{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-skalesapp--skales","slug":"skalesapp--skales","name":"skales","type":"agent","url":"https://skales.app","page_url":"https://unfragile.ai/skalesapp--skales","categories":["ai-agents"],"tags":["ai-agent","ai-assistant","ai-companions","automation","autonomous-agent","calendar","code-generation","desktop-app","desktop-buddy","electron","linux","llm","local-ai","macos","ollama","privacy","productivity","typescript","windows","wordpress"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-skalesapp--skales__cap_0","uri":"capability://planning.reasoning.react.loop.agent.orchestration.with.multi.provider.llm.routing","name":"react-loop agent orchestration with multi-provider llm routing","description":"Implements a Reason-Act-Observe loop that chains LLM reasoning with tool execution across 15+ AI providers (OpenAI, Anthropic, Ollama, etc.). The agent maintains a unified provider abstraction layer that normalizes function-calling schemas and response formats, enabling seamless provider switching without code changes. Tool execution results feed back into the reasoning loop for iterative refinement.","intents":["I want to build an agent that can reason about tasks and execute tools without being locked into a single LLM provider","I need an agent that can switch between local Ollama and cloud providers like OpenAI based on task complexity or cost","I want to understand how my agent is reasoning through multi-step problems with full observability"],"best_for":["developers building privacy-first desktop agents","teams wanting provider-agnostic agent infrastructure","solo developers prototyping autonomous workflows without cloud dependency"],"limitations":["ReAct loop adds latency per reasoning step (typically 500ms-2s depending on provider)","No built-in caching of reasoning chains — each loop iteration re-evaluates context","Provider-specific function-calling quirks (e.g., Anthropic vs OpenAI schema differences) require normalization overhead"],"requires":["API key for at least one provider (OpenAI, Anthropic, etc.) OR local Ollama instance running","Electron runtime (bundled with desktop app)","Node.js 18+ for development"],"input_types":["natural language task descriptions","structured tool schemas (JSON)","conversation history with multi-turn context"],"output_types":["agent reasoning trace (thought process)","tool invocation payloads","final response text","execution logs with timestamps"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_1","uri":"capability://planning.reasoning.autonomous.autopilot.with.ooda.self.correction.loop","name":"autonomous autopilot with ooda self-correction loop","description":"Implements an Observe-Orient-Decide-Act state machine that enables fully autonomous task execution with built-in error detection and self-correction. The agent observes task outcomes, re-orients its understanding if results deviate from expectations, decides on corrective actions, and re-executes. Safe Mode requires explicit user approval before autonomous actions modify system state.","intents":["I want my agent to autonomously complete multi-step tasks but with a safety net that prevents destructive actions","I need the agent to detect when a task failed and automatically retry with a different approach","I want to audit what the agent did autonomously and approve/reject actions before they execute"],"best_for":["teams deploying agents in production with governance requirements","non-technical users who want automation without understanding implementation details","developers building fail-safe autonomous workflows"],"limitations":["OODA loop adds 2-5 second overhead per correction cycle due to re-reasoning","Safe Mode requires synchronous user interaction — cannot be fully autonomous in approval-required scenarios","Self-correction is heuristic-based; complex failures may require manual intervention"],"requires":["Active LLM provider connection (cloud or local Ollama)","Desktop app running (Autopilot is tray-resident, not headless)","User approval workflow enabled in settings"],"input_types":["task descriptions with success criteria","execution context (current desktop state, file system)","error messages and task outcome data"],"output_types":["task completion status (success/failed/retry)","audit log of autonomous actions taken","approval requests (in Safe Mode)","corrective action rationale"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_10","uri":"capability://tool.use.integration.calendar.and.email.integration.with.planner.ai","name":"calendar and email integration with planner ai","description":"Integrates with calendar systems (Google Calendar, Outlook, iCal) and email (IMAP/SMTP) to enable agents to read schedules, propose meetings, send emails, and manage tasks. Planner AI is a specialized agent that understands calendar context and can autonomously schedule meetings, send reminders, and coordinate across attendees. Supports natural language scheduling (e.g., 'schedule a meeting with John next Tuesday at 2 PM').","intents":["I want my agent to autonomously schedule meetings