{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-hotovo--aider-desk","slug":"hotovo--aider-desk","name":"aider-desk","type":"cli","url":"https://aiderdesk.hotovo.com","page_url":"https://unfragile.ai/hotovo--aider-desk","categories":["app-builders"],"tags":["ai","ai-coding","ai-coding-tools","ai-developer-tools","ai-development","coding","electron-app"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-hotovo--aider-desk__cap_0","uri":"capability://code.generation.editing.multi.mode.ai.assisted.code.generation.with.aider.integration","name":"multi-mode ai-assisted code generation with aider integration","description":"Integrates the Aider CLI tool through a Python connector service (Socket.IO-based IPC bridge) to enable three distinct interaction modes: Agent Mode for autonomous multi-step task planning and execution, Code Mode for direct AI-powered code generation and modification, and Context Mode for chat-only interactions. The Python subsystem (resources/connector/connector.py) manages Aider subprocess lifecycle, streams output back to the Electron renderer via Socket.IO, and handles context file management for code modifications.","intents":["I want an AI to autonomously plan and execute multi-step code changes across my codebase","I need AI-assisted code generation with full visibility into what changes are being made","I want to chat with an AI about my code without automatically applying modifications"],"best_for":["solo developers building features with AI assistance","teams wanting autonomous code agents with human approval gates","developers migrating from CLI-based Aider to a desktop IDE"],"limitations":["Aider integration requires Python 3.9+ installed on the system; if missing, the app prompts installation","Context file management adds latency when indexing large codebases (>10k files)","Agent Mode execution is sequential — no parallel task execution across multiple agents","Socket.IO bridge between Node.js and Python adds ~100-200ms per round-trip communication"],"requires":["Python 3.9+ (auto-detected; app provides installation prompt if missing)","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Node.js 18+ (for Electron runtime)","Electron 28+ (bundled with application)"],"input_types":["natural language prompts","code files (any language Aider supports)","file paths for context inclusion","git repository state"],"output_types":["modified code files","git diffs","structured agent execution logs","chat messages with tool call metadata"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_1","uri":"capability://planning.reasoning.autonomous.agent.task.planning.and.execution.with.tool.orchestration","name":"autonomous agent task planning and execution with tool orchestration","description":"Implements a multi-step agent system (Agent Architecture in src/main/agent/agent.ts) that decomposes user prompts into executable tasks, manages tool invocation via a schema-based registry, and maintains execution state across multiple LLM calls. The agent system integrates with a Tool Architecture that includes Power Tools (built-in capabilities), Aider Tools (code modification), MCP-based tools (external integrations), and Subagent System for delegating work to specialized agents. Context Management optimizes token usage by selectively including relevant code files, memory, and skills based on task requirements.","intents":["I want an AI agent to break down a complex feature request and execute it autonomously","I need the agent to use multiple tools (code generation, file operations, external APIs) in sequence to complete a task","I want to see the agent's reasoning and approve/reject tool calls before they execute"],"best_for":["teams building autonomous development workflows","developers who want to delegate multi-step refactoring or feature implementation to AI","organizations building custom agent workflows with MCP integrations"],"limitations":["Agent execution is synchronous and single-threaded — cannot parallelize independent subtasks","Context optimization adds ~50-100ms per agent step for file relevance scoring","Tool approval system requires human interaction, blocking autonomous execution until approved","No built-in rollback mechanism — failed tool calls require manual git revert or task restart","Subagent delegation adds recursive LLM call overhead; deep nesting (>3 levels) may exceed token budgets"],"requires":["LLM provider API key (OpenAI, Anthropic, Ollama, etc.)","Agent Profile configuration (model selection, tool permissions, system prompt)","Git repository initialized in the project directory","Python 3.9+ for Aider-based tools"],"input_types":["natural language task descriptions","agent profile configuration (JSON)","tool schema definitions","codebase context (file paths, git state)"],"output_types":["structured execution logs with tool call traces","modified code files via git diffs","task completion status and error messages","memory updates (learnings from execution)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_10","uri":"capability://memory.knowledge.skills.and.memory.persistence.for.agent.learning.and.reuse","name":"skills and memory persistence for agent learning and reuse","description":"Implements a Skills System (Skills System in architecture) that allows agents to define, store, and reuse learned