{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-skyvern","slug":"skyvern","name":"Skyvern","type":"mcp","url":"https://github.com/Skyvern-AI/skyvern/tree/main/integrations/mcp","page_url":"https://unfragile.ai/skyvern","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-skyvern__cap_0","uri":"capability://automation.workflow.vision.based.browser.element.identification.and.interaction","name":"vision-based browser element identification and interaction","description":"Skyvern uses Vision LLMs to analyze rendered web pages and identify interactive elements without relying on brittle XPath selectors or DOM parsing. The system captures screenshots, sends them to vision models (Claude, GPT-4V, etc.), and receives structured element coordinates and interaction instructions. This approach enables the agent to work on previously unseen websites and adapt to layout changes automatically, replacing traditional selector-based automation with semantic understanding of page content.","intents":["Automate workflows on websites that frequently change their layout or structure","Build browser automation that works on new websites without pre-configuration","Reduce maintenance burden of XPath-based automation by using visual understanding instead"],"best_for":["Teams automating cross-domain web workflows (e.g., multi-SaaS data entry)","Enterprises with frequently-updated internal web applications","Developers building resilient RPA solutions without brittle selectors"],"limitations":["Vision LLM inference latency adds 1-3 seconds per page analysis vs instant DOM queries","Requires screenshot capability — cannot work with headless-only environments without display server","Vision model accuracy degrades on complex, densely-packed UIs or non-English content","Cost scales with number of page interactions (each screenshot = API call to vision model)"],"requires":["Playwright or compatible browser automation library","Vision LLM API access (Claude, GPT-4V, or compatible provider)","Display server or virtual display (Xvfb) for screenshot generation"],"input_types":["rendered HTML page (via browser automation)","user instruction/task description (natural language)"],"output_types":["element coordinates (x, y, width, height)","interaction action (click, type, scroll, etc.)","structured element metadata"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_1","uri":"capability://planning.reasoning.forgeagent.based.agentic.step.execution.with.llm.decision.making","name":"forgeagent-based agentic step execution with llm decision-making","description":"Skyvern's ForgeAgent implements a loop-based execution model where an LLM makes real-time decisions about which actions to take next based on page state and task progress. Each iteration captures the current page state, sends it to the LLM with the task context, receives an action decision, executes that action via Playwright, and loops until task completion or failure. The system maintains execution history and context across steps, allowing the LLM to reason about multi-step workflows without pre-defined scripts.","intents":["Execute complex multi-step workflows where the exact sequence depends on page state or user data","Build agents that can handle conditional logic and error recovery without explicit programming","Enable LLMs to autonomously navigate and interact with web applications in real-time"],"best_for":["Developers building AI agents that need to autonomously navigate web UIs","Teams automating workflows with conditional branches or dynamic page flows","Researchers prototyping LLM-based browser automation systems"],"limitations":["Each step requires LLM inference (1-3 second latency per decision), making workflows slower than pre-compiled scripts","LLM decision quality depends on prompt engineering and context window size — complex workflows may exceed token limits","No built-in error recovery beyond LLM retry logic — requires careful prompt design to handle edge cases","Costs accumulate with workflow length (N steps = N LLM API calls)"],"requires":["LLM API access (OpenAI, Anthropic, or compatible provider with function calling support)","Playwright browser instance","Task description and success criteria in natural language"],"input_types":["task description (natural language)","page screenshot (visual state)","execution history (previous actions and results)"],"output_types":["action decision (structured action object)","action parameters (element coordinates, text input, etc.)","task completion status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_10","uri":"capability://planning.reasoning.dynamic.workflow.generation.from.natural.language.task.descriptions.taskv2","name":"dynamic workflow generation from natural language task descriptions (taskv2)","description":"Skyvern's TaskV2 system enables dynamic workflow generation where a natural language task description is converted into an executable workflow at runtime. Instead of pre-defining workflows, users describe what they want automated, and the system generates a workflow (block DAG) that accomplishes the task. This combines the flexibility of agentic execution with the reusability of workflows — the generated workflow can be cached and reused for similar tasks. The generation process uses LLM reasoning to decompose tasks into blocks and determine execution order.","intents":["Generate automation workflows from natural language descriptions without manual workflow design","Enable non-technical users to describe automation tasks in plain English","Dynamically adapt workflows to task variations without pre-defining all possible flows"],"best_for":["Non-technical users who want to describe automation tasks without workflow