{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-self-operating-computer","slug":"self-operating-computer","name":"Self-operating computer","type":"agent","url":"https://www.hyperwriteai.com/self-operating-computer","page_url":"https://unfragile.ai/self-operating-computer","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-self-operating-computer__cap_0","uri":"capability://automation.workflow.multimodal.vision.based.computer.control","name":"multimodal-vision-based-computer-control","description":"Enables multimodal AI models (vision + language) to interpret screen content and execute computer actions by analyzing visual UI elements, text, and layout. The system captures screenshots, processes them through vision models to understand interface state, and translates visual understanding into executable commands (clicks, typing, navigation) on the host operating system.","intents":["I want an AI agent to autonomously navigate web applications and desktop software by understanding what it sees on screen","I need to automate repetitive UI-based workflows without writing brittle selectors or maintaining complex automation scripts","I want to delegate computer tasks to an AI that can adapt to UI changes because it understands visual context rather than relying on fixed element IDs"],"best_for":["Teams automating cross-application workflows that span web and desktop","Enterprises with legacy software lacking APIs that need RPA-style automation","Developers building AI agents that need to interact with any GUI without custom integrations"],"limitations":["Vision model accuracy degrades with complex, cluttered, or non-standard UIs; may misinterpret overlapping elements","Latency per action cycle (screenshot → inference → execution) typically 2-5 seconds, making real-time interactions slow","No persistent memory of past interactions within a session; each screenshot is analyzed independently without learning from previous actions","Requires continuous screen access and may struggle with dynamic content, animations, or rapidly changing interfaces","Cannot handle multi-monitor setups or windowed applications that move off-screen"],"requires":["Multimodal LLM API access (GPT-4V, Claude Vision, or equivalent)","Operating system with programmatic input control (Windows, macOS, Linux with X11/Wayland)","Screen capture capability at OS level","API credentials for vision model provider"],"input_types":["screenshots (PNG, JPEG)","natural language task descriptions","structured task specifications"],"output_types":["mouse coordinates and click events","keyboard input sequences","window management commands","execution logs with visual reasoning"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_1","uri":"capability://planning.reasoning.autonomous.task.decomposition.and.execution","name":"autonomous-task-decomposition-and-execution","description":"Breaks down high-level user goals into sequences of discrete computer actions by reasoning about task dependencies and UI state. The system maintains an execution plan, monitors progress through visual feedback loops, and dynamically adjusts subsequent steps based on observed outcomes, enabling multi-step workflows without explicit step-by-step instructions.","intents":["I want to give an AI a complex goal like 'book a flight and send a confirmation email' and have it figure out the sequence of actions needed","I need an agent that can recover from errors by detecting when an action failed and trying alternative approaches","I want to automate workflows that require conditional logic based on what appears on screen"],"best_for":["Non-technical users defining automation goals in natural language","Workflows with variable paths (e.g., different flows based on search results or error states)","Scenarios where the exact UI flow is unknown or changes frequently"],"limitations":["Task decomposition quality depends on model reasoning capability; complex multi-step workflows may fail if intermediate steps are misunderstood","No explicit state machine or workflow definition; relies on model inference, which can be unpredictable","Error recovery is heuristic-based and may enter infinite loops if an action repeatedly fails","Cannot handle tasks requiring domain-specific knowledge not visible on screen (e.g., business logic, pricing rules)"],"requires":["Multimodal LLM with strong reasoning capabilities (GPT-4V or equivalent)","Task description in natural language","Access to target application or website"],"input_types":["natural language task goals","screenshots for state observation"],"output_types":["action sequences","execution status and error logs","task completion confirmation"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_2","uri":"capability://automation.workflow.cross.application.workflow.orchestration","name":"cross-application-workflow-orchestration","description":"Coordinates actions across multiple applications and websites within a single automated workflow by maintaining context across application boundaries. The system switches between windows/tabs, transfers data between applications, and synchronizes state across disparate tools without explicit API integrations or data pipelines.","intents":["I want to pull data from a web form, process it in a spreadsheet, and push results to a CRM—all in one automated workflow","I need to copy information from one SaaS tool to another without building custom integrations or using Zapier","I want to automate workflows that span desktop and web applications seamlessly"],"best_for":["SMBs and enterprises with fragmented tool stacks lacking native integrations","Workflows involving legacy software that has no API","Teams avoiding custom integration development or third-party middleware"],"limitations":["Data transfer between applications is limited to what's visible on screen or can be copy-pasted; no direct database access","Context switching between applications adds latency and increases failure points","No built-in data validation or transformation; relies on vision model to correctly interpret and transfer data","Window management can be fragile if applications minimize, close unexpectedly, or have permission restrictions"],"requires":["All target applications accessible and running on the same machine","Multimodal LLM API access","OS-level window and input control permissions"],"input_types":["natural language workflow descriptions","screenshots of multiple applications"],"output_types":["data transferred between applications","workflow execution logs","confirmation of cross-app actions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_3","uri":"capability://automation.workflow.visual.form.filling.and.data.entry","name":"visual-form-filling-and-data-entry","description":"Automatically