{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-gpt-pilot","slug":"gpt-pilot","name":"GPT Pilot","type":"repo","url":"https://github.com/Pythagora-io/gpt-pilot","page_url":"https://unfragile.ai/gpt-pilot","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-gpt-pilot__cap_0","uri":"capability://code.generation.editing.multi.agent.orchestrated.code.generation.with.human.in.the.loop.feedback","name":"multi-agent orchestrated code generation with human-in-the-loop feedback","description":"Coordinates a specialized agent pipeline (Spec Writer → Architect → Tech Lead → Developer → Code Monkey → Troubleshooter) that progressively refines requirements, designs architecture, decomposes tasks, and generates implementation code. Uses a centralized Orchestrator component that manages state transitions between agents, maintains project context in SQLite/PostgreSQL, and integrates human developer feedback at each stage to validate outputs before proceeding. The system implements a 95/5 split where AI handles bulk code generation while humans provide critical oversight for architectural decisions and edge cases.","intents":["I want to generate a complete scalable application from a high-level description without writing boilerplate code","I need an AI system that asks clarifying questions about my requirements before building","I want to maintain control over architectural decisions while delegating implementation to AI","I need to iterate on generated code with AI feedback loops rather than one-shot generation"],"best_for":["Solo developers building MVPs or full-stack applications","Teams wanting to accelerate development velocity while maintaining code quality","Developers prototyping multiple project ideas rapidly"],"limitations":["Requires active human participation at decision gates — cannot run fully autonomous without developer feedback","Agent coordination adds latency between stages (each agent invocation requires LLM round-trip)","State management complexity increases with project size; large monolithic projects may exceed context windows","No built-in version control integration — requires manual git management for generated code"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, or Groq)","SQLite or PostgreSQL for state persistence","Node.js 18+ if using VS Code extension UI"],"input_types":["natural language project description","clarifying question responses","developer feedback on generated code","project configuration (tech stack preferences)"],"output_types":["complete application source code","project architecture documentation","task decomposition plans","implementation status and progress tracking"],"categories":["code-generation-editing","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_1","uri":"capability://memory.knowledge.context.aware.code.generation.with.project.wide.codebase.indexing","name":"context-aware code generation with project-wide codebase indexing","description":"Maintains an indexed representation of the entire project codebase in state management (SQLite/PostgreSQL) and implements context filtering logic that selectively includes relevant files and code snippets when generating new code. The system analyzes dependencies, imports, and semantic relationships to determine which existing code should be included in LLM prompts, reducing token usage and improving code consistency. Uses a relevance-scoring mechanism to prioritize context based on file relationships and recent modifications.","intents":["I want generated code to be consistent with existing project patterns and conventions","I need the AI to understand my project structure and avoid duplicate implementations","I want to reduce LLM token usage by only including relevant context in prompts","I need the AI to respect existing dependencies and avoid breaking changes"],"best_for":["Developers working on multi-file applications with complex interdependencies","Teams with strict token budgets for LLM API calls","Projects requiring high code consistency across modules"],"limitations":["Context filtering heuristics may miss relevant code in loosely-coupled architectures","Indexing overhead increases with project size; very large codebases (>10k files) may have slow context lookup","Semantic relevance scoring is heuristic-based and may not capture domain-specific relationships","Requires periodic re-indexing when large refactors occur"],"requires":["SQLite or PostgreSQL database for codebase index storage","File system access to project directory","Python 3.9+ for indexing logic"],"input_types":["project file structure","existing source code","dependency graphs","recent modification history"],"output_types":["filtered context snippets for LLM prompts","relevance scores for code relationships","dependency impact analysis"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_10","uri":"capability://tool.use.integration.interactive.ui.with.vs.code.extension.and.console.interfaces","name":"interactive ui with vs code extension and console interfaces","description":"Provides multiple user interfaces for interacting with the system: a VS Code extension for integrated development, a console CLI for command-line usage, and