{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_layerbrain","slug":"layerbrain","name":"Layerbrain","type":"product","url":"https://layerbrain.com","page_url":"https://unfragile.ai/layerbrain","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_layerbrain__cap_0","uri":"capability://automation.workflow.natural.language.to.ui.action.translation","name":"natural-language-to-ui-action-translation","description":"Converts free-form natural language commands into executable UI interactions by parsing user intent and mapping it to software-specific action sequences. The system likely uses intent recognition (possibly LLM-based) to understand user goals, then translates those into low-level UI automation primitives like clicks, keyboard input, and form fills across integrated applications. This bridges the gap between conversational user intent and deterministic software actions.","intents":["I want to automate repetitive UI tasks without writing scripts or macros","I need to control multiple applications through voice or text commands instead of manual clicking","I want to reduce context switching by issuing commands conversationally while staying in my current workflow"],"best_for":["power users and developers seeking to eliminate mouse-and-keyboard overhead","knowledge workers performing repetitive UI-based tasks across multiple SaaS tools","teams evaluating conversational automation as an alternative to traditional RPA"],"limitations":["Natural language ambiguity may cause incorrect action execution without explicit confirmation flows","Accuracy degrades with complex multi-step workflows requiring conditional logic or error handling","Limited to applications with supported integrations; unsupported software cannot be controlled","No built-in rollback or undo mechanism if commands execute unintended actions"],"requires":["Active Layerbrain account (free tier available)","Supported application with active integration (specific list not publicly detailed)","Network connectivity to Layerbrain cloud service"],"input_types":["natural language text commands","voice input (if voice-to-text preprocessing is supported)"],"output_types":["UI state changes in target application","execution logs or confirmation messages"],"categories":["automation-workflow","natural-language-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_1","uri":"capability://automation.workflow.multi.application.command.orchestration","name":"multi-application-command-orchestration","description":"Enables single natural language commands to trigger coordinated actions across multiple integrated software applications in sequence or parallel. The system must maintain state across application boundaries, handle inter-app data passing (e.g., copying data from one app to another), and manage timing/dependencies between actions. This likely involves a command orchestration layer that decomposes high-level user intent into application-specific sub-commands.","intents":["I want to create a task in my project management tool and simultaneously post an update to Slack without switching windows","I need to pull data from one SaaS application, transform it, and push it to another application in a single command","I want to automate cross-application workflows that currently require manual copy-paste and context switching"],"best_for":["teams using fragmented SaaS stacks (e.g., Jira + Slack + Notion + Google Workspace)","developers building integration workflows without learning Make/Zapier visual builders","knowledge workers seeking to eliminate manual data shuttling between tools"],"limitations":["Orchestration latency increases with number of applications involved; no published performance benchmarks","Data transformation between apps is limited to simple pass-through; complex ETL requires external tools","Timing dependencies and error handling in multi-app sequences are not explicitly documented","No transaction semantics; partial failures may leave applications in inconsistent states"],"requires":["Integration support for all target applications (ecosystem breadth unknown)","API credentials or OAuth tokens for each integrated application","Layerbrain account with multi-app orchestration enabled"],"input_types":["natural language command referencing multiple applications","implicit data context from user's current application state"],"output_types":["coordinated state changes across multiple applications","execution summary or error report"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_2","uri":"capability://planning.reasoning.context.aware.command.interpretation","name":"context-aware-command-interpretation","description":"Interprets natural language commands with awareness of the user's current application context, active window, and recent actions to disambiguate intent. The system likely maintains a context stack tracking which application is in focus, what data is selected, and recent operations, allowing commands like 'send this to Slack' to implicitly reference the current selection without explicit specification. This reduces command verbosity and improves usability.","intents":["I want to issue commands that implicitly reference my current selection or active application without repeating context","I need the system to understand 'send this' or 'create a task from this' based on what I'm currently viewing","I want to chain commands where each one builds on the previous action's output"],"best_for":["power users who issue rapid-fire commands and expect the system to track context","developers building conversational automation workflows with implicit data flow","teams using Layerbrain as a command-line alternative to traditional UI navigation"],"limitations":["Context tracking may fail if user switches applications rapidly or uses multiple windows","Ambiguous commands without sufficient context may execute unintended actions","Context window size is unknown; very long command chains may lose earlier context","No explicit context reset or clarification mechanism documented"],"requires":["Layerbrain client or browser extension with window/focus detection capability","Supported application with context-aware integration hooks","User's active application must be in Layerbrain's supported integration list"],"input_types":["natural language command with implicit context references","current application state (window focus, selection, clipboard)"],"output_types":["contextually-aware action execution","confirmation message showing interpreted context"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_3","uri":"capability://tool.use.integration.application.integration.registry.and.discovery","name":"application-integration-registry-and-discovery","description":"Maintains a