{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-twinnydotdev--twinny","slug":"twinnydotdev--twinny","name":"twinny","type":"extension","url":"https://twinny.dev","page_url":"https://unfragile.ai/twinnydotdev--twinny","categories":["code-editors"],"tags":["artificial-intelligence","code-chat","code-completion","code-generation","codellama","copilot","free","llama2","llamacpp","ollama","ollama-api","ollama-chat","private","symmetry","vscode-extension"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-twinnydotdev--twinny__cap_0","uri":"capability://code.generation.editing.fill.in.the.middle.fim.code.completion.with.context.aware.suggestions","name":"fill-in-the-middle (fim) code completion with context-aware suggestions","description":"Generates real-time code suggestions by analyzing both prefix (code before cursor) and suffix (code after cursor) context using model-specific FIM templates. The system formats prompts with proper stop tokens for different AI models (Ollama, OpenAI, Anthropic, CodeLlama) and streams completions as the developer types, enabling structurally-aware code generation that understands bidirectional context rather than just left-to-right prediction.","intents":["I want AI code suggestions that understand the full context of where I'm typing, not just what came before","I need completions that work with my local Ollama instance without sending code to the cloud","I want to use different AI models (CodeLlama, Mistral, etc.) with automatic prompt formatting for each"],"best_for":["developers prioritizing code privacy with local model deployment","teams using Ollama or self-hosted LLM infrastructure","developers wanting free Copilot-like functionality without subscription"],"limitations":["FIM template system requires model-specific configuration; unsupported models may produce lower-quality completions","Completion quality depends on local model size and VRAM; smaller models (7B) may have higher latency or lower accuracy than cloud alternatives","No built-in caching of completions across sessions; each keystroke triggers a new inference request"],"requires":["VS Code 1.80+","Node.js 18+ for extension runtime","Local LLM provider (Ollama, LM Studio) OR API key for OpenAI/Anthropic","Minimum 4GB VRAM for local models (8GB+ recommended for 13B+ models)"],"input_types":["source code (prefix context before cursor)","source code (suffix context after cursor)","file path/language identifier for syntax-aware formatting"],"output_types":["code snippet (single or multi-line completion)","streaming text tokens"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_1","uri":"capability://tool.use.integration.multi.provider.ai.backend.abstraction.with.unified.configuration","name":"multi-provider ai backend abstraction with unified configuration","description":"Abstracts multiple AI provider APIs (Ollama, OpenAI, Anthropic, LM Studio, Hugging Face) behind a BaseProvider interface, allowing developers to switch providers via VS Code settings without code changes. The Provider Manager handles authentication, endpoint configuration, model selection, and request/response translation, enabling a single extension to work with local inference servers, commercial APIs, and custom endpoints through a unified configuration UI.","intents":["I want to switch between local Ollama and OpenAI API without reconfiguring the extension","I need to use a custom LLM endpoint that's OpenAI-compatible","I want to manage API keys and provider settings through VS Code UI, not config files"],"best_for":["teams with hybrid infrastructure (local + cloud AI models)","developers evaluating multiple AI providers","organizations with custom LLM deployments needing VS Code integration"],"limitations":["Provider switching requires VS Code settings reload; no hot-swapping during active completion requests","API response format normalization adds ~50-100ms overhead per request for non-native providers","No built-in fallback mechanism if primary provider is unavailable; requires manual provider switching"],"requires":["VS Code 1.80+","API key or endpoint URL for at least one provider (Ollama, OpenAI, Anthropic, etc.)","Network connectivity for cloud providers; local network access for Ollama"],"input_types":["provider configuration (type, endpoint, API key, model name)","completion request (prompt, temperature, max tokens)"],"output_types":["normalized completion response (text, tokens, finish_reason)","provider metadata (model name, latency)"],"categories":["tool-use-integration","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_10","uri":"capability://text.generation.language.documentation.and.docstring.generation.for.code","name":"documentation and docstring generation for code","description":"Analyzes selected code (functions, classes, modules) and generates documentation strings (docstrings, JSDoc comments) using the AI model with a documentation template. The system extracts code structure and purpose, passes it to the AI with documentation format specifications, and returns formatted documentation that can be inserted above code definitions, enabling developers to quickly add comprehensive documentation without manual writing.","intents":["I want to generate JSDoc comments for my JavaScript functions","I need docstrings for Python functions with parameter and return type documentation","I want to improve code documentation without spending time writing comments manually"],"best_for":["developers improving code documentation in existing projects","teams enforcing documentation standards","developers working with multiple