{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-dyad-sh--dyad","slug":"dyad-sh--dyad","name":"dyad","type":"product","url":"https://dyad.sh","page_url":"https://unfragile.ai/dyad-sh--dyad","categories":["app-builders"],"tags":["ai-app-builder","anthropic","artificial-intelligence","bolt","deepseek","gemini","generative-ai","github","llm","llms","lovable","nextjs","ollama","openai","qwen","react","typescript","v0","vercel"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-dyad-sh--dyad__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.streaming.chat","name":"multi-provider llm integration with streaming chat","description":"Dyad abstracts multiple AI providers (OpenAI, Anthropic, Google Gemini, DeepSeek, Qwen, local Ollama) through a unified Language Model Provider System that handles authentication, request formatting, and streaming response parsing. The system uses provider-specific API clients and normalizes outputs to a common message format, enabling users to switch models mid-project without code changes. Chat streaming is implemented via IPC channels that pipe token-by-token responses from the main process to the renderer, maintaining real-time UI updates while keeping API credentials isolated in the secure main process.","intents":["I want to use Claude for code generation but switch to GPT-4 for debugging without reconfiguring my project","I need to run my AI app builder entirely offline using local Ollama models","I want to compare outputs from multiple LLM providers side-by-side to pick the best result"],"best_for":["power users building AI apps who want provider flexibility","teams evaluating multiple LLM vendors for cost/quality tradeoffs","developers in restricted environments needing local-only model execution"],"limitations":["Streaming latency depends on provider API response time; local Ollama adds ~500ms cold-start overhead","Token counting is approximate and may not match provider's exact tokenization for billing","No built-in fallback mechanism if primary provider is unavailable — requires manual model switching"],"requires":["API key for at least one provider (OpenAI, Anthropic, Google, etc.) OR local Ollama instance running on port 11434","Node.js 18+ for Electron runtime","Internet connection for cloud providers (not required for Ollama)"],"input_types":["text prompts","code snippets with context","file attachments (markdown, code files)"],"output_types":["streamed text responses","structured code blocks with language tags","markdown with embedded code proposals"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_1","uri":"capability://code.generation.editing.codebase.aware.code.generation.with.context.extraction","name":"codebase-aware code generation with context extraction","description":"Dyad implements a Codebase Context Extraction system that parses the user's project structure, identifies relevant files, and injects them into the LLM prompt as context. The system uses file tree traversal, language-specific AST parsing (via tree-sitter or regex patterns), and semantic relevance scoring to select the most important code snippets. This context is managed through a token-counting mechanism that respects model context windows, automatically truncating or summarizing files when approaching limits. The generated code is then parsed via a custom Markdown Parser that extracts code blocks and applies them via Search and Replace Processing, which uses fuzzy matching to handle indentation and formatting variations.","intents":["I want the AI to understand my existing component library and reuse patterns when generating new features","I need to generate code that follows my project's naming conventions and architectural style","I want to see exactly which files the AI considered when making its suggestion"],"best_for":["teams with established codebases who need AI to maintain consistency","developers building on top of frameworks (Next.js, React) where context is critical","projects where code generation without context leads to duplicated or conflicting implementations"],"limitations":["Context extraction is heuristic-based; it may miss relevant files in deeply nested or unconventional project structures","Token counting is approximate — actual model tokenization may differ, causing context to be truncated unexpectedly","Search-and-replace fallback uses fuzzy matching which can produce incorrect edits in files with similar code patterns","No semantic understanding of imports/dependencies — may generate code that references undefined modules"],"requires":["Project files accessible on local filesystem","Supported file types (JavaScript, TypeScript, Python, etc.) — binary files are skipped","Sufficient model context window (8k+ tokens recommended for medium projects)"],"input_types":["project directory path","file selection (manual or automatic)","natural language description of desired changes"],"output_types":["code blocks with file paths","search-and-replace operations","file creation/deletion instructions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_10","uri":"capability://code.generation.editing.search.and.replace.with.fuzzy.matching.and.fallback.strategies","name":"search and replace with fuzzy matching and fallback strategies","description":"Dyad implements a Search and Replace Processing system that applies AI-generated code changes to files using fuzzy matching and intelligent fallback strategies. The system first attempts exact-match replacement (matching whitespace and indentation precisely), then falls back to fuzzy matching (ignoring minor whitespace differences), and finally falls back to appending the code to the file if no match