{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-gpt-migrate","slug":"gpt-migrate","name":"GPT Migrate","type":"repo","url":"https://github.com/0xpayne/gpt-migrate","page_url":"https://unfragile.ai/gpt-migrate","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-gpt-migrate__cap_0","uri":"capability://planning.reasoning.llm.driven.codebase.analysis.and.migration.planning","name":"llm-driven codebase analysis and migration planning","description":"Analyzes source codebase structure, dependencies, and patterns using LLM prompting to understand migration requirements. Generates a migration plan by decomposing the codebase into logical units (modules, classes, functions) and mapping them to target framework/language equivalents. Uses chain-of-thought reasoning to identify breaking changes, dependency conflicts, and refactoring strategies before code generation begins.","intents":["Understand what needs to change when migrating from Django to FastAPI","Identify which parts of a React codebase map to Vue 3 equivalents","Get a structured plan for migrating from JavaScript to TypeScript","Discover hidden dependencies and coupling before starting migration"],"best_for":["Teams planning large-scale framework migrations","Developers evaluating migration feasibility before committing resources","Architects designing incremental migration strategies"],"limitations":["LLM analysis may miss domain-specific patterns or custom abstractions not well-represented in training data","Plan accuracy depends on codebase documentation and code clarity; poorly documented code produces lower-quality plans","No static analysis — relies on LLM interpretation rather than AST parsing, potentially missing subtle type or dependency issues"],"requires":["OpenAI API key or compatible LLM endpoint","Source codebase accessible as text/files","Target framework/language specification"],"input_types":["source code files","directory structure","dependency manifests (package.json, requirements.txt, etc.)"],"output_types":["migration plan (structured text)","dependency mapping","refactoring recommendations"],"categories":["planning-reasoning","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_1","uri":"capability://code.generation.editing.incremental.code.generation.with.context.preservation","name":"incremental code generation with context preservation","description":"Generates migrated code in chunks, maintaining context of previously generated files and dependencies to ensure consistency across the codebase. Uses a stateful generation loop where each file generation is informed by the migration plan and previously generated code, reducing hallucinations and improving coherence. Implements rollback and retry logic to handle LLM generation failures without corrupting the output codebase.","intents":["Generate migrated code file-by-file while maintaining cross-file consistency","Recover from LLM generation errors without losing previously generated work","Ensure generated code respects the target framework's conventions and patterns","Track which files have been successfully migrated vs. which need retry"],"best_for":["Large codebases where single-pass generation would exceed token limits","Projects requiring high code quality and consistency across files","Teams needing visibility into generation progress and partial results"],"limitations":["Context window limitations mean very large codebases may lose coherence across distant files","No built-in dependency resolution — if generated code references ungenerated modules, may produce import errors","Incremental generation adds latency; full migration of large codebases can take hours","No automatic testing of generated code; relies on post-generation validation"],"requires":["OpenAI API key or compatible LLM with sufficient context window (8k+ tokens)","Writable output directory","Migration plan from prior analysis step"],"input_types":["migration plan","source code files","target framework specification"],"output_types":["generated code files","generation log with status per file","error report for failed generations"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_10","uri":"capability://code.generation.editing.custom.transformation.rule.definition.and.application","name":"custom transformation rule definition and application","description":"Allows users to define custom transformation rules for domain-specific code patterns that the LLM may not handle correctly. Rules can specify pattern matching (regex or AST-based) and transformation logic (code templates or LLM-guided generation). Applies custom rules before or after LLM generation to handle edge cases and framework-specific patterns. Supports rule composition and ordering to handle complex transformations.","intents":["Define custom rules for proprietary code patterns in your codebase","Apply framework-specific transformations that the LLM doesn't understand","Handle legacy code patterns that don't map cleanly to target framework","Ensure consistent transformation of repeated patterns across the codebase"],"best_for":["Teams with proprietary or domain-specific code patterns","Projects with legacy code that doesn't follow standard patterns","Organizations wanting to enforce specific migration strategies"],"limitations":["Rule definition requires technical expertise; not suitable for non-technical users","Complex rules may have unintended side effects or conflicts with other rules","No built-in rule validation; incorrect rules can produce broken code","Rule maintenance burden increases as codebase evolves"],"requires":["OpenAI API key","Rule definition syntax or DSL","Source