{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-stravu--crystal","slug":"stravu--crystal","name":"crystal","type":"product","url":"https://nimbalyst.com/","page_url":"https://unfragile.ai/stravu--crystal","categories":["app-builders","testing-quality"],"tags":["agentic-ai","ai","ai-coding","anthropic","claude","claude-code","codex","cursor","development-environment","ide","llm-coding","openai-codex","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-stravu--crystal__cap_0","uri":"capability://automation.workflow.parallel.ai.session.orchestration.with.git.worktree.isolation","name":"parallel-ai-session-orchestration-with-git-worktree-isolation","description":"Manages multiple concurrent AI coding sessions (Claude Code and OpenAI Codex) running in parallel on the same repository by automatically creating isolated Git worktrees for each session. Uses Electron's multi-process architecture (main process handles SessionManager and WorktreeManager services) with IPC-based coordination to prevent file conflicts and state collisions. Each session maintains its own filesystem context while sharing the parent repository metadata.","intents":["Run multiple AI coding approaches simultaneously on the same codebase to compare strategies","Test different AI models (Claude vs Codex) on identical tasks without merge conflicts","Manage AI-assisted development workflows where multiple agents work in parallel","Isolate experimental AI changes from the main branch without manual branch management"],"best_for":["Teams evaluating multiple AI coding assistants on production codebases","Developers comparing Claude Code and OpenAI Codex outputs side-by-side","Solo developers prototyping multiple AI-assisted refactoring approaches simultaneously"],"limitations":["Git worktree isolation adds filesystem overhead — each session duplicates the entire working directory","No built-in conflict resolution when merging parallel session changes back to main branch","Requires manual Git knowledge to understand worktree semantics and cleanup","Performance degrades with very large repositories due to worktree duplication"],"requires":["Git (any modern version) with worktree support","Claude Code CLI ≥2.0.0 for Claude integration","OpenAI API key for Codex sessions","Anthropic API key for Claude Code sessions","macOS or Linux (Windows not currently supported)"],"input_types":["Git repository path","Session configuration (model selection, system prompts)","Task descriptions for AI agents"],"output_types":["Isolated worktree filesystem state","Git diffs per session","Execution history and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_1","uri":"capability://planning.reasoning.multi.panel.ai.conversation.management.within.sessions","name":"multi-panel-ai-conversation-management-within-sessions","description":"Enables multiple independent AI conversation threads (panels) to run concurrently within a single session context, each maintaining separate conversation history and state. The Panel System Architecture routes AI requests through a unified interface that dispatches to Claude or Codex APIs while maintaining panel-specific context windows and conversation state in the database layer. Panels share the same worktree filesystem but maintain isolated conversation threads.","intents":["Run multiple AI conversations on the same codebase without losing context between threads","Explore different problem-solving approaches within a single session","Maintain separate conversations for different aspects of a task (e.g., frontend + backend)","Compare AI responses across multiple parallel conversation threads"],"best_for":["Developers exploring multiple solution paths for a single problem","Teams breaking down complex tasks into parallel AI-assisted subtasks","Researchers comparing AI reasoning across different conversation contexts"],"limitations":["Panel state is stored in SQLite database — no built-in distributed state management for team collaboration","Context window limits apply per panel; large conversation histories may require pruning","No automatic context sharing between panels — developers must manually reference other panel outputs","Panel switching adds UI latency as conversation history is loaded from database"],"requires":["SQLite database (initialized by DatabaseService)","Active session with allocated worktree","API credentials for Claude Code or OpenAI Codex"],"input_types":["User prompts/messages","Code context from worktree","Previous conversation history (loaded from database)"],"output_types":["AI-generated responses","Conversation metadata (timestamps, model used)","Structured conversation history in database"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_10","uri":"capability://automation.workflow.event.system.for.reactive.state.updates","name":"event-system-for-reactive-state-updates","description":"Implements a publish-subscribe event system that emits state changes from backend services (SessionManager, WorktreeManager, DatabaseService) to the UI renderer process. Services emit typed events when state changes (e.g., session created, file modified, command executed), and the renderer subscribes to these events to update the