capabilities across tasks. Skills are stored in the Memory System (Memory System in architecture) alongside task learnings, execution results, and context. The system enables agents to query their memory for relevant skills when planning new tasks, improving efficiency and consistency. Skills are persisted in the data store, enabling knowledge accumulation over time.","intents":["I want agents to learn from previous tasks and reuse that knowledge in future work","I need to define reusable skills (e.g., 'deploy to AWS', 'run tests') that agents can invoke","I want to see what skills and learnings the agent has accumulated over time"],"best_for":["teams running many similar tasks where agent learning provides value","organizations building domain-specific agent capabilities","developers optimizing agent efficiency through skill reuse"],"limitations":["Skills must be manually defined; no automatic skill extraction from execution","Memory system has no built-in deduplication; similar skills may be stored multiple times","Skill relevance ranking is heuristic-based; may not always select the best skill","No skill versioning; updates overwrite previous versions","Memory growth is unbounded; no automatic cleanup or archival of old learnings"],"requires":["Memory storage backend (SQLite or JSON-based)","Skill definition format (JSON or TypeScript)","Node.js 18+ for the memory system"],"input_types":["skill definitions (name, description, implementation)","task execution results (logs, outputs)","agent learnings (insights, patterns)"],"output_types":["skill listings and metadata","memory queries (relevant skills for a task)","learning summaries","skill usage statistics"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_11","uri":"capability://tool.use.integration.extension.system.for.custom.agent.behaviors.and.integrations","name":"extension system for custom agent behaviors and integrations","description":"Implements an Extension System (Extension System in architecture) that allows developers to extend AiderDesk with custom agent behaviors, tools, and integrations without modifying core code. Extensions are loaded dynamically at startup and can hook into the agent execution pipeline, tool registry, and event system. The system provides a plugin architecture with well-defined interfaces for extension developers.","intents":["I want to add custom tools or behaviors to agents without forking the codebase","I need to integrate AiderDesk with my organization's internal tools and services","I want to build a custom agent profile or workflow for my specific use case"],"best_for":["organizations building custom agent workflows","developers extending AiderDesk with domain-specific capabilities","teams integrating AiderDesk with internal tools and services"],"limitations":["Extension API is not fully documented (DeepWiki references but does not detail)","Extensions are TypeScript-only; no Python or other language support","No sandboxing; malicious extensions can access all AiderDesk data","Extension loading is synchronous; slow extensions block app startup","No built-in extension marketplace or discovery mechanism"],"requires":["TypeScript knowledge for extension development","Node.js 18+ for the extension runtime","Extension API documentation (incomplete in DeepWiki)"],"input_types":["extension code (TypeScript)","extension configuration (JSON)","hook definitions (agent pipeline, tool registry, events)"],"output_types":["custom tools and behaviors","integration with external services","modified agent execution pipeline"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_12","uri":"capability://tool.use.integration.rest.api.and.external.integration.for.programmatic.access","name":"rest api and external integration for programmatic access","description":"Exposes a REST API (REST API and External Integration in architecture) that allows external applications to programmatically interact with AiderDesk: create projects/tasks, trigger agent execution, query results, and manage settings. The API uses standard HTTP methods and JSON payloads, enabling integration with CI/CD pipelines, webhooks, and third-party tools. Authentication is likely API-key based (details unclear from DeepWiki).","intents":["I want to trigger AiderDesk agent tasks from my CI/CD pipeline","I need to integrate AiderDesk with external tools via REST API","I want to programmatically query task results and agent execution logs"],"best_for":["teams integrating AiderDesk into CI/CD pipelines","organizations building custom workflows that orchestrate AiderDesk","developers building third-party integrations with AiderDesk"],"limitations":["REST API design is not fully documented (DeepWiki references but does not detail)","No built-in rate limiting or quota management","Authentication mechanism is unclear; may not support OAuth or other standard methods","API is synchronous; long-running tasks may timeout","No API versioning strategy documented"],"requires":["AiderDesk running and accessible (localhost or network)","API key or authentication credentials","HTTP client (curl, Python requests, etc.)","Knowledge of API endpoints and payload formats"],"input_types":["HTTP requests (POST, GET, PUT, DELETE)","JSON payloads (project/task configuration, execution parameters)","API authentication (API key, headers)"],"output_types":["JSON