design","Rapid prototyping scenarios where workflow design overhead is prohibitive","Systems that need to handle diverse, ad-hoc automation requests"],"limitations":["Generated workflows may be suboptimal — LLM reasoning doesn't guarantee efficient execution","Workflow generation adds latency (LLM inference + block instantiation) before execution starts","Generated workflows are harder to debug than manually-designed ones","Task descriptions must be sufficiently detailed — ambiguous descriptions lead to incorrect workflows"],"requires":["LLM API access (for workflow generation)","Task description (natural language)","Available block definitions (for workflow generation to reference)"],"input_types":["task description (natural language)","task parameters (input data)","block library (available blocks for generation)"],"output_types":["generated workflow (block DAG)","workflow execution result","generated workflow metadata (generation confidence, alternatives)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_11","uri":"capability://automation.workflow.context.aware.parameter.passing.and.state.management.across.workflow.blocks","name":"context-aware parameter passing and state management across workflow blocks","description":"Skyvern's ContextManager maintains execution context across workflow blocks, enabling parameter passing, state tracking, and conditional logic based on previous block outputs. Each block receives input parameters from the context, executes, and updates the context with output values. The system supports variable interpolation (e.g., ${previous_block.output}), conditional block execution based on context values, and context snapshots for debugging. This enables complex workflows where later blocks depend on earlier block results without explicit data flow configuration.","intents":["Pass data between workflow blocks without manual configuration","Implement conditional logic based on previous block outputs","Debug workflow execution by inspecting context state at each step"],"best_for":["Complex workflows with data dependencies between blocks","Workflows requiring conditional branching based on extracted data","Teams debugging workflow execution issues"],"limitations":["Context state can become large and difficult to manage in long workflows","Variable interpolation syntax can be error-prone (e.g., typos in variable names)","Context snapshots consume memory and storage for long-running workflows","Implicit data dependencies (via context) are harder to visualize than explicit data flow"],"requires":["ContextManager instance","Block definitions with input/output parameter schemas","Workflow definition with parameter bindings"],"input_types":["block input parameters (from context or user input)","context state (previous block outputs)"],"output_types":["block output (stored in context)","updated context state","context snapshots (for debugging)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_2","uri":"capability://automation.workflow.workflow.system.with.block.based.dag.execution.and.parameter.management","name":"workflow system with block-based dag execution and parameter management","description":"Skyvern provides a workflow engine that represents automation tasks as directed acyclic graphs (DAGs) of reusable blocks (e.g., browser actions, data extraction, conditionals). Each block has input/output parameters, and the WorkflowExecutionService orchestrates execution order, manages context across blocks, and handles parameter passing. Blocks can be conditional, looped, or chained, enabling complex workflows without code. The system persists workflow definitions and execution state to a database, supporting resumable and auditable automation.","intents":["Define complex multi-step automation workflows using a visual or declarative interface","Reuse automation blocks across multiple workflows without code duplication","Build workflows with conditional branches, loops, and error handling without programming"],"best_for":["Non-technical users or business analysts defining automation workflows","Teams building reusable automation libraries with standardized blocks","Enterprises requiring audit trails and resumable workflow execution"],"limitations":["Block abstraction adds complexity — debugging requires understanding block context and parameter flow","Parameter passing between blocks can become error-prone with deeply nested workflows","No built-in version control for workflows — requires external Git integration for change tracking","Conditional logic is limited to block-level decisions; complex branching requires multiple block definitions"],"requires":["Database (PostgreSQL or compatible) for workflow and execution state persistence","WorkflowExecutionService instance running","Block definitions (browser actions, data extraction, etc.)"],"input_types":["workflow definition (JSON/YAML with block DAG)","input parameters (task-specific data)","block outputs (from previous block execution)"],"output_types":["workflow execution result (success/failure)","block outputs (structured data from each block)","execution logs and state snapshots"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_3","uri":"capability://code.generation.editing.script.generation.and.caching.for.performance.optimization","name":"script generation and caching for performance optimization","description":"Skyvern's script generation system analyzes completed agentic workflows and generates optimized Playwright code that replays the same sequence of actions. This generated script is cached and executed on subsequent runs of the same workflow, bypassing LLM inference entirely. The system uses a code generation pipeline that converts action