UI reactively. Events are routed through IPC, enabling real-time UI updates without polling.","intents":["Update the UI in real-time when backend state changes (new session, file modified)","Avoid polling overhead by using event-driven updates","Maintain UI-backend synchronization without manual state reconciliation","Enable multiple UI components to react to the same state change"],"best_for":["Applications requiring real-time UI updates from backend services","Teams building reactive UIs without polling overhead","Developers needing loose coupling between backend services and UI"],"limitations":["Event ordering is not guaranteed across IPC boundaries — concurrent events may arrive out of order","No built-in event persistence — missed events during app startup are lost","Event subscription leaks may occur if listeners are not properly unsubscribed","Debugging event flows requires tracing IPC messages, which can be complex"],"requires":["IPC infrastructure (contextBridge, preload script)","Event emitter implementation in main process"],"input_types":["State change events from backend services"],"output_types":["Typed event objects emitted to renderer process","UI state updates triggered by events"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_11","uri":"capability://automation.workflow.session.creation.and.configuration.management","name":"session-creation-and-configuration-management","description":"Provides a workflow for creating new AI sessions with configurable parameters (model selection, system prompts, branch/worktree settings). The Session Creation and Configuration subsystem validates inputs, initializes a new session record in the database, creates an associated Git worktree, and sets up initial panel contexts. Users can configure per-session settings like AI model (Claude vs Codex), temperature, max tokens, and custom system prompts.","intents":["Create new AI sessions with custom configuration (model, prompts, branch)","Set up isolated development contexts for different AI tasks","Configure model-specific parameters (temperature, max tokens) per session","Initialize session state in the database and filesystem"],"best_for":["Developers setting up multiple AI sessions with different configurations","Teams standardizing on specific AI models and prompts per task type","Researchers running controlled experiments with different AI parameters"],"limitations":["Session creation time scales with repository size — large repos add 5-30s per session","Configuration validation is basic — invalid parameters may only be caught at runtime","No template system for common session configurations — each session requires manual setup","Parameter changes require session restart — no live reconfiguration"],"requires":["Git repository with worktree support","API credentials for selected AI model (Claude or Codex)","Sufficient disk space for new worktree"],"input_types":["Session name","AI model selection (Claude Code or Codex)","System prompt (optional)","Branch/worktree configuration","Model parameters (temperature, max tokens)"],"output_types":["Session ID","Worktree path","Initial session metadata","Database records"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_12","uri":"capability://automation.workflow.project.and.session.organization.with.hierarchical.ui","name":"project-and-session-organization-with-hierarchical-ui","description":"Organizes multiple sessions within projects using a hierarchical UI structure. Projects group related sessions, and sessions contain multiple panels for different conversation threads. The Navigation and Layout subsystem renders a sidebar with project/session/panel hierarchy, enabling quick switching between contexts. Session metadata (creation time, model, status) is displayed in the UI for easy identification.","intents":["Organize multiple AI sessions into logical projects for better management","Quickly switch between different sessions and conversation threads","View session metadata (model, creation time, status) at a glance","Manage complex workflows with many parallel AI sessions"],"best_for":["Teams running many parallel AI sessions on multiple projects","Developers managing complex multi-session workflows","Organizations needing clear session organization for auditing"],"limitations":["UI navigation becomes unwieldy with >50 sessions — no built-in search or filtering","Session switching requires UI interaction — no keyboard shortcuts for quick navigation","Hierarchical organization is fixed (project > session > panel) — no custom grouping","No session tagging or labeling system — organization relies on naming conventions"],"requires":["React 19 for UI rendering","Session metadata in database"],"input_types":["Project name","Session list with metadata"],"output_types":["Rendered hierarchical UI","Navigation state (selected project/session/panel)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_13","uri":"capability://automation.workflow.settings.and.configuration.persistence","name":"settings-and-configuration-persistence","description":"Manages application-wide settings (API keys, default models, UI preferences) through a ConfigManager service that persists settings to