{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"twinny","slug":"twinny","name":"Twinny","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=rjmacarthy.twinny","page_url":"https://unfragile.ai/twinny","categories":["code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"twinny__cap_0","uri":"capability://code.generation.editing.real.time.inline.code.completion.with.fill.in.the.middle","name":"real-time inline code completion with fill-in-the-middle","description":"Generates single-line and multi-line code suggestions as developers type, using fill-in-the-middle (FIM) architecture where the model predicts code tokens between cursor position and surrounding context. Integrates directly into VS Code's IntelliSense pipeline, triggering automatically on keystroke with configurable debounce and context window management to balance latency against suggestion quality.","intents":["Get instant code suggestions while typing without breaking flow","Complete boilerplate code patterns automatically","Reduce typing effort for repetitive code structures","Explore alternative implementations inline"],"best_for":["Solo developers using local Ollama for privacy-first development","Teams with strict data residency requirements","Developers wanting Copilot-like experience without cloud dependency"],"limitations":["Latency depends on local model size and hardware — larger models (7B+) may add 200-500ms per completion on CPU-only machines","No multi-file context awareness in completion suggestions — only current file and open tabs considered","Completion quality varies significantly by model; smaller quantized models (3B-7B) show degraded accuracy vs 13B+ variants","No caching of repeated patterns — each completion request re-processes full context window"],"requires":["VS Code 1.60+ (estimated minimum, not explicitly documented)","For local mode: Ollama running locally with compatible model (Mistral, Llama 2, CodeLlama, etc.)","For cloud mode: API key for OpenAI, Anthropic, OpenRouter, Groq, or other OpenAI-compatible provider","4GB+ RAM for local inference with 7B models; 8GB+ recommended for 13B models"],"input_types":["Current file content (full or windowed)","Cursor position (line, column)","Open file tabs (optional context)","Git diff context (optional)"],"output_types":["Code string (single or multi-line)","Completion metadata (confidence, source model)"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_1","uri":"capability://text.generation.language.chat.based.code.explanation.and.refactoring","name":"chat-based code explanation and refactoring","description":"Provides a sidebar chat interface where developers can ask questions about code, request refactoring suggestions, or discuss implementation approaches. The chat maintains conversation history locally, passes selected code blocks and file context to the LLM, and renders responses with syntax-highlighted code blocks that can be accepted, diffed, or inserted into new documents. Uses stateful conversation management to preserve context across multiple turns.","intents":["Ask questions about what code does without leaving editor","Get refactoring suggestions for specific functions or modules","Discuss architectural decisions or design patterns","Understand legacy or unfamiliar code quickly"],"best_for":["Developers learning unfamiliar codebases","Teams doing code reviews with AI assistance","Solo developers wanting rubber-duck debugging with AI","Refactoring workflows where suggestions need iteration"],"limitations":["Chat context limited by model's max token window — large files or long conversations may be truncated","No automatic codebase indexing for semantic search — chat cannot reference files not explicitly selected or open","Conversation history stored locally only — no sync across devices or team sharing","Code block insertion does not validate syntax — developer must manually verify suggestions before committing"],"requires":["VS Code 1.60+ (estimated)","Active connection to LLM provider (local Ollama or cloud API with valid credentials)","Selected code block or open file for context (optional but recommended for quality)"],"input_types":["Natural language query (text)","Selected code block (code)","Current file context (code)","Conversation history (structured)"],"output_types":["Natural language response (text)","Code suggestions with syntax highlighting (code)","Structured code blocks with accept/reject/diff actions"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_10","uri":"capability://tool.use.integration.configurable.api.endpoint.and.port.management","name":"configurable api endpoint and port management","description":"Allows developers to specify custom API endpoints and ports for LLM providers, enabling connection to local Ollama instances on non-standard ports, private API gateways, or self-hosted model servers. Configuration is stored in VS Code settings and applied to all requests. Supports endpoint path customization for providers with non-standard API routes.","intents":["Connect to Ollama running on non-standard port (not 11434)","Route requests through corporate API gateway or proxy","Use self-hosted model server with custom endpoint","Test against development or staging LLM servers"],"best_for":["Developers with custom infrastructure or self-hosted models","Organizations with API gateways or proxies","Teams testing against staging LLM servers","Developers running Ollama on non-standard ports"],"limitations":["No endpoint