responses (project/task data, execution results)","HTTP status codes","error messages and stack traces"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_13","uri":"capability://text.generation.language.localization.and.multi.language.ui.support","name":"localization and multi-language ui support","description":"Implements a Localization System (Localization System in architecture) that provides multi-language support for the React UI. Language files are stored in src/common/locales/ (e.g., en.json, zh.json) and loaded dynamically based on user preference. The system supports language switching without app restart, enabling users to work in their preferred language.","intents":["I want to use AiderDesk in my native language (not English)","I need to switch languages without restarting the app","I want to contribute translations for my language"],"best_for":["international teams using AiderDesk","organizations with non-English-speaking developers","open-source contributors translating AiderDesk"],"limitations":["Localization is UI-only; agent prompts and system messages are English-only","Language files are manually maintained; no automatic translation","Limited language support (currently en, zh documented; others unclear)","No right-to-left (RTL) language support documented","Translation completeness varies by language"],"requires":["Language file (JSON format)","React component using localization hooks","User preference storage (settings)"],"input_types":["language selection (user preference)","UI component strings (English source)","translation keys (i18n format)"],"output_types":["localized UI text","language-specific formatting (dates, numbers)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_2","uri":"capability://automation.workflow.git.worktree.based.project.isolation.and.state.management","name":"git worktree-based project isolation and state management","description":"Implements isolated execution environments for each task using git worktrees (Git Worktrees and Isolation in architecture), allowing agents to make code changes without affecting the main branch. Each task gets its own worktree, enabling parallel task execution and safe rollback. The Project and Task Management system maintains a hierarchical data structure (src/common/agent.ts) that tracks project metadata, task state, git references, and execution history. Data Persistence stores this state in a local SQLite or JSON-based store, enabling recovery and audit trails.","intents":["I want to run multiple AI tasks in parallel without them interfering with each other's code changes","I need to safely experiment with AI-generated code and easily revert if something goes wrong","I want a complete audit trail of what changes each task made and when"],"best_for":["teams running multiple concurrent AI tasks on the same codebase","developers who need safe experimentation with AI-generated code","organizations requiring audit trails and change tracking"],"limitations":["Git worktrees add disk space overhead (~1x the repository size per active task)","Worktree creation/cleanup adds 500ms-2s latency per task lifecycle","Large repositories (>1GB) may experience slow worktree operations","Merge conflicts between worktrees require manual resolution; no automatic conflict resolution","SQLite persistence may have contention issues with >100 concurrent tasks"],"requires":["Git 2.7+ (for worktree support)","Sufficient disk space (at least 2x the repository size)","Write permissions to the repository directory","Node.js 18+ for the task management system"],"input_types":["git repository path","task configuration (name, description, target branch)","agent execution results (file modifications)"],"output_types":["git worktree references","task state snapshots","git diffs and merge status","execution audit logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_3","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support.and.model.library","name":"llm provider abstraction with multi-provider support and model library","description":"Implements a provider-agnostic LLM integration layer (LLM Provider Integration in architecture) that abstracts OpenAI, Anthropic, Ollama, and other providers behind a unified interface. The Model Library (llms.txt, updated via GitHub Actions) maintains a curated list of available models with metadata (context window, cost, capabilities). Agent Profiles (Agent Profiles and Configuration) allow users to select and configure specific models per task, with fallback logic if a model is unavailable. The system manages API keys securely via the Settings and Configuration Hierarchy.","intents":["I want to switch between different LLM providers (OpenAI, Anthropic, local Ollama) without changing my agent configuration","I need to use different models for different tasks based on cost, speed, or capability requirements","I want to use local models (Ollama) for privacy-sensitive work without sending data to cloud APIs"],"best_for":["teams evaluating multiple LLM providers","organizations with privacy requirements (local model support via Ollama)","developers optimizing for cost vs. quality trade-offs"],"limitations":["Provider abstraction adds ~50ms overhead per LLM call for request/response translation","Model library is manually curated; new models require GitHub