code matching rule patterns"],"input_types":["rule definitions","source code","pattern specifications"],"output_types":["transformed code","rule application log","unmatched code report"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_2","uri":"capability://code.generation.editing.multi.language.and.multi.framework.code.transformation","name":"multi-language and multi-framework code transformation","description":"Supports arbitrary source-to-target language and framework combinations by using LLM-driven semantic understanding rather than hardcoded transformation rules. Handles language-specific syntax, idioms, and framework patterns by prompting the LLM with target framework documentation and best practices. Automatically adapts to different type systems, module systems, and dependency management approaches between source and target.","intents":["Migrate Python Django app to Node.js Express","Convert JavaScript React components to Vue 3","Translate Java Spring Boot service to Python FastAPI","Port C# .NET application to Go"],"best_for":["Organizations with polyglot codebases needing framework-agnostic migration","Developers exploring language switches without investing in specialized migration tools","Startups pivoting technology stacks and needing rapid migration"],"limitations":["Quality varies significantly based on LLM familiarity with target framework; obscure or new frameworks produce lower-quality migrations","Language-specific idioms and performance patterns may not translate well; generated code may be functionally correct but non-idiomatic","No built-in handling of platform-specific code (e.g., Windows-only APIs, system calls); requires manual post-processing","Type system mismatches (e.g., Python dynamic types to TypeScript strict types) may require extensive refactoring"],"requires":["OpenAI API key","Source code in supported language","Target language/framework specification","Optional: target framework documentation for better prompting"],"input_types":["source code in any language","framework/library specifications","configuration files"],"output_types":["migrated code in target language","dependency mappings","migration notes and warnings"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_3","uri":"capability://data.processing.analysis.dependency.and.import.resolution.with.framework.mapping","name":"dependency and import resolution with framework mapping","description":"Automatically maps source framework dependencies to target framework equivalents by analyzing import statements and library usage patterns. Resolves transitive dependencies and identifies which source libraries have direct target equivalents vs. which require architectural changes. Generates updated dependency manifests (package.json, requirements.txt, etc.) for the target framework with appropriate version constraints.","intents":["Find the Express equivalent of Django's URL routing system","Map React hooks to Vue 3 composition API","Identify which npm packages replace Python pip packages","Generate correct dependency versions for the target framework"],"best_for":["Teams migrating between frameworks with different dependency ecosystems","Projects with complex dependency trees requiring careful mapping","Developers needing to understand library equivalences before migration"],"limitations":["Mapping accuracy depends on LLM knowledge; niche or proprietary libraries may not have known equivalents","No version compatibility checking; generated dependency versions may have conflicts requiring manual resolution","Transitive dependency conflicts not automatically resolved; may require manual intervention","Does not handle platform-specific dependencies (e.g., native modules, system libraries)"],"requires":["OpenAI API key","Source dependency manifests (package.json, requirements.txt, Gemfile, etc.)","Target framework specification"],"input_types":["dependency manifests","import statements from source code","framework specifications"],"output_types":["target dependency manifest","dependency mapping table","unresolved dependencies report"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_4","uri":"capability://code.generation.editing.test.generation.and.validation.for.migrated.code","name":"test generation and validation for migrated code","description":"Generates test cases for migrated code by analyzing the original source code's test suite and translating tests to the target framework's testing conventions. Validates generated code by running tests and comparing behavior against the original codebase. Identifies test failures and generates fixes or highlights areas requiring manual review.","intents":["Automatically convert Jest tests to pytest for Python migration","Validate that migrated code produces the same outputs as the original","Identify behavioral differences between source and migrated code","Generate test coverage reports for the migrated codebase"],"best_for":["Teams requiring high confidence in migration correctness","Projects with comprehensive test suites that can be migrated","Organizations with CI/CD pipelines that can run generated tests"],"limitations":["Test generation quality depends on original test clarity; poorly written tests produce poor migrations","Cannot validate tests that depend on external services or hardware without mocking setup","Test framework differences may require manual adaptation (e.g., async/await patterns differ between Jest and pytest)","No automatic generation of integration or end-to-end tests; only unit test migration"],"requires":["OpenAI API key","Source test suite","Target test framework installed","Ability to execute