validation before use — invalid endpoints cause silent failures or cryptic errors","No proxy authentication documented — unclear if basic auth, OAuth, or mTLS supported","Endpoint configuration stored in plaintext settings — no encryption for sensitive URLs","No endpoint health checking — cannot verify connectivity before making requests","No fallback or retry logic for endpoint failures — single point of failure"],"requires":["Custom API endpoint URL (string)","Port number (integer, 1-65535)","Optional: API endpoint path (string)"],"input_types":["Endpoint URL (string)","Port number (integer)","API path (string)"],"output_types":["Configured endpoint (used for all LLM requests)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_11","uri":"capability://safety.moderation.local.first.privacy.model.with.optional.cloud.provider.routing","name":"local-first privacy model with optional cloud provider routing","description":"Defaults to routing all AI requests through a local Ollama instance (running on localhost:11434), keeping code and context on the developer's machine by default. Developers can optionally configure cloud providers (OpenAI, Anthropic, etc.) for higher-quality models, but this is an explicit opt-in choice. This architecture prioritizes privacy by default while maintaining flexibility for users who prefer cloud inference.","intents":["Use AI code assistance without sending code to cloud providers","Maintain full control over code and context data","Comply with data residency or privacy regulations that restrict cloud transmission","Avoid vendor lock-in by keeping inference local"],"best_for":["Developers and teams with strict privacy requirements","Organizations subject to data residency regulations (e.g., GDPR, HIPAA)","Solo developers wanting to avoid cloud service costs","Teams building proprietary code that should not leave the machine"],"limitations":["Local Ollama models are typically lower quality than cloud models (e.g., CodeLlama vs GPT-4)","Requires local GPU or CPU resources to run Ollama — adds infrastructure overhead","Ollama setup and model management are not handled by Twinny — users must manage separately","Local inference is slower than cloud providers — completion latency may be 1-10+ seconds depending on hardware","Privacy claim is only valid if using local Ollama; cloud providers transmit code to external servers"],"requires":["VS Code (minimum version unknown)","Local Ollama instance running on localhost:11434 (or custom endpoint)","Sufficient local compute resources to run models (GPU recommended, CPU acceptable for smaller models)"],"input_types":["source code (current file or selection)","chat messages (natural language)"],"output_types":["completions, chat responses, or embeddings from local model"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_2","uri":"capability://memory.knowledge.workspace.aware.embeddings.for.context.aware.assistance","name":"workspace-aware embeddings for context-aware assistance","description":"Computes vector embeddings of workspace files locally to enable semantic search and context retrieval for chat and completion suggestions. When enabled, the extension indexes accessible workspace files, stores embeddings in local storage, and retrieves relevant code snippets based on semantic similarity to current context or chat queries. Uses embedding model inference (likely via Ollama or provider API) to generate dense vectors for retrieval-augmented generation (RAG) patterns.","intents":["Get suggestions informed by similar code patterns elsewhere in codebase","Find relevant code examples when refactoring","Maintain consistency with existing code style and patterns","Reduce hallucination in suggestions by grounding in actual codebase"],"best_for":["Large codebases (10k+ lines) where pattern consistency matters","Teams with established code style guidelines","Developers working on unfamiliar projects needing context","Projects with reusable components or libraries"],"limitations":["Embedding computation is one-time cost but re-indexing on large workspaces (100k+ lines) may take minutes, blocking editor responsiveness","No incremental indexing documented — full workspace re-index required on significant file changes","Embedding quality depends on model choice — smaller embedding models (384-dim) may miss semantic nuances vs larger models (1536-dim)","Storage overhead: embeddings for 10k files at 768-dim vectors ≈ 30MB+ local storage","Scope of indexing unclear — may include node_modules, build artifacts, or other non-source files, inflating index size"],"requires":["Embedding model available (via Ollama, OpenAI, or other provider)","Local storage for embedding vectors (location not documented)","Workspace with indexable files (minimum 100 files recommended for meaningful retrieval)"],"input_types":["Workspace file paths and content (code)","Current file context (code)","Chat query or completion context (text/code)"],"output_types":["Ranked list of relevant code snippets (structured)","Embedding vectors (dense numeric arrays)","Retrieved context for LLM augmentation"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.backend.abstraction","name":"multi-provider llm backend abstraction","description":"Abstracts LLM inference across 8+ providers (Ollama, OpenAI, Anthropic, OpenRouter, Deepseek, Cohere, Mistral, Groq, Perplexity) through a unified OpenAI-compatible API interface. Developers configure provider and endpoint via settings, and the extension