{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools","slug":"x1xhlol--system-prompts-and-models-of-ai-tools","name":"system-prompts-and-models-of-ai-tools","type":"repo","url":"https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools","page_url":"https://unfragile.ai/x1xhlol--system-prompts-and-models-of-ai-tools","categories":["prompt-engineering","app-builders"],"tags":["ai","bolt","cluely","copilot","cursor","cursorai","devin","github-copilot","lovable","open-source","perplexity","replit","system-prompts","trae","trae-ai","trae-ide","v0","vscode","windsurf","windsurf-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_0","uri":"capability://memory.knowledge.multi.tool.system.prompt.extraction.and.cataloging","name":"multi-tool system prompt extraction and cataloging","description":"Extracts, organizes, and catalogs system prompts from 25+ AI coding tools (Cursor, Windsurf, Claude Code, v0, Lovable, etc.) into a structured repository with version tracking and architectural pattern identification. Uses community-driven collection to reverse-engineer tool behavior, enabling developers to understand how different AI systems are instructed to behave, what tool ecosystems they expose, and how they prioritize task execution across parallel vs. sequential workflows.","intents":["Understand how competing AI coding tools are architected and what instructions guide their behavior","Extract reusable system prompt patterns for building custom AI agents","Compare tool capabilities by analyzing their underlying system prompts and tool definitions","Reverse-engineer AI tool behavior to replicate or improve upon specific features"],"best_for":["AI tool builders and framework developers creating agentic IDEs","Researchers studying AI system design patterns and prompt engineering","Teams evaluating or migrating between AI coding assistants","Open-source maintainers building AI-powered development tools"],"limitations":["System prompts are reverse-engineered or community-contributed, not official documentation — may become stale as tools update","No guarantee of accuracy or completeness for proprietary tools that actively obfuscate their prompts","Lacks runtime behavior validation — prompts alone don't capture actual LLM model differences or fine-tuning","No structured schema validation — prompts stored as raw text without semantic parsing"],"requires":["GitHub account to access repository","Basic understanding of AI system prompts and tool calling conventions","No API keys or authentication required for read access"],"input_types":["system prompts (text)","tool definitions (JSON/YAML)","architectural documentation (markdown)"],"output_types":["structured prompt catalogs (text/markdown)","tool ecosystem maps (JSON)","comparative analysis documents"],"categories":["memory-knowledge","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_1","uri":"capability://tool.use.integration.agentic.ide.tool.ecosystem.mapping","name":"agentic ide tool ecosystem mapping","description":"Maps and categorizes the tool ecosystems exposed by agentic IDEs (Qoder, Windsurf, Claude Code, VSCode Agent) into 8-30+ discrete tool categories including code search, file operations, command execution, browser interaction, and memory systems. Analyzes how tools are organized hierarchically, whether they execute in parallel or sequential chains, and how validation pipelines (e.g., linter checks via get_problems) constrain tool output before user presentation.","intents":["Understand what tools each agentic IDE exposes and how they're organized architecturally","Design tool ecosystems for custom AI agents by learning from production patterns","Identify gaps in tool coverage when building specialized AI development assistants","Analyze execution strategies (parallel vs. sequential) to optimize agent performance"],"best_for":["AI framework developers building tool-calling systems (e.g., LangChain, Anthropic SDK users)","Teams designing custom agentic IDEs or specialized coding assistants","Researchers studying agent architecture patterns in production AI systems"],"limitations":["Tool definitions extracted from prompts may not reflect actual API signatures or parameter constraints","No runtime execution data — cannot determine actual tool success rates or latency profiles","Execution strategy (parallel vs. sequential) inferred from prompts, not from actual system behavior logs","Tool categories are descriptive, not prescriptive — no formal schema or ontology"],"requires":["Understanding of tool-calling conventions (OpenAI function calling, Anthropic tool_use, etc.)","Familiarity with agentic AI patterns and task decomposition"],"input_types":["system prompts (text)","tool definitions (JSON/YAML)","architectural documentation"],"output_types":["tool ecosystem diagrams (markdown/JSON)","tool category taxonomies","execution flow diagrams"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_10","uri":"capability://planning.reasoning.multi.model.routing.and.llm.configuration.pattern.extraction","name":"multi-model