disk. Settings include API credentials for Claude and Codex, default AI model selection, UI theme, and logging level. Settings are loaded on application startup and can be modified through a settings UI panel. Sensitive settings (API keys) are stored securely using OS-level credential storage when available.","intents":["Configure API credentials for Claude Code and OpenAI Codex","Set default AI model and parameters for new sessions","Customize UI preferences (theme, layout)","Enable/disable logging and set logging level"],"best_for":["Users setting up Crystal for the first time with API credentials","Teams standardizing on default AI models and parameters","Developers debugging issues with detailed logging"],"limitations":["API keys are stored in plaintext in config file — no encryption at rest (unless OS credential storage is used)","Settings changes require application restart for some options","No settings validation — invalid API keys are only caught when used","No settings export/import — migrating to a new machine requires manual reconfiguration"],"requires":["Write permissions to config directory (~/.crystal or equivalent)","OS credential storage for secure API key storage (optional)"],"input_types":["API keys","Model selection","UI preferences","Logging configuration"],"output_types":["Persisted configuration file","Loaded settings on application startup"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_14","uri":"capability://tool.use.integration.file.operations.and.ipc.based.file.access","name":"file-operations-and-ipc-based-file-access","description":"Provides file read/write operations within worktrees through IPC-based file access APIs. The File Operations and IPC subsystem exposes file operations (read, write, delete, list directory) through the preload script, allowing the renderer to request file operations from the main process. File operations are scoped to the active worktree, preventing access outside the session context. All file I/O is handled by the main process, maintaining security boundaries.","intents":["Read and display file contents from the worktree in the UI","Write file modifications made by AI agents to disk","List directory contents for file browser UI","Delete or rename files as part of AI-generated changes"],"best_for":["Applications needing secure file access from a renderer process","Developers building file browsers or editors in Electron apps","Teams requiring audit trails of file modifications"],"limitations":["IPC overhead makes file operations slower than direct filesystem access (~5-50ms per operation)","Large file reads (>10MB) may cause UI freezing due to IPC serialization","No built-in file locking — concurrent writes from multiple sources may cause conflicts","File operations are not atomic — partial writes may occur if the process crashes"],"requires":["IPC infrastructure (preload script, contextBridge)","Write permissions to worktree directory"],"input_types":["File path (relative to worktree)","File content (for write operations)","Directory path (for list operations)"],"output_types":["File content (for read operations)","Directory listing (for list operations)","Operation status (success/failure)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_2","uri":"capability://code.generation.editing.claude.code.integration.with.streaming.output.rendering","name":"claude-code-integration-with-streaming-output-rendering","description":"Integrates Claude Code CLI (≥2.0.0) as a native AI backend with real-time streaming output rendering in the UI. The Claude Integration layer in the main process spawns Claude Code CLI as a child process, captures streaming responses via PTY (pseudo-terminal) management, and pipes structured output to the renderer process via IPC. AI Output Rendering components parse and display Claude's responses with syntax highlighting and interactive code blocks.","intents":["Use Claude Code as the primary AI coding assistant within Crystal's multi-session framework","Stream Claude's real-time responses to the UI without buffering entire outputs","Execute Claude-generated code changes directly within isolated worktrees","Maintain full conversation history with Claude within panel contexts"],"best_for":["Teams standardized on Anthropic's Claude for AI-assisted development","Developers wanting Claude Code's native capabilities integrated into a multi-session workflow","Organizations requiring audit trails of Claude interactions (stored in SQLite)"],"limitations":["Requires Claude Code CLI installation and configuration — adds external dependency management","Streaming output parsing depends on Claude's CLI output format — breaking changes in Claude Code versions may require adapter updates","No built-in retry logic for failed Claude API calls — relies on Claude CLI's error handling","Context window limited by Claude's model (e.g., Claude 3.5 Sonnet ~200K tokens) — large codebases may exceed context"],"requires":["Claude Code CLI ≥2.0.0 installed and in PATH","Anthropic API key configured in environment or Crystal settings","PTY support in OS (macOS/Linux)"],"input_types":["User prompts/messages","Code files from worktree","System prompts and