based on calendar availability","I need the agent to send emails and follow up on tasks without manual intervention","I want natural language scheduling where I say 'meet with John next week' and the agent handles the details"],"best_for":["executives and managers automating scheduling and email management","teams using agents for meeting coordination and task tracking","developers building calendar-aware agent workflows"],"limitations":["Calendar integration requires OAuth tokens — setup complexity for multiple calendar providers","No built-in conflict detection across multiple calendars — may double-book if attendees have hidden calendars","Email sending is one-way — no built-in email parsing for complex message threads","Natural language scheduling is heuristic-based — ambiguous requests may be misinterpreted"],"requires":["Calendar provider credentials (Google, Outlook, iCal URL)","Email provider credentials (IMAP/SMTP server, OAuth token)","Planner AI enabled and configured with calendar access"],"input_types":["natural language scheduling requests","email content and recipients","calendar event details","attendee availability data"],"output_types":["calendar event creation confirmations","email send confirmations","scheduling conflict alerts","meeting summaries and reminders"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_11","uri":"capability://automation.workflow.desktop.buddy.mascot.with.fsm.based.personality.and.notification.routing","name":"desktop buddy mascot with fsm-based personality and notification routing","description":"A persistent desktop mascot (animated character) that represents the agent's state and personality. The Buddy uses a Finite State Machine (FSM) to transition between states (idle, thinking, speaking, error) with corresponding animations and sounds. Notifications are routed through the Buddy (desktop toast, sound, animation) with intelligent prioritization. The Buddy can be clicked to open the chat interface or dismissed.","intents":["I want a visual indicator of what my agent is doing without opening the chat window","I need notifications that are non-intrusive but still noticeable","I want my agent to have a personality and presence on my desktop"],"best_for":["users who want always-visible agent status without opening the app","teams deploying agents with user-friendly interfaces","developers building engaging agent UX"],"limitations":["Mascot animations consume GPU resources — may impact performance on low-end systems","FSM state transitions are predefined — limited customization of Buddy behavior","Notification routing is heuristic-based — may miss important alerts if priority logic is misconfigured","Buddy is desktop-only — no mobile equivalent"],"requires":["Desktop environment with GPU acceleration (optional but recommended)","Electron runtime (bundled with app)","Audio output for notification sounds"],"input_types":["agent state changes (idle, thinking, speaking, error)","notification events (task complete, error, reminder)","user interactions (click, hover)"],"output_types":["animated Buddy state transitions","notification toast messages","notification sounds","chat window launch"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_12","uri":"capability://code.generation.editing.lio.ai.code.builder.with.multi.ai.code.generation.and.review","name":"lio ai code builder with multi-ai code generation and review","description":"A specialized code generation and review system that coordinates multiple AI models for different coding tasks. One model generates code, another reviews it for bugs and style, a third optimizes for performance. Supports 40+ programming languages with language-specific linting and formatting. Integrates with local development environments (Git, package managers, test runners) to validate generated code.","intents":["I want AI to generate code and have it automatically reviewed for quality before I use it","I need code generation that understands my project structure and coding standards","I want to generate code in multiple languages with confidence that it's correct"],"best_for":["developers accelerating coding with AI assistance and built-in review","teams enforcing code quality standards through AI review","polyglot projects needing consistent code generation across languages"],"limitations":["Multi-model review adds 2-5 second latency per code generation","Language-specific linting requires local toolchain installation (eslint, pylint, etc.)","Generated code may not match project conventions despite review — requires manual adjustment","No built-in test generation — validation is limited to linting and style checks"],"requires":["Multiple LLM providers (at least 2 for generation + review workflow)","Language-specific linters and formatters (eslint, black, rustfmt, etc.)","Git repository for context (project structure, recent commits)","Local development environment (Node.js, Python, Rust, etc. as needed)"],"input_types":["code generation prompts (natural language or structured)","project context (files, dependencies, coding standards)","target programming language","code snippets