routing and llm configuration pattern extraction","description":"Catalogs how AI tools implement multi-model support and LLM configuration: model selection strategies, fallback mechanisms, cost optimization, and performance tuning. Analyzes how tools choose between models (GPT-4, Claude, Llama) based on task complexity, latency requirements, or cost constraints. Captures configuration patterns like temperature settings, token limits, and how tools adapt prompts for different model families and their specific capabilities/limitations.","intents":["Design multi-model AI systems that optimize for cost, latency, or quality based on task requirements","Implement model fallback strategies for reliability and cost management","Adapt prompts and tool definitions for different LLM families and their specific capabilities","Build systems that route tasks to appropriate models based on complexity assessment"],"best_for":["Developers building multi-model AI platforms or cost-optimized agents","Teams implementing LLM orchestration and model selection systems","Researchers studying model routing and prompt adaptation strategies"],"limitations":["Model routing strategies inferred from prompts, not from actual routing logic or performance data","No benchmarks for model selection accuracy, cost savings, or quality trade-offs","Prompt adaptation strategies are model-specific and may not generalize across families","Configuration parameters (temperature, token limits) are tool-specific and may not be optimal for custom use cases"],"requires":["Understanding of different LLM families and their capabilities/limitations","Familiarity with prompt engineering and model-specific optimization","Knowledge of cost and latency trade-offs in LLM selection"],"input_types":["system prompts describing model selection","task specifications (text)","model capability matrices (structured data)"],"output_types":["model selection decisions (text/JSON)","routing rules (JSON/code)","cost and latency estimates"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_11","uri":"capability://planning.reasoning.specialized.ai.system.pattern.documentation.trae.perplexity.proton","name":"specialized ai system pattern documentation (trae, perplexity, proton)","description":"Catalogs architectural patterns from specialized AI systems: Trae's agentic IDE design, Perplexity's web search and browser integration, Proton's multi-model routing and ecosystem integration, and Lumo's specialized capabilities. Analyzes how these systems differentiate through unique tool ecosystems, specialized prompts, and domain-specific optimizations. Captures cross-cutting patterns like communication protocols, user interaction models, and how systems adapt to different use cases (coding vs. research vs. productivity).","intents":["Learn from specialized AI systems to build domain-specific agents and tools","Understand how different AI systems differentiate through unique architectural choices","Identify patterns that work across specialized domains (coding, research, productivity)","Design systems that adapt to different use cases through specialized prompts and tool ecosystems"],"best_for":["Developers building specialized AI agents for specific domains","Teams designing differentiated AI products with unique capabilities","Researchers studying domain-specific AI system design patterns"],"limitations":["Specialized system patterns inferred from prompts and documentation, not from actual implementation","No performance benchmarks or user satisfaction metrics for specialized systems","Domain-specific optimizations may not generalize across different use cases","Ecosystem integration patterns are tool-specific and may not apply to custom systems"],"requires":["Understanding of domain-specific requirements (coding, research, productivity)","Familiarity with specialized tool ecosystems and integrations"],"input_types":["system prompts from specialized systems","tool definitions and ecosystem documentation","domain-specific requirements and use cases"],"output_types":["pattern analysis documents (markdown)","specialized system comparisons","domain-specific design guidelines"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_12","uri":"capability://planning.reasoning.cross.cutting.architectural.pattern.identification.and.comparison","name":"cross-cutting architectural pattern identification and comparison","description":"Identifies and compares cross-cutting architectural patterns that appear across multiple agentic IDEs and AI systems: tool system design patterns, file editing strategies, validation pipelines, memory architectures, and communication protocols. Analyzes how different tools solve similar problems (e.g., context window management, tool orchestration, error handling) with different approaches. Provides pattern language and taxonomy for describing AI system architectures.","intents":["Understand common architectural patterns in AI systems to inform custom system design","Identify trade-offs between different approaches