a virtual UI for automated testing. The UI Layer handles communication between the developer and the Orchestrator, presenting generated code, requesting feedback, and displaying progress. The VS Code extension integrates directly into the editor workflow, while the console interface supports scripting and CI/CD integration. All UIs communicate with the same backend Orchestrator, ensuring consistent behavior.","intents":["I want to use GPT Pilot without leaving my IDE (VS Code)","I need to run GPT Pilot from the command line for automation","I want to see real-time progress and generated code as it's created","I need to provide feedback and approve changes through an intuitive interface"],"best_for":["VS Code users wanting integrated AI development","DevOps engineers automating code generation in CI/CD pipelines","Teams using multiple IDEs or development environments"],"limitations":["VS Code extension requires VS Code 1.80+ and Node.js runtime","Console interface lacks visual feedback for complex outputs (architecture diagrams)","Real-time progress updates require WebSocket or polling, adding latency","Virtual UI for testing is limited to scripted interactions"],"requires":["VS Code 1.80+ (for extension)","Node.js 18+ (for extension runtime)","Python 3.9+ (for backend)","Network access to backend service"],"input_types":["natural language commands","project specifications","developer feedback and approvals","configuration parameters"],"output_types":["generated code display","progress updates","architectural diagrams or descriptions","task status and logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_11","uri":"capability://text.generation.language.prompt.engineering.system.with.agent.specific.templates","name":"prompt engineering system with agent-specific templates","description":"Implements a Prompt Engineering System that maintains specialized prompt templates for each agent type (Spec Writer, Architect, Tech Lead, Developer, Code Monkey, Troubleshooter, Bug Hunter, Frontend Agent). Prompts are parameterized with project context, previous decisions, and feedback history. The system uses dynamic prompt construction to include relevant code snippets, architectural decisions, and developer feedback, ensuring each agent has the necessary context without exceeding token limits. Prompt templates are versioned and can be updated to improve agent behavior.","intents":["I want each agent to have specialized knowledge for its domain","I need prompts to include relevant project context without exceeding token limits","I want to improve agent behavior by refining prompts","I need to track which prompt versions produced good results"],"best_for":["Teams wanting to optimize agent behavior through prompt engineering","Projects requiring domain-specific agent customization","Organizations with prompt engineering expertise"],"limitations":["Prompt quality directly impacts agent quality — requires expertise to improve","No automatic prompt optimization; improvements are manual","Prompt versioning adds complexity to state management","Token limit calculations are approximate and may cause truncation"],"requires":["Prompt template files (text or JSON format)","Python 3.9+ for prompt construction","LLM API access for testing prompt variations"],"input_types":["prompt templates","project context (requirements, architecture, code)","feedback history","agent-specific parameters"],"output_types":["constructed prompts with context","token count estimates","prompt versions and history"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_12","uri":"capability://automation.workflow.docker.based.isolated.execution.environment.for.generated.code","name":"docker-based isolated execution environment for generated code","description":"Provides Docker containerization for running generated code in isolated environments, preventing system contamination and enabling safe testing of untrusted generated code. The Docker Environment layer handles container creation, dependency installation, code execution, and output capture. Supports both local Docker and cloud-based container services. Generated code can be executed in containers with specific resource limits (CPU, memory) and network isolation, enabling safe testing before deployment.","intents":["I want to test generated code safely without affecting my development environment","I need to run generated code with specific dependencies and versions","I want to enforce resource limits on generated code execution","I need to capture execution logs and test results"],"best_for":["Developers concerned about security of generated code","Teams needing reproducible execution environments","Projects with complex dependency requirements"],"limitations":["Docker overhead adds 2-5 seconds per execution for container startup","Requires Docker installation and configuration","Network isolation may prevent generated code from accessing required services","Container image size can be large for projects with many dependencies"],"requires":["Docker 20.10+ installed and running","Sufficient disk space for container images","Python 3.9+ for container orchestration","Dockerfile or container image