is found. This multi-stage approach handles variations in indentation, line endings, and formatting that are common when AI generates code. The system also tracks which replacements succeeded and which failed, providing feedback to the user. For complex changes, the system can fall back to full-file replacement, replacing the entire file with the AI-generated version.","intents":["I want the AI to update my code even if indentation or formatting doesn't match exactly","I want to see which code changes succeeded and which failed","I want a safe fallback if the AI's search pattern doesn't match exactly"],"best_for":["developers working with AI-generated code that may have formatting variations","teams with inconsistent code formatting where exact matching fails","projects where code generation needs to be robust to formatting changes"],"limitations":["Fuzzy matching can produce incorrect replacements if multiple code sections are similar","Full-file replacement loses any manual edits made since the last AI generation","No support for complex refactorings (e.g., moving functions between files)","Fallback to append may result in duplicate code if the search pattern is too broad"],"requires":["File paths specified in AI proposals","Search patterns that are sufficiently specific to avoid false matches"],"input_types":["search patterns (exact or fuzzy)","replacement code","file paths"],"output_types":["modified files","success/failure status for each replacement","fallback decisions"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_11","uri":"capability://automation.workflow.electron.based.desktop.application.with.ipc.security.model","name":"electron-based desktop application with ipc security model","description":"Dyad is implemented as an Electron desktop application using a three-process security model: Main Process (handles app lifecycle, IPC routing, file I/O, API credentials), Preload Process (security bridge with whitelisted IPC channels), and Renderer Process (UI, chat interface, code editor). All cross-process communication flows through a secure IPC channel registry defined in the Preload script, preventing the renderer from directly accessing sensitive operations. The Main Process runs with full system access and handles all API calls, file operations, and external integrations, while the Renderer Process is sandboxed and can only communicate via whitelisted IPC channels. This architecture ensures that API credentials, file system access, and external service integrations are isolated from the renderer, preventing malicious code in generated applications from accessing sensitive data.","intents":["I want to run AI-generated code safely without it accessing my API keys or file system","I want to ensure that my GitHub credentials and API keys are never exposed to the renderer process","I want to run Dyad as a desktop application with full offline capability"],"best_for":["developers concerned about security when running AI-generated code","teams with strict security policies requiring local-only execution","power users who want full control over their development environment"],"limitations":["Electron adds ~100MB to application size and ~500MB RAM overhead compared to web apps","IPC communication adds latency (~5-10ms per call) compared to direct function calls","Preload script must be manually maintained; adding new IPC channels requires code changes","Cross-platform compatibility requires testing on Windows, macOS, and Linux"],"requires":["Electron 20+ runtime","Node.js 18+ for development","macOS 10.13+, Windows 7+, or Linux with X11/Wayland"],"input_types":["IPC messages from renderer","file system operations","API requests"],"output_types":["IPC responses to renderer","file system changes","API responses"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_12","uri":"capability://memory.knowledge.database.snapshots.and.state.persistence.with.sqlite","name":"database snapshots and state persistence with sqlite","description":"Dyad implements a Data Persistence system using SQLite to store application state, chat history, project metadata, and snapshots. The system uses Jotai for in-memory global state management and persists changes to SQLite on disk, enabling recovery after application crashes or restarts. Snapshots are created at key points (after AI generation, before major changes) and include the full application state (files, settings, chat history). The system also implements a backup mechanism that periodically saves the SQLite database to a backup location, protecting against data loss. State is organized into tables (projects, chats, snapshots, settings) with relationships that enable querying and filtering.","intents":["I want my chat history and project state to persist across Dyad restarts","I want to recover from accidental changes by restoring a previous snapshot","I want automatic backups of my work in case of disk failure"],"best_for":["developers who want persistent project state across sessions","teams collaborating on AI-generated code who need audit trails","power users who want to preserve exploration history and snapshots"],"limitations":["SQLite is single-user; concurrent access from multiple Dyad instances will cause lock contention","Snapshots can grow large (100MB+) for projects with many files; no automatic pruning","Backups are local only; no cloud sync or multi-device synchronization","State schema is not versioned; database migrations must be handled manually"],"requires":["SQLite 3.8+ (bundled with Dyad)","Sufficient disk space for snapshots and backups"],"input_types":["application state changes","chat messages","file