registry of supported applications and their available actions, allowing users to discover what commands are possible within Layerbrain's ecosystem. The system likely exposes application capabilities through a schema or capability model that the natural language interpreter uses to validate and execute commands. This may include dynamic capability discovery if applications expose their own action schemas via API.","intents":["I want to know what applications Layerbrain can control and what actions are available in each","I need to understand if my specific SaaS tool is supported before committing to Layerbrain","I want to explore available commands for an application without reading documentation"],"best_for":["teams evaluating Layerbrain's integration breadth for their specific SaaS stack","developers building custom integrations or extending Layerbrain's capabilities","power users seeking to discover advanced commands within supported applications"],"limitations":["Integration ecosystem breadth is unknown; likely significantly smaller than Zapier (7000+ apps) or Make (1000+ apps)","No public documentation of supported applications or their action schemas","Custom integration development process is not documented; may require Layerbrain team involvement","Registry updates may lag behind application API changes"],"requires":["Access to Layerbrain's application registry (in-product or via API)","Layerbrain account to view supported integrations"],"input_types":["application name or category query","action capability search"],"output_types":["list of supported applications with available actions","action schema or capability documentation"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_4","uri":"capability://text.generation.language.natural.language.command.parsing.and.validation","name":"natural-language-command-parsing-and-validation","description":"Parses free-form natural language commands to extract intent, entities, and parameters, then validates them against the application registry before execution. The system likely uses NLP/LLM-based intent classification to map user utterances to registered application actions, with fallback mechanisms for ambiguous or unrecognized commands. Validation ensures commands are executable before attempting to run them, reducing failed executions.","intents":["I want to issue commands in natural language without worrying about exact syntax or parameter order","I need the system to clarify ambiguous commands before executing them rather than failing silently","I want feedback on whether my command is valid before it executes"],"best_for":["non-technical users who expect natural language interfaces to be forgiving of phrasing variations","developers building conversational automation who need robust intent recognition","teams using Layerbrain as a voice-first interface where exact phrasing is difficult"],"limitations":["Parsing accuracy degrades with complex or ambiguous commands; no published accuracy metrics","Synonymy and paraphrasing support is unknown; may require exact phrasing for some commands","No explicit error messages for unrecognized commands; user experience on failures is unclear","LLM-based parsing may introduce latency (likely 500ms-2s per command) and cost per inference"],"requires":["Layerbrain backend with NLP/LLM inference capability","Application registry with action schemas for validation","Network connectivity for cloud-based parsing (if not local)"],"input_types":["natural language text command","voice input (if voice-to-text preprocessing is supported)"],"output_types":["parsed intent and parameters","validation result (valid/invalid/ambiguous)","clarification prompt if needed"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_5","uri":"capability://safety.moderation.execution.confirmation.and.safety.gates","name":"execution-confirmation-and-safety-gates","description":"Implements confirmation flows and safety mechanisms to prevent unintended command execution, particularly for high-risk actions like deletions or bulk updates. The system may require explicit user confirmation before executing commands, show previews of intended actions, or implement dry-run modes. This is critical for natural language interfaces where ambiguity could lead to destructive actions.","intents":["I want to review what action will be taken before Layerbrain executes a command","I need safeguards against accidental destructive actions triggered by ambiguous commands","I want to test commands in dry-run mode before executing them against real data"],"best_for":["teams using Layerbrain for production workflows where mistakes are costly","developers building safety-critical automation with natural language interfaces","risk-averse organizations evaluating conversational automation"],"limitations":["Confirmation flows add latency and friction to command execution; may reduce adoption for power users","Dry-run capability depends on target application's API support; not all SaaS tools support preview/rollback","Risk classification (which commands require confirmation) is not documented; may be overly conservative or permissive","No audit trail or execution history documented for compliance/debugging"],"requires":["Layerbrain backend with confirmation/approval workflow engine","Target application API support for dry-run or preview operations","User interface for displaying action previews and confirmation prompts"],"input_types":["parsed command ready for execution","user confirmation or rejection"],"output_types":["action preview or dry-run result","execution confirmation prompt","execution result or rollback confirmation"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_6","uri":"capability://memory.knowledge.command.history.and.replay","name":"command-history-and-replay","description":"Maintains a history of executed commands with their parameters, results, and timestamps, allowing users to replay, modify, and reuse previous commands. This enables command discovery through history search, debugging of failed executions, and rapid re-execution of common workflows. The system likely stores command metadata (intent, parameters, execution result) for audit and replay purposes.","intents":["I want to find and re-execute a command I ran earlier without retyping it","I need to debug why a command failed by reviewing its execution history","I want to create a template from a previously-executed command and reuse it with different parameters"],"best_for":["power users who execute similar commands repeatedly and want to avoid retyping","teams needing audit trails for compliance or debugging","developers building command-driven workflows who want to discover patterns in their usage"],"limitations":["History storage