translates all completion and chat requests to the selected provider's API format. Supports both local inference (Ollama) and cloud APIs with configurable authentication and endpoint paths.","intents":["Switch between local and cloud inference without code changes","Use cost-effective or specialized models from different providers","Maintain vendor independence and avoid lock-in","Route requests to provider with best latency or pricing for use case"],"best_for":["Developers experimenting with different models and providers","Teams with multi-cloud or hybrid infrastructure","Cost-conscious teams wanting to optimize provider selection","Organizations with vendor lock-in concerns"],"limitations":["Provider-specific features (vision, function calling, streaming parameters) not abstracted — advanced features may not work across all providers","API key management mechanism not documented — unclear if keys stored in VS Code secrets or plaintext config","No automatic provider failover — if primary provider is down, user must manually switch","Endpoint configuration requires manual URL entry — no provider discovery or validation","Rate limiting and quota management delegated to provider — no built-in throttling or retry logic documented"],"requires":["API key for selected provider (except local Ollama)","Network connectivity for cloud providers","Ollama running locally for local inference mode","Provider-specific model availability (e.g., OpenAI requires gpt-3.5-turbo or gpt-4)"],"input_types":["Provider name (string)","API endpoint URL (string)","API key or authentication token (string)","Model identifier (string)","Completion or chat request (structured)"],"output_types":["LLM response (text or code)","Streaming tokens (optional)","Usage metadata (tokens, cost estimate)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_4","uri":"capability://text.generation.language.git.commit.message.generation","name":"git commit message generation","description":"Analyzes staged changes in Git (diff between HEAD and staging area) and generates descriptive commit messages using the configured LLM. Extracts file changes, added/removed lines, and context from commit scope, then prompts the model to generate conventional commit-formatted messages. Generated messages can be accepted or edited before committing.","intents":["Generate descriptive commit messages automatically from code changes","Enforce consistent commit message formatting across team","Reduce time spent writing commit messages","Improve commit history readability and searchability"],"best_for":["Teams using conventional commits or semantic versioning","Solo developers wanting better commit history without manual effort","Projects with strict commit message standards","High-velocity teams making frequent small commits"],"limitations":["Commit message quality depends on diff clarity — large or complex diffs may generate vague messages","No access to commit history or issue tracking — cannot reference related issues or PRs","Generated messages may not follow team conventions if not explicitly trained or prompted","No validation of generated messages against commit message linter rules","Requires staged changes to be meaningful — empty or whitespace-only diffs may produce nonsensical messages"],"requires":["Git repository initialized in workspace","Staged changes (git add) before generation","Active LLM provider connection"],"input_types":["Git diff (staged changes)","File paths and change types (added, modified, deleted)","Line-level additions and deletions"],"output_types":["Commit message (text, typically 1-3 lines)","Conventional commit format (type: scope: subject)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_5","uri":"capability://text.generation.language.customizable.prompt.templates.for.completion.and.chat","name":"customizable prompt templates for completion and chat","description":"Allows developers to define custom system prompts and instruction templates for code completion and chat interactions. Templates are stored in extension settings (likely JSON or YAML format) and injected into LLM requests before user input, enabling fine-tuning of model behavior without forking the extension. Supports variable substitution for context like file path, language, or selected text.","intents":["Enforce coding standards or style guidelines in suggestions","Customize model behavior for domain-specific tasks (e.g., security-focused code review)","Adapt suggestions to team conventions or architecture patterns","Reduce hallucination by providing explicit constraints or examples"],"best_for":["Teams with strict code style or security requirements","Domain-specific development (e.g., embedded systems, cryptography)","Organizations wanting to customize AI behavior without forking","Developers experimenting with prompt engineering"],"limitations":["Template syntax and variable substitution not documented — unclear what variables are available or how to reference them","No validation of templates before injection — malformed prompts may cause LLM errors or unexpected behavior","Templates stored in plaintext settings — no encryption or access control for sensitive instructions","No template versioning or rollback — changes to templates apply immediately to all requests","Limited to system prompt customization — cannot customize response format or post-processing"],"requires":["Access to VS Code settings (settings.json or UI)","Understanding of prompt engineering and