for context"],"output_types":["generated code (source files)","code review feedback (bugs, style issues, performance suggestions)","linting and formatting reports","test results (if tests are available)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_13","uri":"capability://planning.reasoning.agent.swarm.with.mdns.discovery.and.peer.to.peer.coordination","name":"agent swarm with mdns discovery and peer-to-peer coordination","description":"Enables multiple Skales instances on a local network to discover each other via mDNS (Bonjour) and coordinate as a swarm. Agents can delegate tasks to peers, share memory and skills, and load-balance work across the network. No central server required — coordination is peer-to-peer. Useful for distributed teams or multi-device setups.","intents":["I want multiple agents on my network to work together without a central server","I need agents to discover each other automatically and delegate tasks","I want to scale agent capacity across multiple machines without infrastructure"],"best_for":["teams with multiple Skales instances wanting to coordinate","developers building distributed agent systems on local networks","organizations wanting agent swarms without cloud infrastructure"],"limitations":["mDNS discovery is limited to local network — no cross-internet coordination","No built-in consensus mechanism — swarm decisions are heuristic-based","Network latency between agents adds overhead — slower than single-machine coordination","No persistent swarm state — if an agent goes offline, its tasks may be lost"],"requires":["Multiple Skales instances on the same local network","mDNS support enabled on network (usually default)","Network connectivity between agents (no firewalls blocking mDNS ports)"],"input_types":["task descriptions with swarm delegation hints","agent capability declarations","peer discovery signals (mDNS broadcasts)"],"output_types":["swarm membership list (discovered agents)","task delegation decisions","shared memory updates across swarm","load balancing metrics"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_14","uri":"capability://memory.knowledge.local.first.data.persistence.with.skales.data.directory.and.no.cloud.sync","name":"local-first data persistence with ~/.skales-data directory and no cloud sync","description":"All user data (conversations, memories, API keys, settings, task history) is stored exclusively in ~/.skales-data on the user's machine. No cloud sync, no telemetry, no data transmission to external servers (except to configured LLM providers). Data is organized hierarchically: conversations/, memory/, skills/, tasks/, config/. Users can manually backup or migrate data by copying the directory.","intents":["I want complete control over my agent data and privacy — no cloud storage or sync","I need to backup my agent's memory and conversations locally","I want to migrate my agent to a new machine by copying a directory"],"best_for":["privacy-conscious users who distrust cloud storage","organizations with data residency requirements","developers building agents for offline or air-gapped environments"],"limitations":["No automatic cloud backup — data loss if local disk fails","No cross-device sync — agent state doesn't follow user between machines","Manual migration required for multi-machine setups","Disk space requirements grow with conversation history — no built-in archival"],"requires":["Sufficient local disk space (typically 100MB-1GB depending on conversation volume)","File system with POSIX permissions (Linux/macOS) or NTFS (Windows)","User home directory write access"],"input_types":["conversation messages","memory facts and preferences","API keys and provider credentials","skill definitions and custom tools"],"output_types":["persisted conversation history (JSON)","memory index (structured facts)","configuration files (YAML/JSON)","task execution logs"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_15","uri":"capability://text.generation.language.multi.language.internationalization.i18n.with.locale.specific.formatting","name":"multi-language internationalization (i18n) with locale-specific formatting","description":"Full internationalization support for UI, agent responses, and system messages across 20+ languages. Locale-specific formatting for dates, times, numbers, and currency. Agent responses can be generated in the user's preferred language. Settings page allows language selection with instant UI refresh.","intents":["I want to use Skales in my native language, not just English","I need dates, times, and numbers formatted according to my locale","I want my agent to respond in my preferred language"],"best_for":["international teams using Skales across different locales","non-English-speaking users","organizations deploying agents globally"],"limitations":["Agent response language depends on LLM provider support — not all models support all languages equally","Translation quality varies by language — some languages may have fewer translations","Locale-specific formatting requires system locale configuration","Custom skills may not have translations — falls back to English"],"requires":["Language selection in settings","LLM provider that supports target language","System locale configuration (for date/time/number formatting)"],"input_types":["language preference selection","locale identifier (e.g., 'en-US', 'de-DE', 'ja-JP')","user messages in any language"],"output_types":["UI text in selected language","agent responses in selected language","locale-formatted dates, times, numbers","translated error messages"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_2","uri":"capability://automation.workflow.always.on.cron.scheduled.agent.with.persistent.task.queue","name":"always-on cron-scheduled agent with persistent task queue","description":"Runs a background agent process that executes scheduled tasks on cron expressions without requiring the main UI to be active. Tasks are persisted in ~/.skales-data with execution history, enabling long-running automations (e.g., daily reports, periodic monitoring). The agent maintains a task queue that survives app restarts and executes on schedule even if the desktop is locked.","intents":["I want to schedule my agent to run tasks at specific times (e.g., every morning at 9 AM) without keeping the app open","I need reliable task execution that persists across app restarts and system reboots","I want to monitor scheduled task execution history and debug why a task failed"],"best_for":["users automating recurring workflows (daily reports, periodic data collection)","teams deploying agents for background monitoring and alerting","developers building always-on desktop automation without server infrastructure"],"limitations":["Cron execution is tied to desktop app running — if app is closed, scheduled tasks won't execute","No distributed task queue — single-machine execution only, no horizontal scaling","Task execution latency depends on system load and LLM provider availability"],"requires":["Skales desktop app installed and configured to auto-start on system boot","At least one LLM provider configured (local Ollama or cloud API key)","Sufficient disk space for task queue and execution logs in ~/.skales-data"],"input_types":["cron expression (e.g., '0 9 * * *' for 9 AM daily)","task description and success criteria","context data (files, URLs, parameters)"],"output_types":["task execution logs with timestamps","task result (success/failure/partial)","notifications (desktop, Telegram, Discord, etc.)","structured execution metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_3","uri":"capability://memory.knowledge.bi.temporal.memory.system.with.auto.extracted.facts.and.preferences","name":"bi-temporal memory system with auto-extracted facts and preferences","description":"Maintains a dual-layer memory architecture: episodic memory (conversation history with timestamps) and semantic memory (auto-extracted facts, preferences, and user identity). A Memory Scanner runs periodically to extract structured facts from conversations (e.g., 'user prefers Python over JavaScript', 'user works at Acme Corp') and injects them into future agent reasoning. The Soul system maintains a persistent human identity profile that evolves over time.","intents":["I want my agent to remember my preferences and context across conversations without me re-explaining","I need the agent to extract actionable facts from our conversations and use them to personalize responses","I want to audit what the agent has learned about me and correct misunderstandings"],"best_for":["users building long-term relationships with their agent (weeks/months of interaction)","teams deploying agents that need to maintain consistent user context across sessions","developers building personalized AI experiences without external knowledge bases"],"limitations":["Memory extraction is heuristic-based — may extract incorrect or outdated facts","No automatic memory decay or forgetting — old preferences persist indefinitely unless manually edited","Memory injection adds ~100-200ms latency per agent reasoning step due to retrieval and context assembly","No built-in conflict resolution when extracted facts contradict each other"],"requires":["Active LLM provider for Memory Scanner fact extraction","Sufficient disk space in ~/.skales-data for conversation history and memory index","At least one multi-turn conversation to bootstrap memory extraction"],"input_types":["conversation messages (text, voice transcripts)","user identity hints (name, role, organization)","explicit preference statements"],"output_types":["extracted facts (structured key-value pairs)","user identity profile (Soul)","memory retrieval results (relevant facts for current context)","memory audit log"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_4","uri":"capability://tool.use.integration.built.in.agentic.browser.with.web.automation.and.screenshot.vision","name":"built-in agentic browser with web automation and screenshot vision","description":"Integrates a Chromium-based browser controlled by the agent via tool calls (navigate, click, fill forms, extract text). The agent can take screenshots and analyze them with vision-capable models (Claude