and search performance at scale is unknown; no published limits on history retention","Privacy implications of storing command history (may include sensitive data) are not addressed","Replay functionality may fail if target application state has changed since original execution","No documented mechanism for exporting or sharing command history across team members"],"requires":["Layerbrain backend with persistent command history storage","User interface for browsing and searching command history","Replay engine capable of re-executing stored commands"],"input_types":["command history query or search","selected command for replay or modification"],"output_types":["command history list with metadata","command replay result","command template for modification"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_7","uri":"capability://safety.moderation.error.handling.and.recovery","name":"error-handling-and-recovery","description":"Implements error detection, reporting, and recovery mechanisms for failed command executions. The system must distinguish between user error (ambiguous command), application error (API failure), and system error (Layerbrain service issue), then provide actionable recovery suggestions. This may include automatic retry logic, fallback actions, or detailed error messages guiding users to resolution.","intents":["I want clear error messages when a command fails, not silent failures","I need the system to automatically retry transient failures (network timeouts, rate limits)","I want suggestions on how to fix or rephrase a command that failed due to ambiguity"],"best_for":["teams using Layerbrain for critical workflows where failures must be visible and recoverable","developers building automation who need detailed error diagnostics","non-technical users who need guidance on fixing failed commands"],"limitations":["Error classification and recovery suggestions depend on application-specific error handling; not all integrations may support detailed error reporting","Automatic retry logic may mask transient issues or cause duplicate actions if not carefully implemented","No documented SLA for error detection or recovery time","User experience on errors is not documented; may be confusing or unhelpful"],"requires":["Layerbrain backend with error detection and classification logic","Application integrations with detailed error reporting (not all APIs provide this)","Retry and recovery policy configuration"],"input_types":["failed command execution with error details"],"output_types":["error classification and message","recovery suggestions or automatic retry","execution log for debugging"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_layerbrain__cap_8","uri":"capability://text.generation.language.voice.command.input.and.processing","name":"voice-command-input-and-processing","description":"Accepts voice input via microphone or voice API, converts speech to text, and processes it as natural language commands. The system likely integrates with speech-to-text services (e.g., Whisper, Google Speech-to-Text) and may include voice-specific optimizations like wake words, command-specific acoustic models, or speaker identification. This enables hands-free automation for accessibility and convenience.","intents":["I want to control software via voice commands while my hands are occupied or I'm away from keyboard","I need accessibility features for users who cannot use traditional keyboard/mouse input","I want to issue rapid-fire voice commands without context switching to a text interface"],"best_for":["accessibility-focused teams building inclusive automation tools","power users seeking hands-free workflow control","developers building voice-first interfaces for knowledge work"],"limitations":["Voice input accuracy depends on speech-to-text service quality; background noise and accents may degrade performance","Latency from speech capture to command execution may be 2-5 seconds, slower than text input","Voice commands are inherently more ambiguous than text; confirmation flows become critical","Privacy concerns with continuous voice recording; data handling practices not documented"],"requires":["Microphone or voice API integration (e.g., browser microphone access, Twilio, etc.)","Speech-to-text service (Whisper, Google Speech-to-Text, etc.)","Voice-specific NLP processing for command parsing"],"input_types":["audio stream from microphone or voice API"],"output_types":["transcribed text command","command execution result"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active Layerbrain account (free tier available)","Supported application with active integration (specific list not publicly detailed)","Network connectivity to Layerbrain cloud service","Integration support for all target applications (ecosystem breadth unknown)","API credentials or OAuth tokens for each integrated application","Layerbrain account with multi-app orchestration enabled","Layerbrain client or browser extension with window/focus detection capability","Supported application with context-aware integration hooks","User's active application must be in Layerbrain's supported integration list","Access to Layerbrain's application registry (in-product or via API)"],"failure_modes":["Natural language ambiguity may cause incorrect action execution without explicit confirmation flows","Accuracy degrades with complex multi-step workflows requiring conditional logic or error handling","Limited to applications with supported integrations; unsupported software cannot be controlled","No built-in rollback or undo mechanism if commands execute unintended actions","Orchestration latency increases with number of applications involved; no published performance benchmarks","Data transformation between apps is limited to simple pass-through; complex ETL requires external tools","Timing dependencies and error handling in multi-app sequences are not explicitly documented","No transaction semantics; partial failures may leave applications in inconsistent states","Context tracking may fail if user switches applications rapidly or uses multiple windows","Ambiguous commands without sufficient context may execute unintended actions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:31.446Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=layerbrain","compare_url":"https://unfragile.ai/compare?artifact=layerbrain"}},"signature":"YZnSw0HJflGSZmbgIFi0S8i09EHnAo4FFHMo/YB/Xsh8ky67OFxbmVKvPBU21NuGwTbrpLifKhPzETovGUUbDg==","signedAt":"2026-06-21T14:19:27.285Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/layerbrain","artifact":"https://unfragile.ai/layerbrain","verify":"https://unfragile.ai/api/v1/verify?slug=layerbrain","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"}}