documentation formats (JSDoc, Sphinx, etc.)"],"limitations":["Generated documentation may be generic or miss important implementation details","Documentation quality depends on AI model understanding of code purpose and semantics","No automatic validation that documentation matches actual code behavior","Complex functions with multiple code paths may produce incomplete documentation","Documentation format must be specified in template; no automatic format detection"],"requires":["VS Code 1.80+","Code selection in editor","Active AI provider configured","Documentation format specification in template (JSDoc, Sphinx, etc.)"],"input_types":["selected source code (function, class, or module)","documentation format identifier","documentation template"],"output_types":["documentation string (docstring or comment block)","formatted documentation with parameters, return types, examples"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_11","uri":"capability://code.generation.editing.real.time.streaming.code.completion.with.latency.optimization","name":"real-time streaming code completion with latency optimization","description":"Streams code completion tokens in real-time as they are generated by the AI model, displaying suggestions to the user with minimal latency. The system manages streaming connections, buffers tokens for display, and handles connection interruptions gracefully, enabling responsive code completion that feels natural and doesn't block the editor while waiting for full responses.","intents":["I want code suggestions to appear as the AI generates them, not after the full response is ready","I need responsive completion that doesn't freeze my editor while waiting for the model","I want to see partial suggestions and accept them early if they're good enough"],"best_for":["developers using local Ollama or streaming-capable APIs","users with slower network connections who benefit from early suggestions","developers wanting responsive, interactive code completion experience"],"limitations":["Streaming adds complexity to error handling; partial completions may be displayed if connection fails mid-stream","Token buffering adds ~50-100ms latency before first token appears; not true real-time","Not all AI providers support streaming; fallback to non-streaming mode may be slower","Streaming connections consume more bandwidth than batch requests; may impact data usage"],"requires":["VS Code 1.80+","AI provider with streaming support (Ollama, OpenAI, Anthropic, etc.)","Stable network connection for streaming"],"input_types":["completion request (prompt, model, parameters)","streaming configuration (buffer size, timeout)"],"output_types":["token stream (individual tokens as they arrive)","complete completion string (accumulated tokens)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_12","uri":"capability://text.generation.language.language.aware.syntax.highlighting.and.code.formatting.in.chat.messages","name":"language-aware syntax highlighting and code formatting in chat messages","description":"Renders code snippets in chat messages with syntax highlighting appropriate to the detected programming language, and formats code blocks with proper indentation and line breaks. The system detects language from code context or explicit language tags, applies syntax highlighting rules, and preserves code structure for readability in the chat interface, enabling clear code discussion without formatting degradation.","intents":["I want code snippets in chat to be syntax-highlighted so they're easy to read","I need proper code formatting in chat messages to preserve indentation and structure","I want to discuss code in chat without losing readability due to plain text formatting"],"best_for":["developers discussing code in the chat interface","teams using chat for code review and collaboration","developers wanting readable code examples in conversations"],"limitations":["Syntax highlighting quality depends on language detection accuracy; misdetected languages produce wrong colors","Complex code with nested structures may not format perfectly in chat UI","No support for interactive code editing in chat; code is display-only","Limited to languages supported by the syntax highlighting library; obscure languages may not highlight"],"requires":["VS Code 1.80+","Chat message rendering component with syntax highlighting support","Language detection logic (automatic or explicit language tags)"],"input_types":["code snippet string","programming language identifier (optional)"],"output_types":["HTML-formatted code block with syntax highlighting","rendered message component"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_2","uri":"capability://memory.knowledge.workspace.embeddings.and.semantic.context.retrieval.for.improved.completion.accuracy","name":"workspace embeddings and semantic context retrieval for improved completion accuracy","description":"Builds a vector database of workspace files using embeddings, enabling semantic search to retrieve relevant code context for completions. The system indexes workspace files on activation, stores embeddings locally, and retrieves the most similar code snippets based on semantic similarity rather than keyword matching, improving completion relevance by providing the model with contextually similar code examples from the codebase.","intents":["I want code completions that understand patterns from my existing codebase, not just generic suggestions","I need to retrieve similar code snippets from my project to use as context for the AI model","I want embeddings indexed locally so my