configuration"],"output_types":["Streaming text responses","Code blocks with syntax highlighting","File modification instructions","Structured conversation history"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_3","uri":"capability://code.generation.editing.openai.codex.integration.with.api.based.code.generation","name":"openai-codex-integration-with-api-based-code-generation","description":"Integrates OpenAI Codex as an alternative AI backend using REST API calls, with request/response handling managed by the main process and rendered in the UI. The Codex Integration layer constructs API requests with code context from the worktree, sends them to OpenAI's API, and streams responses back to the renderer via IPC. Unlike Claude Code CLI integration, Codex integration is API-first without subprocess management.","intents":["Use OpenAI Codex as an alternative AI coding model within multi-session workflows","Compare Codex outputs against Claude Code outputs on identical tasks","Leverage Codex's code completion and generation capabilities in isolated sessions","Maintain Codex conversation history within panel contexts"],"best_for":["Teams evaluating OpenAI Codex vs Claude Code for code generation tasks","Developers with existing OpenAI API infrastructure and billing","Researchers comparing different AI models on code generation benchmarks"],"limitations":["Codex API calls incur per-token costs — parallel sessions with Codex multiply API expenses","No direct file system access like Claude Code CLI — code changes must be manually applied or integrated via separate mechanism","API rate limits may throttle parallel Codex sessions — requires careful request batching","Codex model is older than Claude 3.5 — may produce lower-quality outputs for complex tasks"],"requires":["OpenAI API key with Codex access","Network connectivity for API calls","API quota/billing setup with OpenAI"],"input_types":["Code context (file paths, snippets)","User prompts","Temperature and other API parameters"],"output_types":["Streamed API responses","Code completions and generations","API usage metadata (tokens, cost)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_4","uri":"capability://automation.workflow.git.worktree.creation.and.lifecycle.management","name":"git-worktree-creation-and-lifecycle-management","description":"Automatically creates, manages, and cleans up Git worktrees for each session using the WorktreeManager service. When a session is created, WorktreeManager spawns a new worktree from the parent repository, assigns it a unique filesystem path, and tracks its lifecycle in the database. On session deletion, the worktree is pruned and its filesystem is cleaned up. Worktree state is monitored via file watching and Git status polling.","intents":["Automatically isolate each AI session in its own Git worktree without manual git commands","Clean up worktrees when sessions are deleted to prevent filesystem bloat","Track worktree state and sync it with session metadata in the database","Enable safe parallel development without merge conflicts between sessions"],"best_for":["Teams running multiple AI sessions on large repositories where isolation is critical","Developers who want automatic worktree management without manual Git operations","CI/CD pipelines integrating Crystal for parallel AI-assisted testing"],"limitations":["Worktree creation time scales with repository size — large repos (>1GB) may add 5-30s per session startup","Filesystem space usage multiplies with number of sessions — each worktree duplicates the entire working directory","Git worktree pruning is not atomic — if the application crashes during cleanup, orphaned worktrees may remain","No built-in worktree repair mechanism — corrupted worktrees require manual git worktree prune"],"requires":["Git ≥2.17 (worktree support)","Sufficient disk space for multiple worktree copies","Write permissions to repository directory"],"input_types":["Parent repository path","Session configuration (branch, worktree name)"],"output_types":["Worktree filesystem path","Worktree metadata (creation time, branch)","Git status information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_5","uri":"capability://automation.workflow.real.time.git.status.and.diff.visualization","name":"real-time-git-status-and-diff-visualization","description":"Monitors Git status in real-time for each worktree using file watching and Git status polling, then renders diffs visually in the UI. The Git Status and File Watching subsystem watches the worktree filesystem for changes, polls git status periodically, and emits events via the Event System. The Diff Visualization component parses git diffs and renders them with syntax highlighting, showing added/removed/modified lines with context.","intents":["See real-time changes made by AI agents in the worktree without manual git status commands","Review diffs before committing AI-generated changes to understand what was modified","Track which files were touched by each AI session for audit and review purposes","Visualize merge conflicts or divergences between parallel AI sessions"],"best_for":["Developers reviewing AI-generated code changes before committing","Teams auditing