LLM behavior","Knowledge of available template variables (not documented)"],"input_types":["Template text (string with variables)","Variable values (file path, language, selected code, etc.)"],"output_types":["Injected system prompt (text)","Modified LLM request with custom instructions"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_6","uri":"capability://memory.knowledge.local.conversation.history.persistence","name":"local conversation history persistence","description":"Stores chat conversation history locally in VS Code's extension storage (likely IndexedDB or file-based), preserving multi-turn conversations across editor sessions. Conversations are indexed by timestamp or ID and can be retrieved, resumed, or cleared via UI. No cloud sync or team sharing — history remains on developer's machine only.","intents":["Resume code discussion from previous session without re-explaining context","Maintain conversation thread for iterative refactoring or debugging","Review past suggestions and decisions without re-running queries","Keep sensitive code discussions off cloud services"],"best_for":["Solo developers with privacy concerns","Teams with data residency requirements","Long-running projects requiring conversation continuity","Developers preferring local-only operation"],"limitations":["No conversation sync across devices — history only available on machine where conversation occurred","No team sharing or collaboration on conversations — each developer has isolated history","Storage location not documented — unclear if stored in VS Code extension storage, workspace, or user home directory","No conversation export or backup mechanism documented","Storage quota not specified — unclear if there are limits on conversation history size","No search or filtering of past conversations — must manually browse history"],"requires":["VS Code extension storage available (automatic)","Local disk space for conversation storage (amount not specified)"],"input_types":["Chat messages (text and code)","LLM responses (text and code)","Metadata (timestamp, model, provider)"],"output_types":["Conversation history (structured list of turns)","Conversation metadata (date, duration, tokens used)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_7","uri":"capability://code.generation.editing.code.block.rendering.and.acceptance.workflow","name":"code block rendering and acceptance workflow","description":"Renders code suggestions from chat responses with syntax highlighting, language detection, and interactive actions (accept, diff, copy, new document). When user accepts a suggestion, the extension inserts code into the current file at cursor position, or optionally creates a new document. Diff view shows changes side-by-side before acceptance, allowing review before committing.","intents":["Review suggested code changes before accepting them","Insert suggestions into editor without manual copy-paste","Create new files from code suggestions","Compare suggested code against current implementation"],"best_for":["Developers wanting to review suggestions before applying","Refactoring workflows requiring careful change review","Teams with code review processes","Developers learning from suggested code patterns"],"limitations":["No syntax validation before insertion — invalid code can be inserted and break editor","Diff view may not handle large code blocks efficiently — performance unclear for 1000+ line suggestions","No undo integration documented — unclear if accepted changes can be undone via Ctrl+Z","Code insertion does not trigger linting or formatting — inserted code may violate project style","No conflict detection — if code is inserted at wrong location, may create syntax errors"],"requires":["Active editor with open file (for insertion)","Code block in chat response with language tag"],"input_types":["Code block (text with language identifier)","Current file content (for diff)","Cursor position (for insertion)"],"output_types":["Rendered code block (HTML with syntax highlighting)","Diff view (side-by-side comparison)","Modified file content (after acceptance)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_8","uri":"capability://tool.use.integration.symmetry.network.decentralized.inference.peer.to.peer","name":"symmetry network decentralized inference (peer-to-peer)","description":"Experimental feature enabling distributed inference across peer nodes in a decentralized network, allowing developers to contribute spare compute capacity and access inference from peers. Technical implementation details are not documented, but likely involves splitting inference workload across multiple machines or sharing model weights via P2P protocol. Status and stability unclear.","intents":["Reduce inference latency by distributing computation across peers","Contribute spare compute capacity to community","Access inference without relying on centralized cloud provider","Build resilient, decentralized AI infrastructure"],"best_for":["Developers in communities with shared compute resources","Organizations wanting to avoid cloud provider dependency","Researchers experimenting with decentralized inference","High-latency regions where cloud providers are distant"],"limitations":["Technical implementation not documented — unclear how inference is distributed or how peers are discovered","Stability and reliability unknown — no SLA or uptime guarantees mentioned","Network overhead may exceed latency savings — P2P communication could be