Vision, GPT-4V) to understand page state and make decisions. Supports JavaScript execution for dynamic content and cookie/session management for authenticated workflows.","intents":["I want my agent to autonomously browse websites and extract information without manual intervention","I need the agent to fill out web forms, submit data, and navigate multi-step workflows","I want the agent to understand visual page layouts and click on elements based on what it sees, not just HTML selectors"],"best_for":["developers automating web scraping and data extraction workflows","teams building agents for customer support (e.g., ticket submission, form filling)","users automating repetitive web-based tasks (expense reporting, data entry)"],"limitations":["Vision analysis adds 1-3 second latency per screenshot due to model inference","No built-in anti-bot detection evasion — may be blocked by sites with strict bot detection","JavaScript execution is sandboxed — cannot access browser DevTools or execute arbitrary code","Screenshot-based navigation is slower than direct DOM manipulation but more robust to UI changes"],"requires":["Vision-capable LLM provider (Claude 3.5+, GPT-4V, or local multimodal model)","Chromium runtime (bundled with Electron)","Sufficient RAM for browser process (~200-500MB per browser instance)"],"input_types":["URLs and navigation instructions","form data (key-value pairs)","CSS selectors or visual descriptions of elements to interact with","JavaScript code snippets for page manipulation"],"output_types":["extracted text from web pages","structured data (tables, lists)","screenshots (PNG/JPEG)","page state analysis (form fields, buttons, links)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_5","uri":"capability://planning.reasoning.multi.agent.team.coordination.with.group.chat.and.skill.dispatch","name":"multi-agent team coordination with group chat and skill dispatch","description":"Enables multiple specialized agents to collaborate within a single conversation through a Group Chat interface. Each agent has a distinct role (e.g., 'code reviewer', 'documentation writer', 'tester') and can be invoked via @mentions. The Skill Dispatcher routes tasks to agents based on their declared capabilities, and agents can delegate subtasks to each other. Conversation history is shared across the team for context.","intents":["I want multiple AI agents with different specializations to collaborate on a complex project","I need agents to hand off work to each other (e.g., developer writes code, reviewer checks it, tester validates)","I want to see the full conversation between agents and understand how they divided the work"],"best_for":["teams building complex software with multiple specialized agents","developers prototyping multi-agent workflows without orchestration frameworks","non-technical users coordinating AI agents for content creation, analysis, etc."],"limitations":["No built-in conflict resolution when agents disagree on approach","Shared conversation history can become unwieldy with many agents and long interactions","Skill dispatch is rule-based (capability matching) — no learned routing optimization","Coordination overhead increases with team size (more agents = more negotiation rounds)"],"requires":["Multiple LLM provider connections (agents can use different providers)","Skill definitions for each agent (SKILL.md format)","Sufficient context window to maintain shared conversation history"],"input_types":["task descriptions with @mentions of target agents","shared context (project files, requirements)","agent role definitions and capabilities"],"output_types":["group conversation transcript","task completion status per agent","delegation graph (who handed off to whom)","final deliverables from coordinated agents"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_6","uri":"capability://tool.use.integration.custom.skill.creation.with.skill.zip.packaging.and.skill.ai","name":"custom skill creation with .skill.zip packaging and skill ai","description":"Allows users to extend agent capabilities by creating custom skills as .skill.zip packages containing tool definitions, prompts, and optional code. Skill AI is an AI-assisted skill builder that generates skill scaffolding from natural language descriptions. Skills are registered in a local registry and automatically discovered by the agent. Supports both declarative (JSON schema) and imperative (TypeScript) skill definitions.","intents":["I want to add custom tools to my agent without modifying core code","I need to package and share reusable skills with my team","I want AI to help me generate skill boilerplate from a description"],"best_for":["developers extending Skales with domain-specific tools","teams building skill libraries for their organization","non-technical users creating skills with Skill AI assistance"],"limitations":["Skills are single-machine only — no built-in skill distribution across agent swarms","No skill versioning or dependency management — conflicts possible if multiple skills define same tool","Skill AI generation is heuristic-based — generated scaffolding may