code never leaves my machine"],"best_for":["developers working in large codebases with consistent patterns","teams prioritizing code privacy with local embedding storage","projects where domain-specific code patterns are critical for completion quality"],"limitations":["Initial indexing of large workspaces (10k+ files) can take 5-10 minutes; incremental updates required for new files","Embedding quality depends on the embedding model used; smaller models may miss semantic relationships","Vector database stored locally in VS Code storage; no built-in sync across machines or team members","Retrieval adds ~100-200ms latency per completion request for semantic search"],"requires":["VS Code 1.80+","Embedding model (local or API-based)","Sufficient disk space for vector database (typically 100MB-1GB depending on codebase size)","Initial indexing time proportional to workspace size"],"input_types":["workspace file paths and content","completion context (prefix/suffix code)","similarity threshold for retrieval"],"output_types":["ranked list of similar code snippets","embedding vectors","relevance scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_3","uri":"capability://text.generation.language.interactive.ai.chat.sidebar.with.code.context.and.multi.turn.conversation","name":"interactive ai chat sidebar with code context and multi-turn conversation","description":"Provides a VS Code sidebar chat interface (SidebarProvider) that maintains multi-turn conversation history with the AI model while allowing users to reference selected code, ask questions about code, and execute AI-powered code transformations. The chat component manages conversation state, renders messages with syntax highlighting, and integrates with the completion provider to enable contextual discussions about code without leaving the editor.","intents":["I want to ask the AI questions about my code without switching to a separate window","I need to refactor code, add types, or generate tests by chatting with the AI","I want to maintain conversation history and reference previous messages in the same session"],"best_for":["developers who prefer interactive chat over passive code suggestions","teams using AI for code review, refactoring, and documentation","developers wanting to explore multiple solutions through conversation"],"limitations":["Chat history is session-scoped; no persistence across VS Code restarts without manual export","Large conversation histories (100+ messages) may impact sidebar rendering performance","Code context is limited to selected text or current file; no automatic multi-file context gathering","No built-in conversation branching; each message extends a linear conversation thread"],"requires":["VS Code 1.80+","Active AI provider configured (Ollama, OpenAI, Anthropic, etc.)","Sufficient context window in the model (8K+ tokens recommended for multi-turn conversations)"],"input_types":["user text message","selected code snippet (optional)","conversation history (previous messages)"],"output_types":["AI response text","formatted message with syntax highlighting","conversation state (message history, metadata)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_4","uri":"capability://code.generation.editing.customizable.prompt.templates.for.code.generation.tasks","name":"customizable prompt templates for code generation tasks","description":"Provides user-configurable prompt templates for common code generation tasks (refactoring, type addition, test generation, documentation, git commit messages) that can be customized via VS Code settings. The template system uses placeholder variables (e.g., {code}, {language}) that are substituted at runtime, enabling developers to define task-specific prompts without modifying extension code and ensuring consistent prompt formatting across different AI models.","intents":["I want to customize the prompts used for code refactoring, test generation, and documentation","I need task-specific templates that match my team's coding standards and conventions","I want to define custom code generation tasks beyond the built-in ones"],"best_for":["teams with specific coding standards or documentation requirements","developers wanting to fine-tune AI behavior for domain-specific tasks","organizations using custom prompt engineering for better model performance"],"limitations":["Template customization requires manual JSON editing in VS Code settings; no visual template builder","Invalid template syntax (missing placeholders, malformed JSON) can cause silent failures or poor completions","No template versioning or rollback mechanism; changes apply immediately to all tasks","Template variables are limited to predefined set ({code}, {language}, etc.); no dynamic variable injection"],"requires":["VS Code 1.80+","Access to VS Code settings (settings.json or UI)","Understanding of template variable syntax and AI prompt engineering"],"input_types":["template string with placeholder variables","task context (selected code, file language, etc.)"],"output_types":["substituted prompt string ready for AI model","task-specific completion request"],"categories":["code-generation-editing","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_5","uri":"capability://tool.use.integration.symmetry.peer.to.peer.network.for.distributed.ai.inference.resource.sharing","name":"symmetry peer-to-peer network for distributed ai inference resource sharing","description":"Integrates with the Symmetry P2P network (SymmetryService) to enable developers to share AI inference resources across a distributed network of peers. The system allows