slower than direct cloud API","Security implications unclear — no documentation on peer authentication, encryption, or data privacy","Peer availability unpredictable — inference may fail if peers go offline","No incentive mechanism documented — unclear why peers would contribute compute capacity","Feature status unclear — may be experimental or abandoned"],"requires":["Network connectivity to peer nodes","Spare compute capacity to contribute (optional)","Symmetry Network client or protocol implementation (not documented)"],"input_types":["Completion or chat request (code/text)","Peer node addresses or discovery mechanism"],"output_types":["LLM response (text or code)","Inference latency and peer information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__cap_9","uri":"capability://text.generation.language.full.screen.dedicated.chat.interface","name":"full-screen dedicated chat interface","description":"Provides a full-screen chat mode separate from the sidebar, offering a distraction-free environment for extended code discussion and collaboration. Likely includes conversation history, model/provider selection, and all chat features (code block actions, context insertion) in a dedicated view. Can be toggled between sidebar and full-screen modes.","intents":["Have extended code discussions without sidebar clutter","Focus on chat interaction without editor distractions","Collaborate with AI on complex refactoring or architecture decisions","Review and iterate on suggestions in dedicated interface"],"best_for":["Developers doing intensive code review or refactoring","Pair programming with AI assistance","Learning and exploration workflows","Teams with large monitors wanting dedicated chat space"],"limitations":["Full-screen mode may reduce editor visibility — context switching between chat and code","No split-view mode documented — cannot view code and chat simultaneously in full-screen","Switching between sidebar and full-screen may lose scroll position or context","Full-screen mode may not be optimized for small screens or laptop displays"],"requires":["VS Code 1.60+ (estimated)","Active LLM provider connection"],"input_types":["Chat messages (text)","Code context (optional, from editor)"],"output_types":["Chat responses (text and code)","Conversation history"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"twinny__headline","uri":"capability://code.generation.editing.local.ai.code.completion.extension.for.vs.code","name":"local ai code completion extension for vs code","description":"Twinny is a free and open-source AI code completion extension for Visual Studio Code that offers Copilot-like autocomplete and chat features while keeping all data local to your machine, ensuring privacy and security.","intents":["best AI code completion extension","AI code assistant for VS Code","local AI code editor","open-source code completion tool","VS Code extension for AI-assisted coding"],"best_for":["developers seeking privacy","users of Visual Studio Code"],"limitations":["requires VS Code","limited to local machine data"],"requires":["Visual Studio Code installed"],"input_types":["code snippets","natural language queries"],"output_types":["code suggestions","explanations","refactoring advice"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.60+ (estimated minimum, not explicitly documented)","For local mode: Ollama running locally with compatible model (Mistral, Llama 2, CodeLlama, etc.)","For cloud mode: API key for OpenAI, Anthropic, OpenRouter, Groq, or other OpenAI-compatible provider","4GB+ RAM for local inference with 7B models; 8GB+ recommended for 13B models","VS Code 1.60+ (estimated)","Active connection to LLM provider (local Ollama or cloud API with valid credentials)","Selected code block or open file for context (optional but recommended for quality)","Custom API endpoint URL (string)","Port number (integer, 1-65535)","Optional: API endpoint path (string)"],"failure_modes":["Latency depends on local model size and hardware — larger models (7B+) may add 200-500ms per completion on CPU-only machines","No multi-file context awareness in completion suggestions — only current file and open tabs considered","Completion quality varies significantly by model; smaller quantized models (3B-7B) show degraded accuracy vs 13B+ variants","No caching of repeated patterns — each completion request re-processes full context window","Chat context limited by model's max token window — large files or long conversations may be truncated","No automatic codebase indexing for semantic search — chat cannot reference files not explicitly selected or open","Conversation history stored locally only — no sync across devices or team sharing","Code block insertion does not validate syntax — developer must manually verify suggestions before committing","No endpoint validation before use — invalid endpoints cause silent failures or cryptic errors","No proxy authentication documented — unclear if basic auth, OAuth, or mTLS supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:34.118Z","last_scraped_at":null,"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=twinny","compare_url":"https://unfragile.ai/compare?artifact=twinny"}},"signature":"yOBihkvDtYwgszgbV31lC2IW/vebunFW6XnrKkUazGxGiKMDEsPfWuiOQCU9T2GrNm+CCc2xa5DEqH+ys20pAg==","signedAt":"2026-06-20T03:08:34.128Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/twinny","artifact":"https://unfragile.ai/twinny","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}