to solving similar problems","Build pattern libraries for AI system development","Compare architectural choices across tools to understand design rationales"],"best_for":["AI framework and platform developers designing new systems","Architects evaluating design choices for AI-assisted development tools","Researchers studying AI system design patterns and best practices"],"limitations":["Patterns identified from prompts and documentation, not from actual implementation analysis","No performance or reliability metrics comparing different pattern implementations","Pattern applicability varies by use case — patterns optimized for one domain may not work for others","Pattern language is descriptive, not prescriptive — no formal specification or validation"],"requires":["Understanding of software architecture and design patterns","Familiarity with AI system design and agentic architectures"],"input_types":["system prompts from multiple AI tools","architectural documentation","tool definitions and ecosystem descriptions"],"output_types":["pattern catalogs (markdown/JSON)","architectural comparison matrices","pattern language documentation"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_2","uri":"capability://code.generation.editing.file.editing.strategy.pattern.extraction","name":"file editing strategy pattern extraction","description":"Extracts and compares file editing approaches used across AI tools: line-replace strategies (Lovable), ReplacementChunks (Windsurf), Quick Edit Comments (v0), and full-file rewrites. Analyzes how each tool handles edit validation, linter feedback integration, and conflict resolution when multiple edits target the same file region. Captures constraints like maximum edit chunk sizes and how tools preserve code structure during modifications.","intents":["Design efficient file editing systems for AI agents that minimize token usage and edit conflicts","Understand trade-offs between granular edits (line-replace) vs. chunk-based edits (ReplacementChunks)","Implement linter-aware editing that validates changes before committing to disk","Handle concurrent edits and merge conflicts in multi-agent or iterative refinement scenarios"],"best_for":["Developers building code generation or refactoring agents","Teams implementing AI-assisted code editors or IDE extensions","Researchers studying edit efficiency and code modification strategies"],"limitations":["Edit strategies are inferred from prompts, not from actual implementation code — may not reflect production behavior","No performance benchmarks comparing edit efficiency (token cost, latency, error rates) across strategies","Linter integration details vary by tool and language — patterns may not generalize","Conflict resolution strategies not fully documented for concurrent multi-agent scenarios"],"requires":["Understanding of code AST and line-based file representations","Familiarity with linter output formats and error reporting"],"input_types":["system prompts describing edit operations","code files (any language)","linter output (JSON/text)"],"output_types":["edit strategy comparisons (markdown)","edit operation sequences (JSON)","validation pipeline diagrams"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_3","uri":"capability://search.retrieval.code.search.and.context.discovery.pattern.analysis","name":"code search and context discovery pattern analysis","description":"Documents how different agentic IDEs implement code search and context gathering: semantic search (embeddings-based), keyword search, AST-based navigation, and codebase indexing strategies. Analyzes how tools prioritize context selection (recent files, related modules, search results ranking) and how search results are incorporated into LLM context windows. Captures constraints like maximum search result count and context window allocation strategies.","intents":["Design efficient code search systems that surface relevant context for AI agents without exceeding token budgets","Choose between semantic vs. keyword search based on use case and performance requirements","Implement context prioritization strategies to maximize code understanding within fixed context windows","Optimize codebase indexing for fast retrieval in large monorepos"],"best_for":["Developers building code-aware AI agents or IDE extensions","Teams implementing RAG (Retrieval-Augmented Generation) for code understanding","Researchers studying context selection strategies for code LLMs"],"limitations":["Search strategies inferred from prompts, not from actual search implementation or performance metrics","No latency or accuracy benchmarks for different search approaches","Codebase indexing details not fully documented — unclear how tools handle dynamic code changes","Context window allocation strategies vary by model and tool — patterns may not generalize"],"requires":["Understanding of code embeddings and semantic search","Familiarity with codebase indexing and retrieval systems"],"input_types":["system prompts describing search operations","code