require manual refinement","TypeScript skills require compilation; JSON-only skills have limited expressiveness"],"requires":["Skill definition template (SKILL.md format)","For TypeScript skills: Node.js 18+ and TypeScript compiler","For Skill AI: Active LLM provider connection"],"input_types":["natural language skill description (for Skill AI)","tool schema (JSON Schema format)","TypeScript or JavaScript implementation code","prompt templates for skill behavior"],"output_types":["generated skill scaffolding (.skill.zip)","skill registry entry","tool invocation results","skill usage logs"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_7","uri":"capability://text.generation.language.voice.pipeline.with.stt.tts.and.voice.activity.detection","name":"voice pipeline with stt/tts and voice activity detection","description":"Implements a full-duplex voice interface with Speech-to-Text (STT), Text-to-Speech (TTS), and Voice Activity Detection (VAD). Users can speak naturally to the agent, and the agent responds with synthesized speech. VAD automatically detects when the user stops speaking and triggers agent response, eliminating the need for manual 'send' buttons. Supports multiple TTS voices and languages.","intents":["I want to interact with my agent using voice instead of typing","I need the agent to respond with natural-sounding speech","I want hands-free operation where the agent detects when I'm done speaking"],"best_for":["users with accessibility needs (visual impairment, mobility constraints)","developers building voice-first agent interfaces","teams automating voice-based workflows (customer support, dictation)"],"limitations":["STT accuracy depends on audio quality and background noise — may require quiet environment","VAD latency adds 200-500ms delay before agent response begins","TTS synthesis is slower than text output — full response may take 5-10 seconds for long text","No built-in speaker identification — cannot distinguish between multiple users"],"requires":["Microphone and speaker hardware","STT provider (OpenAI Whisper API, local Whisper model, or cloud provider)","TTS provider (ElevenLabs, Google Cloud, local TTS, or system TTS)","VAD model (local or cloud-based)"],"input_types":["audio stream (PCM, WAV, MP3)","voice activity detection signals","language preference"],"output_types":["transcribed text from speech","synthesized audio (MP3, WAV)","confidence scores for transcription","VAD state changes"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_8","uri":"capability://automation.workflow.desktop.automation.with.system.file.access.and.keyboard.mouse.control","name":"desktop automation with system file access and keyboard/mouse control","description":"Grants agents controlled access to the desktop environment: file system operations (read/write/delete with permission checks), keyboard input simulation, mouse movement and clicking, window management, and clipboard access. All file operations are scoped to user-approved directories to prevent accidental data loss. Safe Mode requires approval for destructive operations (delete, overwrite).","intents":["I want my agent to automate desktop tasks like opening files, typing, and clicking","I need the agent to read and modify files on my system while respecting privacy boundaries","I want safety guardrails that prevent the agent from accidentally deleting important files"],"best_for":["users automating repetitive desktop workflows (file organization, data entry)","developers building desktop RPA agents","teams deploying agents for system administration tasks"],"limitations":["File access is scoped to approved directories — cannot access system files or other users' data by design","Keyboard/mouse simulation is slow compared to direct API calls — may be unreliable for timing-sensitive UIs","No built-in OCR for screen reading — agent must rely on accessibility APIs or vision models","Safe Mode approval adds latency for destructive operations"],"requires":["Appropriate OS permissions (file system access, input simulation)","User approval for directory access (configured in settings)","Desktop environment with accessibility APIs (Windows UIA, macOS Accessibility, Linux AT-SPI)"],"input_types":["file paths (relative to approved directories)","keyboard input sequences","mouse coordinates or window identifiers","clipboard content"],"output_types":["file read results (text, binary)","operation status (success/failure)","desktop state snapshots","clipboard content"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-skalesapp--skales__cap_9","uri":"capability://tool.use.integration.communication.bridges.with.telegram.whatsapp.discord.and.email","name":"communication bridges with telegram, whatsapp, discord, and email","description":"Enables remote interaction with the Skales agent through messaging platforms (Telegram, WhatsApp, Discord) and email. Messages are routed to the agent, processed, and responses are sent back through the same channel. Supports media sharing (images, files) and maintains conversation context across platforms. 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