specifications"],"input_types":["generated source code","dependency specifications (requirements.txt, package.json)","execution parameters (environment variables, command-line args)","resource limits (CPU, memory)"],"output_types":["execution results and output","test results and logs","performance metrics","error messages and stack traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_13","uri":"capability://automation.workflow.cloud.deployment.integration.with.infrastructure.as.code.generation","name":"cloud deployment integration with infrastructure-as-code generation","description":"Generates deployment configurations and infrastructure-as-code (Docker Compose, Kubernetes manifests, cloud provider templates) based on the project architecture and technology stack. The system can generate deployment scripts, environment configurations, and cloud provider-specific setup (AWS, GCP, Azure). Supports both containerized and serverless deployments. Generated deployment code includes monitoring, logging, and scaling configurations appropriate to the technology stack.","intents":["I want the system to generate deployment configurations for my application","I need Docker Compose or Kubernetes manifests for my project","I want to deploy to cloud providers (AWS, GCP, Azure) without manual configuration","I need monitoring and logging setup included in deployment"],"best_for":["Teams deploying applications to cloud platforms","Projects requiring containerized or serverless deployments","Organizations wanting infrastructure-as-code for reproducibility"],"limitations":["Generated deployment configs may not match organization-specific policies","Cloud provider-specific features require manual customization","Scaling and performance tuning requires domain expertise","No automatic cost optimization for cloud deployments"],"requires":["Cloud provider credentials (AWS, GCP, Azure) if deploying","Docker installation for containerized deployments","Kubernetes CLI (kubectl) for Kubernetes deployments","Python 3.9+ for deployment generation"],"input_types":["project architecture and technology stack","deployment target (Docker, Kubernetes, cloud provider)","resource requirements (CPU, memory, storage)","environment-specific configurations"],"output_types":["Docker Compose files","Kubernetes manifests","Cloud provider templates (CloudFormation, Terraform)","deployment scripts and documentation"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_2","uri":"capability://planning.reasoning.specialized.agent.based.task.decomposition.and.planning","name":"specialized agent-based task decomposition and planning","description":"Implements specialized planning agents (Architect Agent for technology stack decisions, Tech Lead Agent for task decomposition, Developer Agent for detailed implementation planning) that progressively break down high-level requirements into concrete, implementable tasks. Each agent uses domain-specific prompts and reasoning patterns to handle its responsibility. The Tech Lead Agent specifically decomposes projects into manageable subtasks with dependency ordering, while the Architect Agent evaluates technology choices and creates system design documents. This multi-stage planning reduces hallucination and improves code quality by separating concerns.","intents":["I want the AI to choose an appropriate technology stack for my project requirements","I need the project broken down into logical, implementable tasks with clear dependencies","I want detailed implementation plans before code generation begins","I need to understand the system architecture before development starts"],"best_for":["Developers building applications with complex architectural requirements","Teams needing clear project roadmaps before implementation","Projects where technology stack selection is critical"],"limitations":["Planning stage adds latency (multiple LLM round-trips before code generation)","Task decomposition quality depends on initial requirement clarity — vague specs produce vague plans","Architect decisions may not account for team expertise or organizational constraints","No automatic re-planning if initial assumptions prove incorrect during implementation"],"requires":["LLM API access (OpenAI, Anthropic, or Groq)","Clear initial project requirements or specification","Python 3.9+ for agent orchestration"],"input_types":["natural language project requirements","technology preferences or constraints","team expertise level","performance/scalability requirements"],"output_types":["technology stack recommendations","system architecture diagrams or descriptions","task decomposition with dependency graphs","implementation roadmaps"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.dynamic.model.selection","name":"multi-provider llm abstraction with dynamic model selection","description":"Provides a unified LLM client interface that abstracts across multiple providers (OpenAI, Anthropic, Groq) and supports dynamic model selection based on task requirements. The LLM Client Architecture layer handles provider-specific API differences, token counting, and cost optimization. Agents can specify preferred models or let the system select