repositories (any language)","search queries (natural language or code snippets)"],"output_types":["search strategy comparisons (markdown)","context selection diagrams","indexing strategy documentation"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_4","uri":"capability://memory.knowledge.memory.and.knowledge.management.architecture.comparison","name":"memory and knowledge management architecture comparison","description":"Catalogs memory systems used by agentic IDEs: Knowledge Items (KI) architecture (Qoder), conversation logs with persistent context, workflow systems with turbo annotations, and state management patterns. Analyzes how tools maintain long-term context across conversations, handle memory eviction when context windows fill, and integrate external knowledge bases or documentation. Captures memory lifecycle: creation, retrieval, update, and deletion strategies.","intents":["Design persistent memory systems for AI agents that maintain context across multiple conversations","Implement knowledge base integration for domain-specific AI assistants","Handle memory eviction and prioritization when context windows are constrained","Build multi-turn conversation systems that learn from previous interactions"],"best_for":["Developers building stateful AI agents or multi-turn assistants","Teams implementing knowledge management for specialized AI tools","Researchers studying memory architectures in production AI systems"],"limitations":["Memory architectures inferred from prompts, not from actual storage implementation or performance data","No benchmarks for memory retrieval latency or eviction efficiency","Persistence mechanisms not fully documented — unclear how tools handle database failures or recovery","Knowledge base integration patterns vary by tool — may not generalize across domains"],"requires":["Understanding of vector databases and semantic search for memory retrieval","Familiarity with conversation state management and context window constraints"],"input_types":["system prompts describing memory operations","conversation histories (text)","knowledge base documents (text/structured data)"],"output_types":["memory architecture diagrams","knowledge item schemas (JSON)","state management patterns (markdown)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_5","uri":"capability://code.generation.editing.web.application.development.framework.pattern.extraction","name":"web application development framework pattern extraction","description":"Extracts web development patterns from AI tools specialized in web building (v0, Lovable, Same.dev): Next.js/React integration, Tailwind CSS design system adherence, shadcn/ui component usage, design aesthetics requirements, and SEO standards. Analyzes how tools handle component generation, styling constraints, and integration with external services (Stripe, analytics). Captures tool-specific conventions like Quick Edit Comments (v0) and design system customization approaches.","intents":["Build AI agents specialized in web application generation with consistent design and architecture","Understand how production AI tools enforce design system compliance and styling constraints","Implement component generation systems that produce production-ready web applications","Design prompts that guide AI models toward specific web frameworks and design patterns"],"best_for":["Developers building AI-powered web builders or code generators","Teams implementing design system enforcement in AI-assisted development","Researchers studying code generation patterns for web applications"],"limitations":["Web development patterns inferred from prompts, not from actual generated code analysis","No metrics on code quality, accessibility compliance, or performance of generated applications","Design system enforcement strategies vary by tool — may not generalize across frameworks","Integration patterns (Stripe, analytics) are tool-specific and may not apply to custom applications"],"requires":["Understanding of Next.js, React, and Tailwind CSS","Familiarity with component libraries like shadcn/ui","Knowledge of web design principles and accessibility standards"],"input_types":["system prompts describing web development","design specifications (text/images)","component libraries (code)"],"output_types":["web development pattern guides (markdown)","component generation templates","design system enforcement rules (JSON)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_6","uri":"capability://planning.reasoning.task.planning.and.complexity.assessment.strategy.documentation","name":"task planning and complexity assessment strategy documentation","description":"Documents how agentic IDEs decompose user requests into executable tasks: task planning algorithms, complexity assessment heuristics, and tool selection strategies. Analyzes how tools decide between parallel vs. sequential execution, when to delegate to sub-agents, and how to break down complex requests into manageable steps. Captures decision criteria like estimated token cost, execution