sequences into idempotent, self-healing scripts with built-in retry logic and element re-detection. This two-phase approach (agent-first, then script-cached) provides both flexibility for new workflows and performance for repeated tasks.","intents":["Optimize repeated automation workflows by caching generated code instead of re-running LLM inference","Reduce API costs for workflows that run multiple times with similar page structures","Enable fast execution of well-tested automation sequences without LLM latency"],"best_for":["Teams running recurring automation tasks (daily reports, data syncs, etc.)","Cost-sensitive deployments where LLM inference per-step is prohibitive","Workflows with stable page structures that don't require adaptive LLM reasoning"],"limitations":["Generated scripts are brittle to page layout changes — require re-generation if UI changes significantly","Script generation adds overhead on first run (analysis + code generation time)","Self-healing logic in scripts is limited compared to LLM's adaptive reasoning","Cache invalidation strategy must be manually managed — no automatic detection of page structure changes"],"requires":["Completed agentic workflow execution (to analyze action sequence)","Playwright runtime","Cache storage (in-memory or persistent database)"],"input_types":["action sequence (from agentic execution)","page state snapshots (for element re-detection)"],"output_types":["generated Playwright code (Python or JavaScript)","cached script executable","execution result (success/failure with fallback to agent if script fails)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_4","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration.for.claude.ai.control","name":"mcp (model context protocol) server integration for claude/ai control","description":"Skyvern exposes browser automation capabilities as an MCP server, allowing Claude and other AI systems to invoke browser actions through standardized MCP tools. The integration maps Skyvern's action system (click, type, scroll, extract) to MCP tool definitions with JSON schemas, enabling Claude to call browser actions as if they were native functions. This allows Claude to autonomously control browsers without embedding Skyvern's full agent logic, treating Skyvern as a tool provider rather than a complete automation system.","intents":["Let Claude or other AI systems control browsers through MCP tool calls","Integrate Skyvern's browser automation into Claude-based agents or applications","Enable AI systems to use browser control as a capability without running Skyvern's full agent loop"],"best_for":["Developers building Claude-based agents that need browser control","Teams integrating Skyvern into larger AI systems via MCP","Applications where Claude is the primary AI decision-maker and Skyvern is a tool"],"limitations":["MCP tool calls are synchronous — cannot leverage Skyvern's agentic loop for multi-step reasoning","Claude must explicitly call each browser action — no implicit workflow optimization","Requires MCP-compatible client (Claude, or custom MCP client implementation)","Tool schema complexity may exceed Claude's context window for large action sets"],"requires":["Skyvern MCP server running (skyvern/cli/mcp.py)","Claude API access or MCP-compatible client","Browser instance managed by Skyvern"],"input_types":["MCP tool call (JSON with action type and parameters)","page state (screenshot or DOM state, if requested by Claude)"],"output_types":["action result (success/failure)","page state update (screenshot, extracted data)","structured response (JSON)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_5","uri":"capability://automation.workflow.persistent.browser.session.and.profile.management","name":"persistent browser session and profile management","description":"Skyvern maintains persistent browser sessions and profiles across workflow executions, enabling stateful automation where login state, cookies, and local storage persist. The system manages browser lifecycle (creation, reuse, cleanup) and supports multiple concurrent sessions with isolated profiles. This allows workflows to maintain authentication state, avoid repeated login steps, and preserve user-specific data across multiple automation runs without re-authentication.","intents":["Maintain login state across multiple workflow executions without re-authenticating","Preserve browser state (cookies, local storage) for stateful automation","Run multiple concurrent workflows with isolated browser profiles"],"best_for":["Teams automating workflows that require persistent authentication","Applications with high-frequency automation where re-login overhead is significant","Multi-user automation scenarios requiring isolated browser profiles"],"limitations":["Persistent profiles consume disk space — requires cleanup strategy for long-running systems","Session reuse can cause state pollution if workflows don't properly isolate actions","Browser profile management adds complexity to deployment and scaling","Credential storage in profiles is a security risk — requires careful handling of sensitive data"],"requires":["Persistent storage for browser profiles (local filesystem or network storage)","Browser management system (Playwright with profile support)","Session lifecycle management (creation, reuse, cleanup)"],"input_types":["profile identifier (user ID, session ID)","browser configuration (headless, proxy, etc.)"],"output_types":["browser instance with persistent state","session metadata (creation time, last used, etc.)