based on context window requirements, cost constraints, or latency needs. Supports both synchronous and asynchronous LLM calls with configurable retry logic and fallback providers.","intents":["I want to use different LLM providers without rewriting agent code","I need to optimize costs by selecting cheaper models for simple tasks","I want automatic fallback if one LLM provider is unavailable","I need to handle long-context tasks that exceed some models' limits"],"best_for":["Teams wanting flexibility in LLM provider selection","Cost-conscious developers needing to optimize API spending","Projects requiring high availability with provider redundancy"],"limitations":["Provider abstraction adds ~50-100ms latency per call due to wrapper overhead","Model-specific behaviors (output formatting, reasoning patterns) may vary across providers","Token counting is approximate and may not match actual provider billing","Fallback logic requires credentials for multiple providers"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, or Groq)","Python 3.9+ with async/await support","Network access to LLM provider APIs"],"input_types":["prompts (text)","model selection criteria (cost, latency, context window)","configuration parameters (temperature, max_tokens)"],"output_types":["LLM completions (text)","token usage metrics","provider metadata (model name, latency)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_4","uri":"capability://code.generation.editing.iterative.code.generation.with.developer.feedback.integration","name":"iterative code generation with developer feedback integration","description":"Implements a feedback loop where generated code is presented to the developer, who can provide corrections, request changes, or approve implementation. The system captures this feedback as structured input (approval, rejection with reasons, specific change requests) and feeds it back into the agent pipeline. The Troubleshooter and Bug Hunter agents specifically handle code issues and generate fixes based on developer-reported problems. State management tracks feedback history and uses it to inform subsequent generation attempts, enabling iterative refinement without full regeneration.","intents":["I want to review generated code before it's committed and request changes","I need the AI to fix bugs I discover in generated code","I want to provide feedback that improves subsequent code generation","I need to maintain control over the development process while delegating implementation"],"best_for":["Developers who want active control over code generation","Teams with specific code quality or style standards","Projects where generated code requires domain-specific adjustments"],"limitations":["Feedback loop adds latency — each iteration requires new LLM calls","Feedback quality directly impacts code quality — vague feedback produces vague fixes","No automatic learning — feedback doesn't improve future generations for other projects","Requires active developer participation; cannot run unattended"],"requires":["Interactive UI (VS Code extension, console, or web interface)","Developer availability to review and provide feedback","LLM API access for iterative generation"],"input_types":["generated code","developer approval/rejection","specific change requests","bug reports with reproduction steps"],"output_types":["revised code implementations","bug fixes","updated architecture or task plans"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_5","uri":"capability://code.generation.editing.project.template.system.with.technology.specific.scaffolding","name":"project template system with technology-specific scaffolding","description":"Provides pre-configured project templates for common application types (Vite React, backend APIs, etc.) that include technology-specific scaffolding, dependency configurations, and architectural patterns. Templates are stored as blueprint files that the system uses to initialize new projects with appropriate directory structures, configuration files, and starter code. The Architect Agent can select templates based on technology stack decisions, accelerating project setup. Templates include build configurations, testing frameworks, and deployment scripts specific to each technology stack.","intents":["I want to start a new project with appropriate directory structure and configuration","I need technology-specific boilerplate (build configs, testing setup, deployment scripts)","I want to ensure projects follow consistent architectural patterns","I need to reduce setup time for common project types"],"best_for":["Teams building multiple projects with similar tech stacks","Developers wanting to standardize project structure","Organizations with specific architectural patterns to enforce"],"limitations":["Templates are static — don't automatically update when dependencies change","Limited to pre-defined template types; custom stacks require manual template creation","Template selection is based on Architect Agent recommendations, which may not match team preferences","No built-in template versioning or migration path for template updates"],"requires":["Template files in project repository","File