locates form fields on screen through vision analysis, interprets their purpose and validation rules from visual cues (labels, placeholders, error messages), and populates them with appropriate data. The system handles various input types (text fields, dropdowns, checkboxes, date pickers) by understanding their visual representation rather than relying on HTML parsing.","intents":["I want to automatically fill out web forms or desktop applications with structured data without writing selectors","I need to handle form validation errors and retry with corrected data automatically","I want to populate forms that change layout or have dynamic fields based on previous selections"],"best_for":["Data entry teams processing high volumes of forms","Workflows involving forms with variable layouts or dynamic fields","Scenarios where form HTML structure is unstable or proprietary"],"limitations":["Vision model may misidentify form field types or purposes, leading to incorrect data entry","Complex validation rules (e.g., business logic constraints) are not understood; only visual validation feedback is detected","CAPTCHA and other anti-automation measures will block form submission","Multi-step forms with state dependencies may fail if intermediate steps are misunderstood","Accessibility features (ARIA labels, semantic HTML) are not leveraged; relies purely on visual appearance"],"requires":["Multimodal LLM with OCR and form understanding capabilities","Structured data source (CSV, JSON, database) or natural language data specification","Access to target form or application"],"input_types":["screenshots of forms","structured data (JSON, CSV)","natural language field descriptions"],"output_types":["filled form submissions","validation error logs","data entry confirmation"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_4","uri":"capability://automation.workflow.intelligent.error.detection.and.recovery","name":"intelligent-error-detection-and-recovery","description":"Monitors action outcomes by analyzing visual feedback (error messages, status indicators, unexpected UI states) and automatically initiates recovery strategies such as retrying with modified inputs, navigating to alternative flows, or escalating to human review. The system learns from failure patterns within a session to avoid repeating the same errors.","intents":["I want the automation to detect when an action failed and try a different approach without stopping","I need to handle transient errors like network timeouts or temporary UI glitches automatically","I want visibility into why automation failed so I can improve the workflow"],"best_for":["Long-running automation workflows where failures are expected and recovery is critical","Scenarios with unreliable external systems (slow networks, flaky APIs)","Workflows where human intervention is expensive and automation should be resilient"],"limitations":["Error detection relies on visual cues; silent failures (data not saved, background errors) are not detected","Recovery strategies are heuristic-based and may not be appropriate for all error types","No persistent learning across sessions; recovery strategies are not improved over time","Infinite loops possible if recovery strategies repeatedly fail without escalation","Cannot distinguish between expected and unexpected errors without explicit configuration"],"requires":["Multimodal LLM capable of interpreting error messages and UI state","Configurable error handling policies or escalation rules","Timeout and retry limits to prevent infinite loops"],"input_types":["screenshots showing error states","error messages and status indicators"],"output_types":["recovery action sequences","error logs with root cause analysis","escalation notifications"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_5","uri":"capability://text.generation.language.natural.language.task.specification","name":"natural-language-task-specification","description":"Accepts high-level automation goals expressed in natural language and translates them into executable computer actions without requiring users to write code or define step-by-step procedures. The system interprets ambiguous language, infers missing context from the current UI state, and handles variations in phrasing.","intents":["I want to describe what I want automated in plain English without learning a DSL or writing code","I need to automate a task but don't know the exact steps—I just know the goal","I want to modify automation by changing a sentence rather than editing code"],"best_for":["Non-technical business users and domain experts","Rapid prototyping of automation workflows","Scenarios where automation requirements are frequently updated"],"limitations":["Ambiguous language may be misinterpreted; users may need to provide clarification","Complex or domain-specific tasks may require more detailed specifications than natural language can express","No formal specification means automation behavior is not deterministic; same instruction may produce different results","Debugging failures is harder because there's no explicit execution plan to inspect","Context-dependent instructions (e.g., 'click the button') may fail if context changes"],"requires":["Multimodal LLM with strong language understanding and reasoning","Current UI state (screenshot) for context inference","Clear task description in English"],"input_types":["natural language task descriptions","screenshots for context"],"output_types":["executed actions","task completion status","clarification requests if ambiguous"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_6","uri":"capability://image.visual.screenshot.based.state.observation.and.reasoning","name":"screenshot-based-state-observation-and-reasoning","description":"Captures and analyzes screenshots to understand current application state, extract visible information (text, UI elements, layout), and reason about what actions are possible or necessary. The system uses OCR and visual understanding to build a mental model of the interface without relying on DOM access or application APIs.","intents":["I want the automation to understand what's currently on screen and make decisions based on that state","I need to extract information from a GUI that has no API or structured data export","I want to verify that an action succeeded by analyzing the resulting screen state"],"best_for":["Automating legacy applications without APIs","Web scraping and data extraction from dynamic content","Verification and testing workflows that need visual confirmation"],"limitations":["OCR accuracy degrades with small text, non-standard fonts, or poor image quality","Vision models may hallucinate or misinterpret complex layouts","No access to underlying data