modifications"],"output_types":["persisted state in SQLite","snapshots with metadata","backup files"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_13","uri":"capability://tool.use.integration.supabase.and.neon.postgresql.integration.for.backend.services","name":"supabase and neon postgresql integration for backend services","description":"Dyad implements integrations with Supabase (PostgreSQL + authentication + real-time) and Neon (serverless PostgreSQL) to enable AI-generated applications to connect to production databases. The system stores database credentials securely in the Main Process (never exposed to the Renderer), provides UI for configuring database connections, and generates boilerplate code for database access (SQL queries, ORM setup). The integration includes schema introspection, allowing the AI to understand the database structure and generate appropriate queries. For Supabase, the system also handles authentication setup (JWT tokens, session management) and real-time subscriptions. Generated applications can immediately connect to the database without additional configuration.","intents":["I want to generate a full-stack app with a database backend without manual configuration","I want the AI to understand my database schema and generate appropriate queries","I want to use Supabase authentication in my generated app"],"best_for":["developers building full-stack applications with AI assistance","teams using Supabase or Neon for their backend infrastructure","power users who want end-to-end application generation including databases"],"limitations":["Database credentials are stored locally; no encryption at rest (relies on OS-level security)","Schema introspection is read-only; cannot generate migrations or schema changes","Limited to PostgreSQL; no support for MySQL, MongoDB, or other databases","Generated queries may not be optimized; no automatic query optimization or indexing suggestions"],"requires":["Supabase or Neon account with database created","Database credentials (connection string or API key)","PostgreSQL 12+ for schema introspection"],"input_types":["database connection string","database schema","natural language descriptions of data access needs"],"output_types":["generated SQL queries","ORM setup code (Prisma, TypeORM, etc.)","authentication boilerplate"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_2","uri":"capability://automation.workflow.live.preview.environment.with.hot.reload.development.server","name":"live preview environment with hot-reload development server","description":"Dyad includes a Preview System and Development Environment that runs generated React/Next.js applications in an embedded Electron BrowserView. The system spawns a local development server (Vite or Next.js dev server) as a child process, watches for file changes, and triggers hot-module-reload (HMR) updates without full page refresh. The preview is isolated from the main Dyad UI via IPC, allowing the generated app to run with full access to DOM APIs while keeping the builder secure. Console output from the preview is captured and displayed in a Console and Logging panel, enabling developers to debug generated code in real-time.","intents":["I want to see my generated app running immediately without switching to a terminal or browser","I need to debug JavaScript errors in the generated code and see console logs in real-time","I want hot-reload to work so I can iterate quickly without manual page refreshes"],"best_for":["frontend developers building React/Next.js apps who need tight feedback loops","non-technical users who want visual confirmation that generated code works","teams prototyping UI-heavy applications where visual iteration is critical"],"limitations":["Preview only supports React/Next.js/Vite-based projects — backend services, databases, and external APIs must be mocked or stubbed","Hot-reload may fail if generated code has syntax errors; full page refresh is required to recover","Console capture is limited to client-side logs; server-side errors in Next.js API routes are not visible","Preview runs in a sandboxed Electron context with limited access to native APIs (file system, network) unless explicitly exposed"],"requires":["Node.js 18+ with npm/yarn/pnpm installed","React 18+ or Next.js 13+ for generated projects","Sufficient disk space for node_modules (~500MB typical)"],"input_types":["generated React/Next.js code","project configuration (package.json, tsconfig.json)"],"output_types":["rendered HTML/CSS/JavaScript in BrowserView","console logs and error messages","network request logs (if instrumented)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_3","uri":"capability://automation.workflow.version.control.and.time.travel.debugging.with.git.integration","name":"version control and time-travel debugging with git integration","description":"Dyad implements a Version Control and Time-Travel system that automatically commits generated code to a local Git repository after each AI-generated change. The system uses Git Integration to track diffs, enable rollback to previous versions, and display a visual history timeline. Additionally, Database Snapshots and Time-Travel functionality stores application state snapshots at each commit, allowing users to revert not just code but also the entire project state (settings, chat history, file structure). The Git workflow is abstracted behind a simple UI that hides complexity — users see a timeline of changes with diffs, and can click to restore any previous version without manual git commands.","intents":["I want to undo the last AI-generated change and try a different approach without losing my work","I need to see