AI modifications for quality and correctness","Researchers analyzing which files AI agents modify most frequently"],"limitations":["File watching has latency — changes may not appear in UI for 100-500ms depending on OS file system events","Git status polling adds overhead — polling interval (typically 1-5s) creates a trade-off between responsiveness and CPU usage","Large diffs (>10K lines) may cause UI lag when rendering syntax highlighting","Binary file diffs are not supported — only text-based diffs are visualized"],"requires":["Active worktree with Git repository","File system event support (inotify on Linux, FSEvents on macOS)"],"input_types":["Worktree filesystem path","Git repository metadata"],"output_types":["Real-time file change events","Git diff output (unified format)","Rendered diff UI with syntax highlighting"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_6","uri":"capability://memory.knowledge.execution.history.tracking.and.replay","name":"execution-history-tracking-and-replay","description":"Records the execution history of each AI session (prompts, responses, file changes, commands executed) in the SQLite database and enables replay/review of past interactions. The Diff Visualization and Execution History subsystem logs each AI interaction with timestamps, model used, and resulting file modifications. Users can browse historical sessions, view what prompts led to which outputs, and understand the evolution of changes over time.","intents":["Review what prompts led to specific code changes made by AI agents","Understand the reasoning and decision-making process of AI across a session","Audit AI-generated changes for compliance and quality purposes","Replay past sessions to understand how different prompts affected outcomes"],"best_for":["Teams auditing AI-assisted development for compliance and correctness","Researchers analyzing AI decision-making patterns in code generation","Developers debugging why an AI agent made a particular change"],"limitations":["Execution history is stored locally in SQLite — no built-in export or centralized logging","Large session histories (>10K interactions) may slow down database queries","No built-in deduplication — identical prompts/responses are stored multiple times","Replay is read-only — cannot re-execute past prompts to test variations"],"requires":["SQLite database with schema for execution history","Active session with recorded interactions"],"input_types":["Session ID","Time range for filtering history"],"output_types":["Execution history records (prompts, responses, timestamps)","Associated file diffs and changes","Metadata (model used, tokens consumed)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_7","uri":"capability://automation.workflow.terminal.and.pty.management.for.command.execution","name":"terminal-and-pty-management-for-command-execution","description":"Manages pseudo-terminal (PTY) instances for executing commands within worktrees, enabling interactive terminal sessions and real-time output streaming. The Terminal and PTY Management subsystem spawns PTY processes for each session, handles stdin/stdout/stderr streams, and pipes output to the UI via IPC. Supports both Claude Code CLI subprocess execution and general command execution within the worktree context.","intents":["Execute shell commands within AI sessions (e.g., npm install, pytest) without leaving the app","Stream command output in real-time to the UI for immediate feedback","Provide interactive terminal access for debugging AI-generated code","Capture command exit codes and stderr for error handling"],"best_for":["Developers testing AI-generated code without switching to external terminals","Teams integrating Crystal into CI/CD pipelines with automated testing","Researchers analyzing AI code generation quality via automated test execution"],"limitations":["PTY management adds complexity — terminal state is not persisted across app restarts","Interactive terminal sessions (e.g., vim, node REPL) may have rendering issues in the UI","Command execution is synchronous per session — long-running commands block other operations","No built-in timeout mechanism — runaway commands may hang the session indefinitely"],"requires":["PTY support in OS (macOS/Linux)","Shell environment (bash, zsh, etc.) in worktree"],"input_types":["Command string","Working directory (worktree path)","Environment variables"],"output_types":["Streaming stdout/stderr","Exit code","Command execution metadata (duration, timestamp)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_8","uri":"capability://tool.use.integration.inter.process.communication.ipc.for.main.renderer.coordination","name":"inter-process-communication-ipc-for-main-renderer-coordination","description":"Implements secure IPC between Electron's main process (backend services) and renderer process (UI) using Electron's contextBridge and preload script. The preload script (main/src/preload.ts) exposes a typed API surface that allows the renderer to invoke main process services (SessionManager, WorktreeManager, DatabaseService) without direct access to Node.js APIs. All IPC calls are routed through the preload bridge, enforcing security