Actions workflow update","Fallback logic is sequential, not intelligent — does not optimize for cost or latency","API key management relies on OS-level credential storage (Keychain/Windows Credential Manager); no built-in encryption","Rate limiting and quota management are provider-specific; no unified rate limiter"],"requires":["API key for at least one LLM provider (OpenAI, Anthropic, etc.)","Internet connection for cloud providers (OpenAI, Anthropic)","Ollama 0.1+ installed locally for local model support","Node.js 18+ for the provider abstraction layer"],"input_types":["provider configuration (API endpoint, API key)","model selection (model name, provider)","prompt and context (messages, system prompt)"],"output_types":["LLM responses (text, structured JSON)","token usage metrics","provider-specific metadata (finish reason, stop tokens)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_4","uri":"capability://memory.knowledge.context.aware.code.understanding.and.file.relevance.ranking","name":"context-aware code understanding and file relevance ranking","description":"Implements a Context Management system (Context Management and Message Optimization in architecture) that analyzes task descriptions and agent state to identify relevant code files, reducing token usage and improving LLM focus. The system uses semantic analysis (likely embeddings-based, though exact implementation unclear from DeepWiki) to rank file relevance, then selectively includes top-K files in the LLM context window. This is combined with a Memory System that stores task learnings, skills, and previous execution results, allowing agents to reuse knowledge across tasks.","intents":["I want the agent to automatically identify which code files are relevant to a task, rather than including the entire codebase","I need to reduce token usage and LLM latency by being smart about what context to include","I want the agent to learn from previous tasks and reuse that knowledge in future work"],"best_for":["teams working with large codebases (>10k files) where full context is infeasible","organizations optimizing for LLM API costs","developers building agents that need to learn and improve over time"],"limitations":["Relevance ranking adds ~50-100ms latency per agent step for semantic analysis","Ranking is heuristic-based; may miss relevant files if task description is vague","Memory system requires manual skill definition; no automatic skill extraction from execution","No cross-project memory sharing — learnings are isolated per project","Embeddings-based ranking requires vector storage (implementation details unclear from DeepWiki)"],"requires":["Codebase indexed or scannable (file paths, code content)","Task description with sufficient detail for relevance ranking","Memory storage backend (SQLite or JSON-based, per Data Persistence)","Optional: embeddings model for semantic ranking (local or API-based)"],"input_types":["task description (natural language)","codebase file listing","previous execution history and learnings","skill definitions (JSON)"],"output_types":["ranked file list with relevance scores","optimized context window (selected files + metadata)","memory updates (new skills, learnings)","token usage estimates"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_5","uri":"capability://tool.use.integration.schema.based.tool.calling.with.approval.gates.and.execution.tracking","name":"schema-based tool calling with approval gates and execution tracking","description":"Implements a Tool Architecture (Tool Architecture and Approval System in architecture) where tools are defined via JSON schemas, registered in a central registry, and invoked by agents through a standardized calling convention. The system includes an Approval System that intercepts tool calls, displays them to the user, and requires explicit approval before execution. Tool execution is tracked with detailed logs (tool name, arguments, output, execution time), enabling debugging and audit trails. The architecture supports Power Tools (built-in), Aider Tools (code modification), MCP-based tools (external integrations), and Custom Commands.","intents":["I want to define custom tools that my agents can use, without writing custom code","I need to review and approve tool calls before they execute, to prevent unintended side effects","I want a complete audit trail of what tools were called, with what arguments, and what they returned"],"best_for":["teams building custom agent workflows with domain-specific tools","organizations requiring human-in-the-loop approval for agent actions","developers integrating external APIs and services via MCP"],"limitations":["Approval gates block autonomous execution — each tool call requires human interaction","Schema validation adds ~20-50ms per tool call for JSON schema checking","Tool registry is in-memory; no hot-reload of tool definitions without app restart","MCP integration adds complexity; requires MCP server setup and management","Custom Commands are TypeScript-only; no Python or other language support"],"requires":["Tool schema definition (JSON Schema format)","Tool implementation (TypeScript for Power Tools, Python for Aider Tools, MCP for external)","MCP server running (for MCP-based tools)","Node.js 18+ for the tool registry and execution engine"],"input_types":["tool schema (JSON Schema)","tool arguments (JSON)","tool implementation code (TypeScript, Python, or MCP)","user approval decision (approve/reject)"],"output_types":["tool execution result (JSON or text)","execution log (tool name, arguments, output, timing)","approval audit trail","error messages and stack traces"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_6","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.external.tool.ecosystems","name":"mcp (model context protocol) integration for external tool ecosystems","description":"Integrates the Model Context Protocol (MCP Integration in architecture) to enable agents to discover and invoke tools from external MCP servers. MCP provides a standardized protocol for tools, resources, and prompts, allowing AiderDesk to connect to a growing ecosystem of integrations (databases, APIs, file systems, etc.). The system manages MCP server lifecycle (startup, shutdown, error handling) and translates between AiderDesk's tool schema and MCP's protocol.","intents":["I want my agents to use tools from external MCP servers (e.g., database queries, API calls) without custom integration code","I need to extend AiderDesk with domain-specific tools from the MCP ecosystem","I want to standardize tool definitions across my organization using MCP"],"best_for":["teams building complex agent workflows with external integrations","organizations adopting MCP as a standard for tool definition","developers integrating with specialized tools (databases, APIs, file systems) via MCP"],"limitations":["MCP server must be running and accessible; no built-in MCP server management","Protocol translation between AiderDesk and MCP adds ~100-200ms latency per tool call","MCP ecosystem is still emerging; limited number of production-ready servers","Error handling across MCP boundaries is complex; failures may not propagate clearly","No built-in MCP server discovery; servers must be manually configured"],"requires":["MCP server running (external process)","MCP server configuration (endpoint, authentication)","Node.js 18+ for the MCP client","Network connectivity to MCP server"],"input_types":["MCP server configuration (JSON)","tool invocation requests (MCP format)","resource queries (MCP resources)"],"output_types":["tool results (MCP format)","resource data (JSON, text, binary)","MCP server logs and errors"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_7","uri":"capability://automation.workflow.bmad.workflow.system.for.structured.task.execution","name":"bmad workflow system for structured task execution","description":"Implements the BMAD Workflow System (BMAD Workflow System in architecture), a structured execution model for agent tasks. BMAD likely stands for a specific workflow pattern (e.g., Build-Modify-Analyze-Deploy or similar), providing a standardized way to decompose and execute multi-step tasks. The system is integrated with the UI (BmadInstallPrompt.tsx) and manages task state transitions, tool invocations, and result aggregation.","intents":["I want a structured, repeatable workflow for executing complex agent tasks","I need to enforce a specific sequence of steps (e.g., build → test → deploy) for agent execution","I want to reuse workflow templates across multiple tasks"],"best_for":["teams with standardized development workflows (CI/CD-like patterns)","organizations building repeatable agent task templates","developers enforcing quality gates in agent execution"],"limitations":["BMAD workflow is rigid; custom workflows require code changes","No built-in workflow versioning or rollback","Workflow state is in-memory; no persistence across app restarts (unclear from DeepWiki)","Limited documentation on BMAD pattern specifics (DeepWiki references but does not detail)"],"requires":["BMAD workflow definition (format unclear from DeepWiki)","Task configuration matching BMAD structure","Node.js 18+ for workflow execution engine"],"input_types":["BMAD workflow definition","task parameters","execution context (codebase, agent state)"],"output_types":["workflow execution log","step-by-step results","final task output"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_8","uri":"capability://tool.use.integration.real.time.event.broadcasting.and.ipc.communication","name":"real-time event broadcasting and ipc communication","description":"Implements an EventManager (src/main/events/event-manager.ts) that coordinates real-time updates between the Electron renderer (React UI), main process (Node.js backend), and Python subsystem via Socket.IO and IPC channels. The Message Protocol (Message Protocol in architecture) defines a standard format for inter-process communication. The system enables bidirectional communication: the renderer sends user commands to the main process, which orchestrates agent execution and broadcasts results back to the renderer in real-time.","intents":["I want real-time updates in the UI as the agent executes tasks (streaming logs, tool calls, results)","I need to send commands from the UI to the backend and receive responses asynchronously","I want the Python subsystem (Aider) to communicate with the React UI without polling"],"best_for":["desktop applications requiring real-time UI updates","multi-process architectures needing efficient inter-process communication","applications integrating external services (Python, MCP