system access to create project structure","Technology-specific tools (Node.js for React, Python for backend, etc.)"],"input_types":["technology stack selection","project type (frontend, backend, full-stack)","custom configuration parameters"],"output_types":["initialized project directory structure","configuration files (package.json, pyproject.toml, etc.)","starter code and examples"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_6","uri":"capability://memory.knowledge.persistent.project.state.management.with.sqlite.postgresql.backend","name":"persistent project state management with sqlite/postgresql backend","description":"Maintains complete project state across agent invocations using a relational database (SQLite for local development, PostgreSQL for production). Stores project metadata, generated code, agent conversation history, developer feedback, task status, and architectural decisions. The State Management System provides a unified interface for agents to query and update state, enabling agents to access project context without re-reading files. Supports both synchronous and asynchronous database operations with connection pooling for concurrent agent access.","intents":["I want the system to remember project context across multiple sessions","I need to track which tasks are complete and which are in progress","I want to maintain a history of architectural decisions and code changes","I need to enable multiple agents to work on the same project concurrently"],"best_for":["Long-running projects requiring persistent state across sessions","Teams with multiple developers or agents working on the same project","Projects requiring audit trails of decisions and changes"],"limitations":["Database schema must be migrated when system architecture changes","SQLite has limited concurrent write support; PostgreSQL required for high-concurrency scenarios","State bloat over time as conversation history and feedback accumulates","No built-in state export/import for migrating between projects"],"requires":["SQLite (included with Python) or PostgreSQL 12+","Writable file system (for SQLite) or database server access (for PostgreSQL)","Python 3.9+ with database driver (psycopg2 for PostgreSQL)"],"input_types":["project metadata (name, description, tech stack)","generated code and file contents","agent outputs and conversation history","developer feedback and approvals"],"output_types":["project state snapshots","task status and progress","architectural decision logs","code change history"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_7","uri":"capability://code.generation.editing.frontend.specific.code.generation.with.ui.framework.support","name":"frontend-specific code generation with ui framework support","description":"Implements a specialized Frontend Development Agent that handles UI-specific code generation for frameworks like React (with Vite). This agent understands component hierarchies, state management patterns, styling approaches, and frontend-specific testing. It generates not just component code but also routing configurations, state management setup, and integration with backend APIs. The agent uses frontend-specific prompts and context filtering to understand existing UI patterns and maintain design consistency.","intents":["I want the AI to generate React components that match my project's design patterns","I need the system to handle routing, state management, and API integration for frontends","I want generated UI code to be testable and follow accessibility standards","I need the AI to understand my existing component library and reuse components"],"best_for":["Full-stack developers building React applications","Teams with complex frontend architectures requiring specialized generation","Projects with strict UI/UX standards that need to be maintained"],"limitations":["Frontend Agent is specialized for React/Vite; other frameworks require custom agents","Generated components may not match designer specifications without explicit design input","State management patterns (Redux, Zustand, Context) must be pre-configured","Accessibility compliance depends on framework and component library capabilities"],"requires":["Node.js 18+ for React/Vite projects","React and related dependencies (react-router, state management library)","LLM API access for frontend-specific generation"],"input_types":["UI requirements and component specifications","existing component library or design system","API endpoint specifications","routing and navigation requirements"],"output_types":["React component code (JSX/TSX)","routing configurations","state management setup","styling (CSS/Tailwind/styled-components)","API integration code"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_8","uri":"capability://code.generation.editing.quality.assurance.and.bug.detection.with.specialized.qa.agents","name":"quality assurance and bug detection with specialized qa agents","description":"Implements specialized Quality Assurance Agents (Bug Hunter, Troubleshooter) that analyze generated code for potential issues, test coverage gaps, and architectural problems. The Bug Hunter Agent reviews code for common bug