boundaries.","intents":["Enable the UI to trigger backend operations (create session, execute command) securely","Stream real-time updates from backend services to the UI without polling","Maintain type safety between frontend and backend code","Isolate backend services from direct renderer access for security"],"best_for":["Electron app developers building multi-process architectures with security requirements","Teams needing type-safe IPC between main and renderer processes","Applications requiring real-time bidirectional communication between backend and UI"],"limitations":["IPC adds latency (~5-50ms per call) compared to direct function calls","Serialization overhead for complex objects — large data structures may be slow to transfer","Type safety requires manual synchronization between main and renderer type definitions","Debugging IPC issues requires understanding Electron's security model and preload script mechanics"],"requires":["Electron ≥13 (contextBridge support)","TypeScript for type definitions"],"input_types":["Function calls from renderer process","Event subscriptions for real-time updates"],"output_types":["Return values from main process services","Event emissions for state changes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stravu--crystal__cap_9","uri":"capability://memory.knowledge.database.persistence.and.state.management","name":"database-persistence-and-state-management","description":"Persists all session metadata, conversation history, execution logs, and configuration in a SQLite database managed by the DatabaseService. The database schema stores sessions, panels, worktree metadata, AI interactions, and file change history. State changes are persisted immediately to the database, enabling recovery of session state across application restarts. The Event System emits database change events to keep the UI in sync.","intents":["Persist session state so AI conversations survive application restarts","Store execution history for audit and analysis purposes","Query historical sessions and interactions for review and debugging","Maintain consistency between UI state and persisted data"],"best_for":["Applications requiring persistent session management across restarts","Teams needing audit trails and historical data for compliance","Developers building complex state management without external databases"],"limitations":["SQLite is single-process — concurrent writes from multiple Crystal instances may cause lock contention","No built-in backup mechanism — database corruption requires manual recovery","Query performance degrades with large datasets (>100K records) — requires manual indexing","No built-in migration system — schema changes require manual database updates"],"requires":["SQLite 3.x (bundled with Node.js)","Disk space for database file (~10-100MB per 1000 sessions)"],"input_types":["Session metadata","Conversation history","File change records"],"output_types":["Persisted session state","Query results for historical data","Database change events"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Git (any modern version) with worktree support","Claude Code CLI ≥2.0.0 for Claude integration","OpenAI API key for Codex sessions","Anthropic API key for Claude Code sessions","macOS or Linux (Windows not currently supported)","SQLite database (initialized by DatabaseService)","Active session with allocated worktree","API credentials for Claude Code or OpenAI Codex","IPC infrastructure (contextBridge, preload script)","Event emitter implementation in main process"],"failure_modes":["Git worktree isolation adds filesystem overhead — each session duplicates the entire working directory","No built-in conflict resolution when merging parallel session changes back to main branch","Requires manual Git knowledge to understand worktree semantics and cleanup","Performance degrades with very large repositories due to worktree duplication","Panel state is stored in SQLite database — no built-in distributed state management for team collaboration","Context window limits apply per panel; large conversation histories may require pruning","No automatic context sharing between panels — developers must manually reference other panel outputs","Panel switching adds UI latency as conversation history is loaded from database","Event ordering is not guaranteed across IPC boundaries — concurrent events may arrive out of order","No built-in event persistence — missed events during app startup are lost","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.28868689068219366,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:34.540Z","last_commit":"2026-02-26T21:48:10Z"},"community":{"stars":3043,"forks":196,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stravu--crystal","compare_url":"https://unfragile.ai/compare?artifact=stravu--crystal"}},"signature":"Tdi5PaO6nykZmXKxdCjqoiuFhBWr2dxva3eyxwolQ0z0QZCyUvRqJvvklYErvSsqelU35fSrvUeDL9OA2HF6CA==","signedAt":"2026-06-22T01:49:56.108Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stravu--crystal","artifact":"https://unfragile.ai/stravu--crystal","verify":"https://unfragile.ai/api/v1/verify?slug=stravu--crystal","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"}}