combinations to find optimal settings for my model","I need to run batch experiments with parameter variations and compare results side-by-side","I want the AI to suggest which parameters are most likely to improve my output quality"],"best_for":["researchers and ML engineers optimizing model inference parameters","content creators doing systematic A/B testing of generation settings","teams running batch processing jobs that require parameter sweep capabilities"],"limitations":["Batch execution speed depends on ComfyUI's queue processing — no parallelization across multiple GPU instances","Parameter suggestion quality limited by LLM's understanding of specific model architectures (may suggest invalid combinations)","History storage is in-memory only — large batch experiments (>1000 variations) may cause UI performance degradation","No built-in statistical analysis of results — users must manually compare outputs to identify optimal parameters"],"requires":["ComfyUI with GPU support for batch execution","LLM API access for parameter suggestion generation","Sufficient disk space for storing batch experiment outputs"],"input_types":["current node parameter configuration","parameter ranges and constraints","user-specified optimization objectives (e.g., 'faster inference', 'higher quality')"],"output_types":["parameter variation suggestions (structured JSON)","batch queue configuration","experiment history with results metadata","comparative analysis visualizations"],"categories":["planning-reasoning","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.backend.abstraction","name":"multi-provider-llm-backend-abstraction","description":"Abstracts communication with multiple LLM providers (OpenAI GPT-4, DeepSeek V3, Qwen-plus) through a unified API interface that handles provider-specific request formatting, authentication, and response parsing. The system allows users to configure which provider to use via settings, automatically routes requests to the selected backend, and handles provider-specific features (e.g., function calling schemas, token counting) transparently. This enables users to switch providers without changing the UI or workflow logic.","intents":["I want to use a cheaper LLM provider (DeepSeek) for routine queries and reserve expensive providers (GPT-4) for complex tasks","I need to switch LLM providers if one service goes down or becomes unavailable","I want to compare response quality across different LLM models without rebuilding the integration"],"best_for":["cost-conscious teams wanting to optimize LLM API spending across different providers","organizations with multi-region deployments requiring provider redundancy","researchers comparing LLM performance on ComfyUI-specific tasks"],"limitations":["Response quality and latency vary significantly across providers — no automatic load balancing or failover","Provider-specific features (e.g., vision capabilities, function calling) may not be uniformly supported across all backends","API rate limits and quota management are provider-specific — no unified rate limiting across backends","Cost tracking requires manual monitoring of each provider's usage dashboard"],"requires":["API keys for at least one supported provider (OpenAI, DeepSeek, or Qwen)","Network connectivity to external LLM services","Configuration UI access to select active provider"],"input_types":["provider selection (enum: openai, deepseek, qwen)","API keys and authentication credentials","standardized LLM request format (messages, system prompt, parameters)"],"output_types":["standardized LLM response format (text, token count, usage metadata)","provider-agnostic structured data (node recommendations, parameter suggestions)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_4","uri":"capability://tool.use.integration.comfyui.workflow.state.synchronization.and.canvas.manipulation","name":"comfyui-workflow-state-synchronization-and-canvas-manipulation","description":"Maintains real-time synchronization between the Copilot UI state and ComfyUI's canvas through bidirectional API communication. The system polls ComfyUI's workflow state (node graph, connections, parameter values), detects changes to selected nodes, and can programmatically insert recommended nodes into the canvas with automatic connection routing. This enables the AI to not only suggest nodes but also directly modify the workflow graph when users approve recommendations.","intents":["I want to click a recommended node and have it automatically added to my canvas in the right position","I want the copilot to understand which nodes I currently have selected and provide context-aware suggestions","I want to see real-time updates in the copilot when I modify my workflow on the canvas"],"best_for":["ComfyUI users who want seamless integration between AI suggestions and canvas manipulation","workflow builders who want to minimize manual node placement and connection work","teams building custom ComfyUI extensions that need to programmatically modify workflows"],"limitations":["Synchronization latency of 500-1000ms due to polling interval — rapid canvas changes may not be immediately reflected in copilot context","Automatic node connection routing is heuristic-based and may fail for complex multi-input nodes, requiring manual adjustment","No undo/redo integration — programmatic node insertion cannot be undone through ComfyUI's standard undo mechanism","State synchronization is one-way for canvas→copilot; copilot cannot detect parameter changes made directly on the canvas without polling"],"requires":["ComfyUI API endpoint accessible from the plugin (default: localhost:8188)","ComfyUI version with workflow state export capability","React component lifecycle hooks for polling and state updates"],"input_types":["ComfyUI workflow JSON (node graph structure)","node selection events from canvas","user approval to insert recommended nodes"],"output_types":["modified workflow JSON with new nodes inserted","canvas update events","node connection metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_5","uri":"capability://search.retrieval.ai.powered.node.search.and.discovery","name":"ai-powered-node-search-and-discovery","description":"Implements semantic search over ComfyUI's node registry and model database using LLM embeddings and similarity matching. Users can search for nodes using natural language descriptions (e.g., 'upscale image quality') rather than exact node names, and the system returns ranked results with relevance