what code the AI generated in the last 5 iterations to understand the progression","I want to compare the current version with a version from 30 minutes ago to see what broke"],"best_for":["developers iterating rapidly with AI, who need safety nets for bad generations","teams collaborating on AI-generated code who need audit trails","solo builders experimenting with different AI approaches who want to preserve exploration history"],"limitations":["Snapshots are stored locally in SQLite; no cloud backup by default — data loss if local disk fails","Time-travel only works within a single Dyad session; snapshots are not synced across machines","Git history can become large if many small edits are made; no automatic pruning or compression","Rollback is all-or-nothing at the snapshot level; cannot selectively revert individual files"],"requires":["Git installed and available in PATH","Local filesystem with write permissions for .git directory","SQLite database (bundled with Dyad)"],"input_types":["code changes from AI generation","user-initiated rollback requests"],"output_types":["git commits with diffs","snapshot metadata (timestamp, description)","visual timeline UI"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_4","uri":"capability://planning.reasoning.chat.modes.and.agent.based.task.decomposition","name":"chat modes and agent-based task decomposition","description":"Dyad implements a Chat Modes and Agent System that supports multiple interaction patterns: direct code generation (single-turn), iterative refinement (multi-turn conversation), and autonomous agent mode (where the AI breaks down complex tasks into subtasks and executes them sequentially). The agent system uses chain-of-thought reasoning to decompose user requests into smaller steps, generates code for each step, applies it to the project, and reports results back to the user. The system maintains conversation context across turns using Jotai-based Global State, allowing the AI to reference previous messages and understand the cumulative intent. Different chat modes are exposed via UI toggles, and the system prompt is adjusted per-mode to guide the AI's behavior (e.g., agent mode includes instructions for task decomposition).","intents":["I want to describe a complex feature and have the AI break it down into steps and implement each one","I want to have a back-and-forth conversation with the AI to refine the generated code iteratively","I want the AI to autonomously implement a feature without me having to approve each step"],"best_for":["power users who want AI to handle complex, multi-step tasks autonomously","teams building features that require multiple interdependent changes","developers who prefer conversational iteration over single-shot generation"],"limitations":["Agent mode can make unexpected decisions if task decomposition is ambiguous; no human-in-the-loop approval between steps","Context window limits mean long conversations may lose early context; system uses summarization which can lose nuance","Agent mode may generate redundant or conflicting code if subtasks overlap; no built-in conflict detection","Conversation state is stored in memory (Jotai); closing the app loses chat history unless explicitly exported"],"requires":["Sufficient model context window (16k+ tokens recommended for agent mode)","Clear, well-structured user prompts for best results"],"input_types":["natural language task descriptions","multi-turn conversation messages","code snippets for refinement"],"output_types":["code proposals with reasoning","task decomposition breakdown","step-by-step execution logs"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_5","uri":"capability://tool.use.integration.mcp.and.tool.system.with.local.agent.execution","name":"mcp and tool system with local agent execution","description":"Dyad implements an MCP (Model Context Protocol) and Tool System that allows the AI to call external tools and services through a schema-based function registry. Tools are defined as JSON schemas with input/output types, and the system supports both built-in tools (file operations, git commands, search-replace) and user-defined tools via MCP servers. The Local Agent System executes tool calls in the main process (for security-critical operations like file I/O) or spawns child processes for isolated execution. Tool results are streamed back to the AI, enabling it to take actions, observe outcomes, and refine its approach. The system includes native bindings for common operations (file read/write, directory traversal, git status) and can invoke external MCP servers for specialized tasks.","intents":["I want the AI to automatically run tests and fix code based on test failures","I want the AI to search my codebase and use the results to inform its code generation","I want to extend Dyad with custom tools that the AI can call (e.g., API calls, database queries)"],"best_for":["developers building complex applications where AI needs to interact with external systems","teams with custom tooling who want to integrate it with Dyad's AI","power users building AI agents that need to execute actions beyond code generation"],"limitations":["Tool execution is synchronous; long-running tools (tests, builds) block the chat interface","MCP server integration requires manual setup and configuration; no auto-discovery","Tool schemas must be manually defined; no automatic schema generation from function signatures","Error handling is basic; tool failures may not gracefully degrade, causing agent loops"],"requires":["MCP server running (for external tools) or built-in tools only","Tool schemas defined in JSON Schema format","Appropriate