time, and success probability.","intents":["Design task decomposition systems for AI agents that break down complex requests efficiently","Implement complexity assessment to predict task difficulty and resource requirements","Choose between parallel and sequential execution based on task dependencies","Delegate to specialized sub-agents when tasks exceed primary agent capabilities"],"best_for":["Developers building agentic systems with task planning capabilities","Teams implementing multi-agent orchestration frameworks","Researchers studying task decomposition and planning in AI systems"],"limitations":["Planning strategies inferred from prompts, not from actual execution logs or performance data","No metrics on planning accuracy, task success rates, or execution efficiency","Complexity assessment heuristics are tool-specific and may not generalize","Sub-agent delegation patterns not fully documented for custom agent networks"],"requires":["Understanding of task decomposition and planning algorithms","Familiarity with agent orchestration and tool calling patterns"],"input_types":["system prompts describing task planning","user requests (natural language)","tool definitions and capabilities"],"output_types":["task decomposition plans (JSON/markdown)","complexity assessment scores","execution strategy recommendations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_7","uri":"capability://automation.workflow.command.execution.and.terminal.integration.pattern.analysis","name":"command execution and terminal integration pattern analysis","description":"Catalogs how agentic IDEs integrate command execution and terminal access: shell command execution strategies, background process management, script execution and debugging systems, and output capture/parsing. Analyzes constraints like command timeout policies, output size limits, and security restrictions (e.g., no destructive commands). Captures how tools handle command failures, stderr/stdout parsing, and integration with linters and build systems.","intents":["Design secure command execution systems for AI agents with appropriate guardrails","Implement build system integration for automated testing and deployment","Parse command output and integrate results back into agent decision-making","Handle command failures and implement retry strategies"],"best_for":["Developers building AI agents with terminal access and build system integration","Teams implementing CI/CD automation with AI assistance","Researchers studying security and safety in AI-assisted development"],"limitations":["Command execution strategies inferred from prompts, not from actual implementation","Security restrictions and guardrails not fully documented — unclear what commands are blocked","No benchmarks for command execution latency or output parsing accuracy","Timeout policies and resource limits are tool-specific and may not generalize"],"requires":["Understanding of shell commands and scripting","Familiarity with build systems and CI/CD pipelines","Knowledge of security best practices for command execution"],"input_types":["system prompts describing command execution","shell commands (text)","build system configurations"],"output_types":["command execution logs (text)","parsed output (structured data)","execution status and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_8","uri":"capability://image.visual.browser.interaction.and.preview.system.pattern.documentation","name":"browser interaction and preview system pattern documentation","description":"Catalogs browser interaction capabilities in web-focused AI tools (Windsurf, Comet, Lovable): page interaction (clicking, typing, scrolling), screenshot capture, DOM inspection, and browser preview systems. Analyzes how tools handle dynamic content, JavaScript execution, and real-time page state tracking. Captures constraints like screenshot resolution, interaction latency, and how browser state is communicated back to the AI agent for decision-making.","intents":["Design AI agents that can interact with web applications and verify visual output","Implement visual feedback loops for web development tasks","Build systems that capture and analyze page state for debugging and validation","Create agents that can test web applications by simulating user interactions"],"best_for":["Developers building AI agents for web application testing and development","Teams implementing visual validation systems for AI-assisted web building","Researchers studying visual reasoning in AI systems"],"limitations":["Browser interaction strategies inferred from prompts, not from actual implementation","No benchmarks for interaction latency, screenshot accuracy, or DOM parsing performance","JavaScript execution and dynamic content handling details not fully documented","Browser state tracking mechanisms are tool-specific and may not generalize"],"requires":["Understanding of browser APIs and DOM manipulation","Familiarity with screenshot capture and image processing","Knowledge of web application testing