scores. The search index includes both built-in ComfyUI nodes and community custom nodes, with metadata about node purpose, inputs, outputs, and compatible models.","intents":["I want to find nodes by describing what I want to do, not by knowing the exact node name","I need to discover community custom nodes that might solve my specific problem","I want to search across 60,000+ models to find ones matching my use case description"],"best_for":["new ComfyUI users unfamiliar with the node naming conventions","users exploring community custom nodes without browsing the full registry","teams building model-agnostic workflows who need flexible model discovery"],"limitations":["Search index is static and requires manual updates when new nodes or models are released","Semantic search quality depends on embedding model quality — may return irrelevant results for ambiguous queries","No ranking by popularity or maintenance status — results include abandoned or poorly-maintained custom nodes","Search latency of 1-2 seconds due to embedding computation and similarity matching"],"requires":["Pre-computed embedding index of ComfyUI nodes and models","Embedding model (can be local or cloud-based)","Access to ComfyUI node registry metadata"],"input_types":["natural language search queries","optional filters (node type, model category, provider)"],"output_types":["ranked list of nodes with relevance scores","node metadata (inputs, outputs, parameters)","model identifiers and download links","usage examples or documentation links"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_6","uri":"capability://memory.knowledge.conversation.history.persistence.and.export","name":"conversation-history-persistence-and-export","description":"Manages chat conversation history through React Context state, allowing users to review previous interactions and export conversations as structured data (JSON, markdown, or PDF). The system tracks message metadata (timestamp, LLM provider used, tokens consumed, response latency) and enables users to reference previous suggestions or parameter configurations. History can be exported for documentation, sharing with team members, or archival purposes.","intents":["I want to review what the AI suggested in previous sessions to understand how I arrived at my current workflow","I need to export a conversation showing the AI's recommendations and my workflow evolution for team documentation","I want to share a specific conversation thread with a colleague to get their feedback on the AI's suggestions"],"best_for":["teams documenting workflow development and decision-making processes","researchers tracking AI-assisted experimentation for reproducibility","content creators maintaining archives of their creative process"],"limitations":["History is stored in-memory only — conversations are lost when ComfyUI is restarted unless manually exported","No built-in database or persistent storage — requires external storage solution for long-term archival","Export formats are static snapshots — cannot re-import and continue conversations from exported history","Large conversation histories (>1000 messages) may cause UI performance degradation due to in-memory storage"],"requires":["React Context API for state management","File system access for export functionality","Optional: external storage service for persistent history (not built-in)"],"input_types":["chat messages (text, metadata)","export format selection (JSON, markdown, PDF)","optional filters (date range, LLM provider)"],"output_types":["structured conversation history (JSON)","formatted conversation documents (markdown, PDF)","metadata reports (token usage, latency statistics)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_7","uri":"capability://planning.reasoning.custom.node.parameter.validation.and.suggestion","name":"custom-node-parameter-validation-and-suggestion","description":"Analyzes custom ComfyUI node parameters using LLM reasoning to validate parameter combinations, suggest optimal values based on the user's stated goals, and warn about incompatible configurations. The system understands parameter types (int, float, enum, string), constraints (min/max values, allowed options), and semantic relationships between parameters (e.g., 'batch_size should not exceed available VRAM'). When users modify parameters, the system provides real-time feedback on validity and optimization opportunities.","intents":["I want the AI to tell me if my parameter combination is valid before I run the workflow","I need suggestions for optimal parameter values given my hardware constraints and quality goals","I want warnings about parameter combinations that are likely to cause errors or poor results"],"best_for":["users working with complex custom nodes that have many interdependent parameters","teams running inference on resource-constrained hardware who need VRAM-aware parameter suggestions","researchers exploring parameter spaces systematically and needing validation feedback"],"limitations":["Parameter validation depends on LLM's understanding of node semantics — may miss domain-specific constraints","No access to actual hardware specs (VRAM, CPU) — suggestions are generic and may not account for user's specific setup","Parameter relationships are inferred from node documentation — may miss undocumented constraints or quirks","Real-time validation adds 500-1000ms latency per parameter change, which may feel slow for rapid iteration"],"requires":["Node parameter metadata (types, constraints, descriptions)","LLM API access for validation and suggestion generation","Optional: hardware specification data for resource-aware suggestions"],"input_types":["node parameter configuration (name, value, type)","parameter constraints (min, max, allowed values)","user context (hardware specs, quality goals)"],"output_types":["validation results (valid/invalid with reasons)","parameter suggestions with rationale","warning messages about problematic combinations","optimization recommendations"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_8","uri":"capability://planning.reasoning.workflow.template.generation.from.natural.language","name":"workflow-template-generation-from-natural-language","description":"Generates complete ComfyUI workflow templates from natural language descriptions by using LLM reasoning to decompose user intent into a sequence of nodes, determine appropriate connections, and set reasonable default parameters. The system outputs a workflow