structures; only visible information can be extracted","Performance depends on screenshot resolution and model inference latency","Cannot detect off-screen content or information in hidden UI elements"],"requires":["Multimodal LLM with OCR and visual understanding capabilities","Screen capture capability at OS level","Sufficient resolution for text readability (typically 1024x768 minimum)"],"input_types":["screenshots (PNG, JPEG)","screen regions or crops"],"output_types":["extracted text and data","UI element locations and descriptions","state analysis and reasoning"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_7","uri":"capability://automation.workflow.interactive.human.in.the.loop.automation","name":"interactive-human-in-the-loop-automation","description":"Pauses automation execution when encountering ambiguous situations, presents options or clarification requests to a human user, and resumes based on human feedback. The system maintains context across pauses and integrates human decisions into the execution flow without requiring manual restart.","intents":["I want automation to handle routine tasks but ask for help when it's unsure","I need to review and approve critical actions before they execute","I want to provide real-time corrections if the automation goes off track"],"best_for":["High-stakes workflows where human oversight is required (financial transactions, data modifications)","Scenarios with inherent ambiguity that automation cannot resolve alone","Compliance-heavy processes requiring audit trails of human decisions"],"limitations":["Pausing automation for human input introduces latency and reduces throughput","Requires active human monitoring; not suitable for unattended automation","Context may be lost if human response is delayed","No standardized interface for human feedback; implementation is custom per workflow","Scaling to many concurrent workflows requires managing multiple human reviewers"],"requires":["User interface for presenting clarification requests and options","Mechanism for capturing human feedback (UI, API, messaging)","Timeout handling for cases where human doesn't respond"],"input_types":["automation execution state","ambiguous situations requiring human judgment","human feedback and approvals"],"output_types":["resumed automation execution","audit logs of human decisions","task completion with human-approved actions"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-self-operating-computer__cap_8","uri":"capability://automation.workflow.browser.and.desktop.application.navigation","name":"browser-and-desktop-application-navigation","description":"Autonomously navigates web browsers and desktop applications by interpreting visual UI elements (buttons, links, menus, navigation bars) and executing appropriate interactions (clicks, scrolls, keyboard shortcuts). The system understands navigation patterns and can traverse complex application hierarchies without explicit URL or menu path specifications.","intents":["I want the automation to navigate to a specific page or section by understanding the UI layout","I need to handle dynamic navigation where menu structures or link locations change","I want to automate workflows that require scrolling, pagination, or modal dialogs"],"best_for":["Web automation across diverse websites with different navigation patterns","Desktop application workflows with complex menu hierarchies","Scenarios where navigation paths are not stable or are dynamically generated"],"limitations":["Vision model may misidentify clickable elements or navigation targets","Scrolling and pagination detection relies on visual cues; infinite scroll or lazy-loaded content may cause issues","Modal dialogs and overlays can obscure navigation elements and cause confusion","Navigation shortcuts (keyboard navigation, search) are not leveraged; relies on visual clicking","Performance degrades with deeply nested navigation structures"],"requires":["Multimodal LLM with UI element detection capabilities","OS-level mouse and keyboard control","Screen capture capability"],"input_types":["screenshots of UI","natural language navigation goals (e.g., 'go to the settings page')"],"output_types":["click coordinates and navigation actions","navigation success confirmation","error logs for failed navigation"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Multimodal LLM API access (GPT-4V, Claude Vision, or equivalent)","Operating system with programmatic input control (Windows, macOS, Linux with X11/Wayland)","Screen capture capability at OS level","API credentials for vision model provider","Multimodal LLM with strong reasoning capabilities (GPT-4V or equivalent)","Task description in natural language","Access to target application or website","All target applications accessible and running on the same machine","Multimodal LLM API access","OS-level window and input control permissions"],"failure_modes":["Vision model accuracy degrades with complex, cluttered, or non-standard UIs; may misinterpret overlapping elements","Latency per action cycle (screenshot → inference → execution) typically 2-5 seconds, making real-time interactions slow","No persistent memory of past interactions within a session; each screenshot is analyzed independently without learning from previous actions","Requires continuous screen access and may struggle with dynamic content, animations, or rapidly changing interfaces","Cannot handle multi-monitor setups or windowed applications that move off-screen","Task decomposition quality depends on model reasoning capability; complex multi-step workflows may fail if intermediate steps are misunderstood","No explicit state machine or workflow definition; relies on model inference, which can be unpredictable","Error recovery is heuristic-based and may enter infinite loops if an action repeatedly fails","Cannot handle tasks requiring domain-specific knowledge not visible on screen (e.g., business logic, pricing rules)","Data transfer between applications is limited to what's visible on screen or can be copy-pasted; no direct database access","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.25,"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":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=self-operating-computer","compare_url":"https://unfragile.ai/compare?artifact=self-operating-computer"}},"signature":"Blnxs3JQfbd4Pqvbs23MAT+haJP+SownVcVeM7Rv9ytzP31kxLg5rM54oEmzg+4iabgBGU6HAp1fmZm+z+sfAw==","signedAt":"2026-06-20T01:10:56.592Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/self-operating-computer","artifact":"https://unfragile.ai/self-operating-computer","verify":"https://unfragile.ai/api/v1/verify?slug=self-operating-computer","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"}}