patterns, security vulnerabilities, and performance issues. The Troubleshooter Agent helps diagnose and fix issues reported by developers. These agents use code analysis patterns and domain knowledge to identify problems without requiring full test execution, reducing feedback latency.","intents":["I want the system to catch bugs and issues before I review generated code","I need security vulnerabilities identified in generated code","I want to understand why generated code isn't working and get fixes","I need test coverage analysis and recommendations"],"best_for":["Teams with strict quality requirements","Projects where security is critical","Developers wanting to reduce manual code review burden"],"limitations":["Static analysis cannot catch all runtime issues — requires actual testing","Bug detection quality depends on agent training and prompt quality","No integration with actual test runners; recommendations are heuristic-based","Security analysis is pattern-based and may miss novel vulnerability types"],"requires":["LLM API access for QA agent analysis","Generated code in analyzable format (source files)","Optional: test framework configuration for coverage analysis"],"input_types":["generated source code","error messages and stack traces","test coverage reports","security scanning results"],"output_types":["bug reports with severity levels","security vulnerability findings","test coverage gaps and recommendations","fixed code implementations"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-pilot__cap_9","uri":"capability://tool.use.integration.configuration.driven.llm.behavior.customization","name":"configuration-driven llm behavior customization","description":"Provides a configuration system (JSON-based, environment variable support) that allows customization of LLM behavior, model selection, temperature settings, token limits, and provider preferences without code changes. Configuration is loaded at startup and can be overridden per-project. Supports environment variable expansion for sensitive credentials (API keys). The Configuration System layer abstracts provider-specific settings and enables different configurations for different project types or development stages.","intents":["I want to use different LLM models for different tasks (cheaper for simple tasks, more capable for complex ones)","I need to control LLM creativity/determinism via temperature settings","I want to set token budgets to control costs","I need to switch between LLM providers without code changes"],"best_for":["Teams managing multiple projects with different LLM requirements","Cost-conscious developers needing fine-grained cost control","Organizations with security policies requiring credential management"],"limitations":["Configuration changes require system restart (no hot-reload)","Complex configuration hierarchies can be error-prone","No validation of configuration values at load time; errors appear at runtime","Environment variable expansion is basic; no support for complex templating"],"requires":["Configuration file (JSON format) or environment variables","Python 3.9+ for configuration parsing","LLM API keys in environment or configuration"],"input_types":["JSON configuration files","environment variables","command-line arguments"],"output_types":["parsed configuration objects","provider-specific settings","model selection parameters"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, or Groq)","SQLite or PostgreSQL for state persistence","Node.js 18+ if using VS Code extension UI","SQLite or PostgreSQL database for codebase index storage","File system access to project directory","Python 3.9+ for indexing logic","VS Code 1.80+ (for extension)","Node.js 18+ (for extension runtime)","Python 3.9+ (for backend)"],"failure_modes":["Requires active human participation at decision gates — cannot run fully autonomous without developer feedback","Agent coordination adds latency between stages (each agent invocation requires LLM round-trip)","State management complexity increases with project size; large monolithic projects may exceed context windows","No built-in version control integration — requires manual git management for generated code","Context filtering heuristics may miss relevant code in loosely-coupled architectures","Indexing overhead increases with project size; very large codebases (>10k files) may have slow context lookup","Semantic relevance scoring is heuristic-based and may not capture domain-specific relationships","Requires periodic re-indexing when large refactors occur","VS Code extension requires VS Code 1.80+ and Node.js runtime","Console interface lacks visual feedback for complex outputs (architecture diagrams)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=gpt-pilot","compare_url":"https://unfragile.ai/compare?artifact=gpt-pilot"}},"signature":"QtdZu6F2oqtM7+8IYYZYMnSe5lbtO9Fc1eEXYPUPE9oxJTERK8MPWqiIR/Atz2qr4PjNCXlURLx5/px3LrUSAQ==","signedAt":"2026-06-21T15:51:14.690Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gpt-pilot","artifact":"https://unfragile.ai/gpt-pilot","verify":"https://unfragile.ai/api/v1/verify?slug=gpt-pilot","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"}}