tests in target environment"],"input_types":["source test files","test framework specification","migrated code"],"output_types":["migrated test files","test execution results","failure report with suggested fixes"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_5","uri":"capability://text.generation.language.interactive.migration.refinement.and.user.feedback.loop","name":"interactive migration refinement and user feedback loop","description":"Provides a CLI or interactive interface where users can review generated code, request changes, and provide feedback that informs subsequent generation steps. Implements a conversation loop where users can ask clarifying questions about migration decisions, request alternative implementations, or highlight code sections needing revision. Incorporates user feedback into the generation context to improve subsequent outputs.","intents":["Review generated code and request changes before committing","Ask why a particular migration decision was made","Request alternative implementations for specific functions","Provide domain-specific guidance to improve code quality"],"best_for":["Teams wanting hands-on control over migration process","Projects with domain-specific requirements that LLM cannot infer","Developers learning from migration decisions and wanting to understand rationale"],"limitations":["Interactive loop adds significant time to migration process; not suitable for fully automated migrations","User feedback quality directly impacts output quality; poor feedback can degrade results","No built-in version control; users must manually track which feedback versions were applied","Context window limitations mean very long feedback conversations may lose earlier context"],"requires":["OpenAI API key","CLI or interactive interface (terminal or web)","User availability for feedback loop"],"input_types":["generated code","user text feedback","user requests for changes"],"output_types":["revised code","explanation of migration decisions","alternative implementations"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_6","uri":"capability://data.processing.analysis.configuration.and.environment.variable.migration","name":"configuration and environment variable migration","description":"Analyzes source configuration files (.env, config.yaml, settings.py, etc.) and generates equivalent configuration for the target framework. Maps environment variable names and configuration structures to target framework conventions. Handles differences in configuration loading mechanisms (e.g., Django settings modules vs. environment variables vs. config files) and generates appropriate configuration code for the target.","intents":["Convert Django settings.py to FastAPI environment variable configuration","Map environment variables from one framework to another","Generate .env files with correct variable names for target framework","Handle secrets management differences between frameworks"],"best_for":["Migrations involving significant configuration changes","Projects with complex environment-specific configurations","Teams needing to maintain configuration consistency across environments"],"limitations":["Framework-specific configuration patterns may not have direct equivalents; requires manual mapping for complex cases","Secrets management approaches differ significantly between frameworks; generated code may not handle secrets securely","No validation of configuration values; generated configs may have incorrect types or missing required fields","Does not handle runtime configuration changes or dynamic configuration loading"],"requires":["OpenAI API key","Source configuration files","Target framework specification"],"input_types":["configuration files (.env, .yaml, .json, .py, etc.)","environment variable documentation","target framework configuration examples"],"output_types":["target framework configuration files","environment variable mapping","configuration validation report"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_7","uri":"capability://code.generation.editing.database.schema.and.orm.migration","name":"database schema and orm migration","description":"Analyzes source database schemas and ORM models (SQLAlchemy, Django ORM, TypeORM, etc.) and generates equivalent models for the target ORM. Maps database column types, constraints, and relationships to target ORM conventions. Generates migration scripts or schema definitions for the target database system. Handles differences in ORM query syntax and relationship definitions between source and target.","intents":["Convert Django ORM models to SQLAlchemy models","Migrate TypeORM entities to Prisma schema","Generate database migration scripts for schema changes","Map complex relationships (many-to-many, polymorphic) between ORMs"],"best_for":["Migrations involving ORM framework changes","Projects with complex database schemas and relationships","Teams needing to maintain data integrity during migration"],"limitations":["ORM-specific features (custom validators, hooks, lifecycle methods) may not have direct equivalents in target ORM","Database-specific features (stored procedures, triggers, views) require manual migration","Generated migration scripts may not handle data transformation correctly; requires testing and validation","No automatic handling of data type mismatches (e.g., PostgreSQL JSON to MySQL JSON differences)"],"requires":["OpenAI API key","Source ORM models or database schema","Target ORM specification","Database system specification (PostgreSQL, MySQL, etc.)"],"input_types":["ORM model definitions","database schema (DDL)","migration files","relationship