users to contribute their local compute resources (GPU/CPU) to the network and access inference from other peers, creating a decentralized alternative to centralized cloud AI services while maintaining privacy through peer-to-peer communication.","intents":["I want to share my GPU resources with other developers in exchange for access to their compute","I need a decentralized AI inference network that doesn't rely on centralized cloud providers","I want to reduce inference costs by leveraging peer resources instead of paying for cloud APIs"],"best_for":["developers with spare GPU/CPU capacity willing to share resources","communities building decentralized AI infrastructure","teams in regions with limited cloud provider access or high API costs"],"limitations":["Peer discovery and network stability depend on P2P network health; no guaranteed availability like centralized services","Inference latency varies based on peer location and network conditions; may be higher than local inference","No built-in reputation or trust system; malicious peers could provide incorrect completions","Resource contribution requires always-on machine; not suitable for laptop-only developers","Network bandwidth usage can be significant for large models; requires stable internet connection"],"requires":["VS Code 1.80+","Symmetry network client installed and configured","Network connectivity and firewall rules allowing P2P communication","Optional: GPU/CPU resources to contribute to the network"],"input_types":["completion request (prompt, model, parameters)","peer network configuration (node ID, bootstrap peers)"],"output_types":["completion response from peer node","network metrics (latency, peer availability)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_6","uri":"capability://code.generation.editing.git.commit.message.generation.from.code.changes","name":"git commit message generation from code changes","description":"Analyzes staged or selected code changes and generates contextually appropriate git commit messages using the AI model. The system extracts diff information, passes it to the AI with a commit message template, and returns a suggested message that summarizes the changes, enabling developers to quickly generate meaningful commit messages without manual composition.","intents":["I want the AI to generate commit messages based on my code changes","I need commit messages that follow my team's conventions and style","I want to speed up the commit workflow by automating message generation"],"best_for":["developers working in git repositories with frequent commits","teams enforcing commit message standards","developers wanting to reduce time spent writing commit messages"],"limitations":["Generated messages may miss important context if diff is too large or complex","No integration with git hooks; requires manual invocation from VS Code command palette","Message quality depends on AI model understanding of code semantics; smaller models may produce generic messages","No built-in validation against team commit message standards; requires manual review"],"requires":["VS Code 1.80+","Git repository initialized in workspace","Active AI provider configured","Staged changes in git index"],"input_types":["git diff (staged changes)","commit message template (customizable)"],"output_types":["suggested commit message string","alternative message suggestions (optional)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_7","uri":"capability://code.generation.editing.code.refactoring.and.transformation.via.ai.powered.suggestions","name":"code refactoring and transformation via ai-powered suggestions","description":"Enables developers to select code and request AI-powered refactoring transformations (simplification, optimization, style changes) through the chat interface or command palette. The system passes selected code with a refactoring template to the AI model, receives the transformed code, and displays it for review before applying changes, enabling safe code transformations with human oversight.","intents":["I want to refactor selected code to improve readability or performance","I need to apply consistent code style or patterns across my codebase","I want to explore alternative implementations of a function"],"best_for":["developers working on code cleanup and modernization","teams enforcing code style standards","developers learning alternative coding patterns"],"limitations":["Refactoring suggestions may not preserve original behavior; requires manual testing and review","Large code blocks (500+ lines) may exceed model context window; requires splitting into smaller chunks","No automatic testing of refactored code; developers must verify correctness manually","Refactoring quality depends on AI model understanding of language semantics; may produce suboptimal changes"],"requires":["VS Code 1.80+","Code selection in editor","Active AI provider configured","Sufficient model context window for code + refactoring template"],"input_types":["selected source code","refactoring instruction (template or custom request)","programming language identifier"],"output_types":["refactored code snippet","explanation of changes (optional)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_8","uri":"capability://code.generation.editing.automatic.type.annotation.generation.for.dynamically.typed.code","name":"automatic type annotation generation for dynamically-typed code","description":"Analyzes selected code (typically JavaScript/TypeScript or Python) and generates appropriate type annotations using the AI model. The system extracts code