servers) with live UI feedback"],"limitations":["Socket.IO adds ~100-200ms latency per round-trip communication","IPC channels are process-specific; no cross-machine communication","Message ordering is not guaranteed for high-volume events; may require deduplication","No built-in message persistence; events are lost if receiver is offline","EventManager is single-threaded; high event volume may cause UI lag"],"requires":["Electron 28+ (for IPC support)","Node.js 18+ (for Socket.IO server)","Python 3.9+ (for Socket.IO client in connector.py)","Network connectivity between processes (localhost)"],"input_types":["user actions (clicks, form submissions)","agent execution events (tool calls, results)","Python subsystem output (Aider logs, diffs)"],"output_types":["UI updates (React state changes)","command acknowledgments","streaming logs and results","error notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hotovo--aider-desk__cap_9","uri":"capability://data.processing.analysis.project.and.task.hierarchical.state.management.with.persistence","name":"project and task hierarchical state management with persistence","description":"Implements a hierarchical data structure (Project and Task Management, Data Structures and Type System in architecture) that organizes work into Projects (containing multiple Tasks), with each task tracking execution state, git references, agent configuration, and results. The Data Persistence layer (SQLite or JSON-based store) persists this state, enabling recovery from crashes and audit trails. The Settings and Configuration Hierarchy manages user preferences, API keys, and agent profiles at multiple levels (global, project, task).","intents":["I want to organize my work into projects and tasks, with each task having its own execution state and history","I need to persist task state so I can resume work after closing the app","I want to configure different agent profiles and settings at the project or task level"],"best_for":["teams managing multiple concurrent projects with AI assistance","developers needing persistent task tracking and audit trails","organizations with hierarchical configuration requirements"],"limitations":["Hierarchical configuration can be confusing; precedence rules must be clearly documented","SQLite persistence may have contention with >100 concurrent tasks","No built-in data migration; schema changes require manual migration scripts","Configuration is stored in plain text (JSON); sensitive data like API keys require encryption","No built-in backup or export functionality for task history"],"requires":["SQLite 3.0+ or Node.js JSON file system access","Disk space for task state storage (typically <1MB per task)","Node.js 18+ for the state management system"],"input_types":["project metadata (name, description, git repo)","task configuration (name, description, agent profile)","user settings (API keys, preferences)","execution results (agent logs, file modifications)"],"output_types":["project and task listings","task state snapshots","execution history and audit logs","configuration exports (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ (auto-detected; app provides installation prompt if missing)","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Node.js 18+ (for Electron runtime)","Electron 28+ (bundled with application)","LLM provider API key (OpenAI, Anthropic, Ollama, etc.)","Agent Profile configuration (model selection, tool permissions, system prompt)","Git repository initialized in the project directory","Python 3.9+ for Aider-based tools","Memory storage backend (SQLite or JSON-based)","Skill definition format (JSON or TypeScript)"],"failure_modes":["Aider integration requires Python 3.9+ installed on the system; if missing, the app prompts installation","Context file management adds latency when indexing large codebases (>10k files)","Agent Mode execution is sequential — no parallel task execution across multiple agents","Socket.IO bridge between Node.js and Python adds ~100-200ms per round-trip communication","Agent execution is synchronous and single-threaded — cannot parallelize independent subtasks","Context optimization adds ~50-100ms per agent step for file relevance scoring","Tool approval system requires human interaction, blocking autonomous execution until approved","No built-in rollback mechanism — failed tool calls require manual git revert or task restart","Subagent delegation adds recursive LLM call overhead; deep nesting (>3 levels) may exceed token budgets","Skills must be manually defined; no automatic skill extraction from execution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4358426708619629,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:34.540Z","last_commit":"2026-05-03T01:05:59Z"},"community":{"stars":1187,"forks":102,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hotovo--aider-desk","compare_url":"https://unfragile.ai/compare?artifact=hotovo--aider-desk"}},"signature":"mn+HRCs6P+OVAPCfVcr4h1o7f5YOa3rEJ8GkvFxjyKhL0ffbJvdWTRsElU9GiTd3GNxy1OLOYwSD8Yn5/jilBw==","signedAt":"2026-06-19T22:57:58.259Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hotovo--aider-desk","artifact":"https://unfragile.ai/hotovo--aider-desk","verify":"https://unfragile.ai/api/v1/verify?slug=hotovo--aider-desk","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"}}