file system and process permissions for tool execution"],"input_types":["tool schema definitions","tool invocation requests from AI","tool execution results"],"output_types":["tool execution results","error messages","structured data from tool responses"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_6","uri":"capability://tool.use.integration.github.integration.with.repository.cloning.and.deployment","name":"github integration with repository cloning and deployment","description":"Dyad implements GitHub Integration that allows users to clone repositories directly into Dyad, work on them with AI assistance, and push changes back to GitHub. The system uses the GitHub API to authenticate (via OAuth or personal access tokens), list user repositories, and perform git operations (clone, push, pull). Generated code is committed to a local branch, and users can create pull requests directly from Dyad's UI. The system also integrates with Vercel and other deployment platforms, enabling one-click deployment of generated applications. Repository metadata (owner, branch, remote URL) is stored in project settings, allowing users to switch between local and remote workflows seamlessly.","intents":["I want to clone my GitHub repo into Dyad, make AI-assisted changes, and push them back","I want to create a pull request with AI-generated code without leaving Dyad","I want to deploy my generated app to Vercel with a single click"],"best_for":["developers working on existing GitHub projects who want AI assistance","teams using GitHub for collaboration and wanting to integrate AI code generation","solo developers who want to deploy generated apps without manual deployment steps"],"limitations":["GitHub authentication requires personal access token or OAuth setup; no built-in GitHub App support","Merge conflicts are not automatically resolved; users must manually resolve conflicts if remote branch diverges","Large repositories (>1GB) may take significant time to clone; no shallow clone option","Vercel deployment requires Vercel account and project setup; no support for other deployment platforms yet"],"requires":["GitHub account with repository access","Personal access token or OAuth credentials","Git installed locally","Vercel account (for deployment feature)"],"input_types":["GitHub repository URL","branch name","commit message"],"output_types":["cloned repository in local filesystem","git commits and push operations","pull request URLs","deployment URLs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_7","uri":"capability://data.processing.analysis.token.counting.and.context.window.management","name":"token counting and context window management","description":"Dyad implements a Token Counting and Context Management system that estimates token usage for each message and respects model context window limits. The system uses provider-specific tokenizers (OpenAI's tiktoken, Anthropic's tokenizer, etc.) to count tokens in prompts, context, and responses. When approaching context limits, the system automatically summarizes or truncates older messages, removes low-relevance context, or prompts the user to start a new conversation. Token counts are displayed in the UI, allowing users to understand cost implications and make informed decisions about context inclusion. The system also tracks cumulative token usage across a session, enabling users to monitor API costs.","intents":["I want to know how many tokens my prompt will use before sending it to an expensive API","I want the AI to automatically manage context so I don't hit the model's context limit mid-conversation","I want to see my cumulative token usage and estimated API costs for the session"],"best_for":["cost-conscious developers using expensive models (GPT-4, Claude 3 Opus)","teams with large codebases where context management is critical","developers working with models that have small context windows (4k tokens)"],"limitations":["Token counting is approximate; actual provider tokenization may differ by 5-10%, causing unexpected overages","Automatic context truncation may remove important information, degrading code generation quality","No support for dynamic context window negotiation; system assumes fixed limits per model","Cost estimation is based on public pricing; actual costs may vary with volume discounts or enterprise agreements"],"requires":["Provider-specific tokenizer library (tiktoken for OpenAI, etc.)","Accurate model context window specification in settings"],"input_types":["prompts and messages","code context and file snippets"],"output_types":["token count estimates","cost estimates","context truncation decisions"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_8","uri":"capability://data.processing.analysis.markdown.parser.with.custom.tags.for.code.proposals","name":"markdown parser with custom tags for code proposals","description":"Dyad implements a custom Markdown Parser that extends standard markdown to support AI-specific syntax for code proposals. The parser recognizes custom tags like `<proposal>`, `<file>`, `<search>`, `<replace>` that structure AI-generated code changes in a machine-readable format. Code blocks within proposals are tagged with language identifiers and file paths, enabling the system to apply changes programmatically. The parser also handles nested structures (e.g., multiple replacements within a single file) and validates syntax before applying changes. This allows the AI to generate complex multi-file changes in a single response, which are then applied atomically via the Search and Replace Processing system.","intents":["I want the AI to generate code changes in a structured format that