definitions"],"output_types":["target ORM models","migration scripts","schema definitions","relationship mapping"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_8","uri":"capability://code.generation.editing.api.endpoint.and.route.migration","name":"api endpoint and route migration","description":"Analyzes source API endpoints and route definitions and generates equivalent routes for the target framework. Maps HTTP method handling, request/response serialization, middleware, and authentication patterns to target framework conventions. Generates OpenAPI/Swagger documentation for migrated endpoints. Handles differences in route parameter syntax, request body parsing, and response formatting between frameworks.","intents":["Convert Django URL patterns to FastAPI route decorators","Migrate Express.js routes to Flask blueprints","Generate OpenAPI specs for migrated API endpoints","Map authentication and authorization middleware between frameworks"],"best_for":["API-heavy applications with many endpoints","Migrations between REST frameworks with different routing paradigms","Projects requiring API documentation updates during migration"],"limitations":["Custom middleware or request/response processing may not translate directly; requires manual adaptation","GraphQL or other non-REST API patterns require different migration strategies","Authentication schemes (JWT, OAuth, session-based) may require significant refactoring","Generated routes may not handle edge cases or custom error handling correctly"],"requires":["OpenAI API key","Source route/endpoint definitions","Target framework specification","API documentation or OpenAPI specs (optional)"],"input_types":["route definitions","endpoint handlers","middleware definitions","API documentation"],"output_types":["target framework routes","endpoint handlers","OpenAPI/Swagger specs","middleware mappings"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-migrate__cap_9","uri":"capability://automation.workflow.batch.migration.with.progress.tracking.and.resumability","name":"batch migration with progress tracking and resumability","description":"Supports migrating entire codebases in batch mode with persistent progress tracking, allowing migrations to be paused and resumed without losing work. Maintains a migration state database tracking which files have been processed, which failed, and which are pending. Implements retry logic with exponential backoff for failed generations. Provides progress reporting and estimated time to completion.","intents":["Migrate a 1000+ file codebase without losing progress if interrupted","Resume a failed migration from the last successful file","Monitor migration progress in real-time","Retry failed file generations with different prompts or parameters"],"best_for":["Large-scale migrations of enterprise codebases","Teams with limited API budgets needing to optimize token usage","Projects requiring visibility into migration progress and status"],"limitations":["Requires persistent storage for migration state; adds complexity to deployment","Progress tracking adds overhead; slower than single-pass generation for small codebases","Resumability depends on consistent file ordering; changes to source code during migration may cause issues","No automatic conflict resolution if source code changes during migration"],"requires":["OpenAI API key","Persistent storage for migration state (file system or database)","Source codebase","Target framework specification"],"input_types":["source codebase","migration configuration","migration state (if resuming)"],"output_types":["migrated codebase","migration state file","progress report","error log"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or compatible LLM endpoint","Source codebase accessible as text/files","Target framework/language specification","OpenAI API key or compatible LLM with sufficient context window (8k+ tokens)","Writable output directory","Migration plan from prior analysis step","OpenAI API key","Rule definition syntax or DSL","Source code matching rule patterns","Source code in supported language"],"failure_modes":["LLM analysis may miss domain-specific patterns or custom abstractions not well-represented in training data","Plan accuracy depends on codebase documentation and code clarity; poorly documented code produces lower-quality plans","No static analysis — relies on LLM interpretation rather than AST parsing, potentially missing subtle type or dependency issues","Context window limitations mean very large codebases may lose coherence across distant files","No built-in dependency resolution — if generated code references ungenerated modules, may produce import errors","Incremental generation adds latency; full migration of large codebases can take hours","No automatic testing of generated code; relies on post-generation validation","Rule definition requires technical expertise; not suitable for non-technical users","Complex rules may have unintended side effects or conflicts with other rules","No built-in rule validation; incorrect rules can produce broken code","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"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-migrate","compare_url":"https://unfragile.ai/compare?artifact=gpt-migrate"}},"signature":"b2pC5cpsr/G+H8XkGNsHpuUsXAXZN4KUSRE/BHLIuBv22EQRxG7sFeh9lby2VUeuZJylNgKnKafMcHPO3v8sDA==","signedAt":"2026-06-19T20:47:32.498Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gpt-migrate","artifact":"https://unfragile.ai/gpt-migrate","verify":"https://unfragile.ai/api/v1/verify?slug=gpt-migrate","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"}}