JSON that can be directly imported into ComfyUI, or renders an interactive preview showing the proposed node graph before import. This enables users to bootstrap complex workflows without manually assembling nodes.","intents":["I want to describe a workflow in plain English and have the AI generate a complete node graph I can use","I need a starting template for a common task (e.g., 'upscale and enhance an image') that I can then customize","I want to see a visual preview of the proposed workflow before importing it to make sure it matches my intent"],"best_for":["new ComfyUI users who want to quickly bootstrap workflows without learning node names and connections","teams building standardized workflows that can be templated and reused","content creators who want to focus on creative direction rather than technical node assembly"],"limitations":["Generated workflows may have suboptimal node ordering or unnecessary nodes — requires manual refinement","LLM may not understand domain-specific requirements (e.g., specific model compatibility, performance constraints)","Complex workflows with conditional logic or dynamic node counts cannot be generated from static templates","Generated parameter defaults may not match user's hardware or quality preferences"],"requires":["LLM API access for workflow generation","ComfyUI node registry metadata for validation","Workflow JSON schema for output generation"],"input_types":["natural language workflow description","optional constraints (model type, quality level, performance targets)","optional existing workflow to extend or modify"],"output_types":["workflow JSON (importable into ComfyUI)","visual node graph preview","parameter suggestions with rationale","alternative workflow suggestions"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-aidc-ai--comfyui-copilot__cap_9","uri":"capability://safety.moderation.model.compatibility.and.dependency.analysis","name":"model-compatibility-and-dependency-analysis","description":"Analyzes model compatibility across nodes by understanding model formats (safetensors, ckpt, LoRA), architecture requirements (SD 1.5, SDXL, Flux), and dependency chains. When users select a model or node, the system identifies compatible downstream nodes and warns about incompatibilities (e.g., 'this LoRA is for SDXL but you're using an SD 1.5 checkpoint'). The system maintains a knowledge base of model metadata indexed by architecture and format.","intents":["I want to know if the model I selected is compatible with the rest of my workflow","I need to find models that are compatible with my current checkpoint and LoRA selections","I want warnings about incompatible model combinations before I run the workflow"],"best_for":["users working with multiple model types (checkpoints, LoRAs, embeddings) who need compatibility checking","teams managing large model libraries who need to understand compatibility constraints","researchers exploring model combinations systematically"],"limitations":["Compatibility knowledge base requires manual curation — may be outdated for newly released models","Some models have undocumented compatibility quirks that the system cannot detect","No runtime validation — system cannot detect incompatibilities that only manifest during inference","Compatibility analysis is based on model metadata, not actual model weights — may miss subtle incompatibilities"],"requires":["Model metadata database with architecture and format information","Knowledge base of compatibility rules (e.g., 'LoRA-A requires SDXL checkpoint')","Access to user's loaded models and their metadata"],"input_types":["model identifiers (checkpoint, LoRA, embedding names)","node configuration (model inputs, architecture requirements)","workflow context (other selected models)"],"output_types":["compatibility assessment (compatible/incompatible/unknown)","warning messages with specific incompatibilities","suggestions for compatible model alternatives","dependency graph visualization"],"categories":["safety-moderation","planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["ComfyUI installation with custom node support","API key for at least one supported LLM provider (OpenAI, DeepSeek, or Qwen)","Network connectivity to external LLM services","React 16.8+ (hooks support for ChatContext)","Active LLM API connection (OpenAI, DeepSeek, or Qwen)","ComfyUI instance with workflow state accessible to the plugin","ComfyUI execution logs with timing and memory data","LLM API access for optimization suggestion generation","Optional: hardware specification data for hardware-aware recommendations","Node metadata (descriptions, parameter explanations)"],"failure_modes":["Recommendation accuracy depends on LLM's training data cutoff; newer models may not be recognized","No real-time model availability checking — recommends models that may not be installed locally","Context window limits prevent comprehensive analysis of very large existing workflows (>100 nodes)","Context window size limits how much workflow history can be included per query (~4k-8k tokens depending on provider)","No persistent conversation storage across ComfyUI sessions — history is lost on restart unless manually exported","Latency of 2-5 seconds per LLM response may feel slow for rapid back-and-forth debugging sessions","Performance profiling requires multiple workflow executions — cannot predict performance without running the workflow","Optimization recommendations are heuristic-based and may not apply to all hardware configurations","No access to actual hardware specs (GPU model, VRAM) — recommendations are generic","Performance improvements from suggestions are not guaranteed — actual speedup depends on specific hardware and model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5754621269256421,"quality":0.49,"ecosystem":0.7000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-04-07T04:23:08Z"},"community":{"stars":5067,"forks":318,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=aidc-ai--comfyui-copilot","compare_url":"https://unfragile.ai/compare?artifact=aidc-ai--comfyui-copilot"}},"signature":"dOZ7fkAhcuy3qsrgGqM7eHHBgJGtKKukvRbLbLcOaDO5oRYp0jBXyEkP7uOgupkJqVNn+boHMAv/l5Atgbd/Dw==","signedAt":"2026-06-23T09:42:37.110Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aidc-ai--comfyui-copilot","artifact":"https://unfragile.ai/aidc-ai--comfyui-copilot","verify":"https://unfragile.ai/api/v1/verify?slug=aidc-ai--comfyui-copilot","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"}}