I can review before applying","I want the AI to make changes to multiple files in a single response without manual copy-paste","I want to see exactly what the AI is proposing to change before it modifies my code"],"best_for":["developers who want to review AI-generated changes before applying them","teams with code review processes who need structured change proposals","power users who want to understand and potentially edit AI proposals before applying"],"limitations":["Custom markdown syntax is not standard; requires documentation and learning curve","Parser is strict about syntax; malformed proposals will fail to parse and require manual correction","No support for complex refactorings (e.g., moving functions between files); only file-level operations","Nested proposals are not supported; cannot have proposals within proposals"],"requires":["AI model trained to generate proposals in Dyad's markdown format (or system prompt that teaches the format)","Parser implementation in TypeScript"],"input_types":["markdown text from AI responses","custom proposal tags"],"output_types":["parsed proposal objects with file paths and changes","validation errors if syntax is invalid"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dyad-sh--dyad__cap_9","uri":"capability://planning.reasoning.system.prompts.and.ai.rules.with.rule.based.behavior.control","name":"system prompts and ai rules with rule-based behavior control","description":"Dyad implements a System Prompts and AI Rules system that allows fine-grained control over AI behavior through configurable prompts and rule files. System prompts are stored as markdown files (e.g., `rules/git-workflow.md`) that define guidelines for code generation, naming conventions, architectural patterns, and project-specific rules. These rules are injected into the LLM prompt before each generation, ensuring the AI adheres to project standards. The system supports multiple rule sets (e.g., frontend rules, backend rules, testing rules) that can be selectively applied based on context. Rules are version-controlled and can be updated without modifying the application code, enabling teams to evolve AI behavior over time.","intents":["I want to enforce coding standards (naming conventions, architectural patterns) across all AI-generated code","I want to document project-specific rules that the AI should follow","I want to update AI behavior without modifying the application code"],"best_for":["teams with established coding standards who want AI to respect them","projects with complex architectural patterns that need to be preserved","organizations that want to version-control AI behavior alongside code"],"limitations":["Rules are advisory; the AI may ignore them if the prompt is ambiguous or conflicting","No automatic validation that generated code follows rules; requires manual review","Rule files are plain text; no structured format for complex rules (e.g., conditional rules)","Large rule sets increase token usage, reducing context available for code generation"],"requires":["Markdown files defining rules in the project directory","Clear, concise rule descriptions for best results"],"input_types":["markdown rule files","rule selection/filtering"],"output_types":["injected system prompts","AI-generated code that respects rules"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["API key for at least one provider (OpenAI, Anthropic, Google, etc.) OR local Ollama instance running on port 11434","Node.js 18+ for Electron runtime","Internet connection for cloud providers (not required for Ollama)","Project files accessible on local filesystem","Supported file types (JavaScript, TypeScript, Python, etc.) — binary files are skipped","Sufficient model context window (8k+ tokens recommended for medium projects)","File paths specified in AI proposals","Search patterns that are sufficiently specific to avoid false matches","Electron 20+ runtime","Node.js 18+ for development"],"failure_modes":["Streaming latency depends on provider API response time; local Ollama adds ~500ms cold-start overhead","Token counting is approximate and may not match provider's exact tokenization for billing","No built-in fallback mechanism if primary provider is unavailable — requires manual model switching","Context extraction is heuristic-based; it may miss relevant files in deeply nested or unconventional project structures","Token counting is approximate — actual model tokenization may differ, causing context to be truncated unexpectedly","Search-and-replace fallback uses fuzzy matching which can produce incorrect edits in files with similar code patterns","No semantic understanding of imports/dependencies — may generate code that references undefined modules","Fuzzy matching can produce incorrect replacements if multiple code sections are similar","Full-file replacement loses any manual edits made since the last AI generation","No support for complex refactorings (e.g., moving functions between files)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.38432856239284896,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-05-02T22:28:48Z"},"community":{"stars":20231,"forks":2372,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dyad-sh--dyad","compare_url":"https://unfragile.ai/compare?artifact=dyad-sh--dyad"}},"signature":"8vJv6CSx+wqB2u+2HTCrjZb0y7sgsVlrnSG1Bx3R//jYKjqQC6oRh7p1rvooz1wCqACggz+JkP1yBxqjRRJkAQ==","signedAt":"2026-06-21T01:47:30.256Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dyad-sh--dyad","artifact":"https://unfragile.ai/dyad-sh--dyad","verify":"https://unfragile.ai/api/v1/verify?slug=dyad-sh--dyad","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"}}