frameworks"],"input_types":["system prompts describing browser interaction","web pages (HTML/CSS/JavaScript)","user interaction specifications (text)"],"output_types":["screenshots (PNG/JPEG)","DOM snapshots (JSON/HTML)","interaction logs (text/JSON)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-x1xhlol--system-prompts-and-models-of-ai-tools__cap_9","uri":"capability://safety.moderation.workspace.access.control.and.security.scanning.pattern.analysis","name":"workspace access control and security scanning pattern analysis","description":"Catalogs security and access control mechanisms in agentic IDEs: workspace isolation, file access restrictions, secrets management, and security scanning pipelines. Analyzes how tools prevent unauthorized file access, detect and redact sensitive information (API keys, credentials), and implement audit logging. Captures constraints like read-only file restrictions and how tools handle sensitive operations like deployment or credential access.","intents":["Design secure AI agent systems with appropriate access controls and audit trails","Implement secrets management and credential redaction for AI-assisted development","Build security scanning pipelines that detect sensitive information before exposure","Create workspace isolation strategies for multi-tenant AI development platforms"],"best_for":["Developers building enterprise AI development tools with security requirements","Teams implementing multi-tenant AI platforms with workspace isolation","Security researchers studying AI system security and access control"],"limitations":["Security mechanisms inferred from prompts, not from actual implementation or security audits","No independent security assessments or vulnerability disclosures documented","Secrets detection and redaction strategies are tool-specific and may not generalize","Audit logging and compliance mechanisms not fully documented"],"requires":["Understanding of security best practices and access control models","Familiarity with secrets management and credential handling","Knowledge of security scanning and threat detection"],"input_types":["system prompts describing security operations","file access requests (text)","code and configuration files (any format)"],"output_types":["security scanning reports (JSON/text)","access control decisions (allow/deny)","audit logs (structured data)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":63,"verified":false,"data_access_risk":"high","permissions":["GitHub account to access repository","Basic understanding of AI system prompts and tool calling conventions","No API keys or authentication required for read access","Understanding of tool-calling conventions (OpenAI function calling, Anthropic tool_use, etc.)","Familiarity with agentic AI patterns and task decomposition","Understanding of different LLM families and their capabilities/limitations","Familiarity with prompt engineering and model-specific optimization","Knowledge of cost and latency trade-offs in LLM selection","Understanding of domain-specific requirements (coding, research, productivity)","Familiarity with specialized tool ecosystems and integrations"],"failure_modes":["System prompts are reverse-engineered or community-contributed, not official documentation — may become stale as tools update","No guarantee of accuracy or completeness for proprietary tools that actively obfuscate their prompts","Lacks runtime behavior validation — prompts alone don't capture actual LLM model differences or fine-tuning","No structured schema validation — prompts stored as raw text without semantic parsing","Tool definitions extracted from prompts may not reflect actual API signatures or parameter constraints","No runtime execution data — cannot determine actual tool success rates or latency profiles","Execution strategy (parallel vs. sequential) inferred from prompts, not from actual system behavior logs","Tool categories are descriptive, not prescriptive — no formal schema or ontology","Model routing strategies inferred from prompts, not from actual routing logic or performance data","No benchmarks for model selection accuracy, cost savings, or quality trade-offs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9605962966823611,"quality":0.6,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-04-29T14:57:40Z"},"community":{"stars":136595,"forks":34114,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=x1xhlol--system-prompts-and-models-of-ai-tools","compare_url":"https://unfragile.ai/compare?artifact=x1xhlol--system-prompts-and-models-of-ai-tools"}},"signature":"CrTuWwOc0YfNKZRddXvOpX5QOzmB1ajUoRrSaMn0QkyPtDB0a+3WAp8yD/46NNx9zsN5QZmd9JNJj/pi/nywBg==","signedAt":"2026-06-19T22:58:43.437Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/x1xhlol--system-prompts-and-models-of-ai-tools","artifact":"https://unfragile.ai/x1xhlol--system-prompts-and-models-of-ai-tools","verify":"https://unfragile.ai/api/v1/verify?slug=x1xhlol--system-prompts-and-models-of-ai-tools","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"}}