context, passes it to the AI with a type annotation template, and returns suggested type definitions that can be inserted into the code, enabling developers to add type safety to dynamically-typed code without manual annotation.","intents":["I want to add TypeScript types to my JavaScript code automatically","I need to generate type definitions for function parameters and return values","I want to improve code type safety without manually writing all annotations"],"best_for":["developers migrating JavaScript to TypeScript","teams adding type safety to existing codebases","developers working with dynamically-typed languages (Python, JavaScript)"],"limitations":["Generated types may be overly generic or incorrect if code semantics are ambiguous","Type inference quality depends on AI model understanding of language type systems","No validation of generated types against actual runtime behavior; requires manual testing","Complex types (unions, generics, conditional types) may not be generated correctly"],"requires":["VS Code 1.80+","Code selection in editor","Active AI provider configured","Support for target language type system (TypeScript, Python, etc.)"],"input_types":["selected source code","programming language identifier","type annotation template"],"output_types":["type annotation string (e.g., ': string', ': number | null')","full typed function signature (optional)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-twinnydotdev--twinny__cap_9","uri":"capability://code.generation.editing.test.case.generation.from.source.code","name":"test case generation from source code","description":"Analyzes selected code (functions, classes, modules) and generates unit test cases using the AI model with a test generation template. The system extracts code structure, passes it to the AI with testing framework specifications, and returns test code that can be inserted into test files, enabling developers to quickly generate comprehensive test coverage without manual test writing.","intents":["I want to generate unit tests for my functions automatically","I need test cases that cover common use cases and edge cases","I want to improve test coverage without spending time writing tests manually"],"best_for":["developers improving test coverage in existing codebases","teams enforcing test-driven development practices","developers working with multiple testing frameworks (Jest, Pytest, etc.)"],"limitations":["Generated tests may not cover all edge cases or error conditions","Test quality depends on AI model understanding of testing best practices and code semantics","No automatic test execution or validation; generated tests must be reviewed and run manually","Complex functions with side effects or external dependencies may produce incomplete tests","Test framework and assertion style must be specified in template; no automatic framework detection"],"requires":["VS Code 1.80+","Code selection in editor","Active AI provider configured","Test framework specification in template (Jest, Pytest, etc.)"],"input_types":["selected source code (function, class, or module)","test framework identifier","test generation template"],"output_types":["test code snippet","test file content (multiple test cases)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.80+","Node.js 18+ for extension runtime","Local LLM provider (Ollama, LM Studio) OR API key for OpenAI/Anthropic","Minimum 4GB VRAM for local models (8GB+ recommended for 13B+ models)","API key or endpoint URL for at least one provider (Ollama, OpenAI, Anthropic, etc.)","Network connectivity for cloud providers; local network access for Ollama","Code selection in editor","Active AI provider configured","Documentation format specification in template (JSDoc, Sphinx, etc.)","AI provider with streaming support (Ollama, OpenAI, Anthropic, etc.)"],"failure_modes":["FIM template system requires model-specific configuration; unsupported models may produce lower-quality completions","Completion quality depends on local model size and VRAM; smaller models (7B) may have higher latency or lower accuracy than cloud alternatives","No built-in caching of completions across sessions; each keystroke triggers a new inference request","Provider switching requires VS Code settings reload; no hot-swapping during active completion requests","API response format normalization adds ~50-100ms overhead per request for non-native providers","No built-in fallback mechanism if primary provider is unavailable; requires manual provider switching","Generated documentation may be generic or miss important implementation details","Documentation quality depends on AI model understanding of code purpose and semantics","No automatic validation that documentation matches actual code behavior","Complex functions with multiple code paths may produce incomplete documentation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.29748088813119244,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:37.060Z","last_commit":"2025-08-07T10:12:07Z"},"community":{"stars":3622,"forks":221,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=twinnydotdev--twinny","compare_url":"https://unfragile.ai/compare?artifact=twinnydotdev--twinny"}},"signature":"5p+K760Nva8qquskevrJZjpTpO20QgjDBUYwgdOGPUbQ1MHIfS5iOwrv/i6OIIV25lem27TuhowUUYNTmmMcAQ==","signedAt":"2026-06-21T19:42:21.245Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/twinnydotdev--twinny","artifact":"https://unfragile.ai/twinnydotdev--twinny","verify":"https://unfragile.ai/api/v1/verify?slug=twinnydotdev--twinny","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"}}