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_6","uri":"capability://tool.use.integration.multi.provider.llm.routing.with.fallback.logic","name":"multi-provider llm routing with fallback logic","description":"Skyvern's LLM integration layer (APIHandlerFactory, ConfigRegistry) abstracts multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) behind a unified interface. The system supports provider-specific configuration, automatic fallback to alternative providers on failure, and cost/latency optimization through router logic. Each provider has a dedicated API handler that manages authentication, request formatting, and response parsing, enabling seamless switching between models without changing agent code.","intents":["Switch between different LLM providers without modifying automation code","Implement fallback logic to use alternative models if primary provider fails","Optimize costs by routing requests to cheaper models for simple tasks"],"best_for":["Teams using multiple LLM providers and wanting unified abstraction","Cost-sensitive deployments where provider switching optimizes expenses","Resilient systems requiring automatic fallback to alternative models"],"limitations":["Provider abstraction adds latency for request routing and fallback logic (~50-100ms overhead)","Model-specific features (function calling, vision capabilities) require provider-specific code paths","Fallback logic can mask underlying issues — difficult to debug provider-specific failures","Configuration complexity increases with number of providers and fallback rules"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Provider configuration in environment or config file","Network access to provider APIs"],"input_types":["LLM request (prompt, messages, function definitions)","provider configuration (model, temperature, max_tokens)"],"output_types":["LLM response (text, function call, structured data)","provider metadata (model used, tokens consumed, latency)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_7","uri":"capability://data.processing.analysis.artifact.collection.and.structured.data.extraction","name":"artifact collection and structured data extraction","description":"Skyvern can extract and collect artifacts (screenshots, PDFs, structured data) from web pages during workflow execution. The system supports multiple extraction methods: vision-based extraction (asking LLM to extract data from screenshots), DOM-based extraction (parsing HTML), and file downloads. Extracted artifacts are stored with workflow execution metadata, enabling data collection alongside automation. The extraction is parameterized — workflows can specify what data to extract and in what format (JSON, CSV, etc.).","intents":["Extract structured data from web pages as part of automation workflows","Collect screenshots or PDFs as evidence of automation execution","Convert unstructured web content into structured formats (JSON, CSV)"],"best_for":["Workflows that combine automation with data extraction (e.g., web scraping + form filling)","Compliance scenarios requiring artifact collection and audit trails","Data pipeline workflows that need to extract and transform web content"],"limitations":["Vision-based extraction requires LLM inference — adds latency and cost per extraction","DOM-based extraction is fragile to page structure changes","Artifact storage can consume significant disk space for long-running workflows","Extraction accuracy depends on page complexity and LLM capability"],"requires":["Vision LLM for extraction (if using vision-based method)","Storage system for artifacts (filesystem or cloud storage)","Extraction schema or prompt (specifying what to extract)"],"input_types":["page screenshot (for vision-based extraction)","HTML DOM (for DOM-based extraction)","extraction schema (JSON schema or natural language prompt)"],"output_types":["extracted data (JSON, CSV, or custom format)","artifact metadata (extraction method, timestamp, confidence)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_8","uri":"capability://tool.use.integration.bitwarden.credential.management.and.multi.field.totp.support","name":"bitwarden credential management and multi-field totp support","description":"Skyvern integrates with Bitwarden for secure credential storage and retrieval during workflow execution. The system can fetch usernames, passwords, and TOTP secrets from Bitwarden vaults and inject them into web forms automatically. Multi-field TOTP support enables workflows to handle authentication flows that require TOTP codes in addition to passwords. Credentials are retrieved at runtime and never stored in workflow definitions, improving security and enabling credential rotation without workflow changes.","intents":["Securely manage credentials for automated workflows without storing them in code","Handle multi-factor authentication (TOTP) in automated login flows","Enable credential rotation without modifying workflow definitions"],"best_for":["Enterprise teams requiring secure credential management for automation","Workflows that need to handle MFA-protected applications","Compliance-sensitive environments (finance, healthcare) requiring credential security"],"limitations":["Requires Bitwarden instance (self-hosted or cloud) — adds external dependency","Bitwarden API latency adds 200-500ms per credential fetch","TOTP generation requires time synchronization between Bitwarden and target system","Credential field mapping must be manually configured — no automatic detection"],"requires":["Bitwarden instance (self-hosted or Bitwarden.com account)","Bitwarden API access (master password or API token)","Credential entries in Bitwarden vault with proper field mapping"],"input_types":["credential identifier (vault item ID or name)","field mapping (which Bitwarden fields map to form fields)"],"output_types":["credential data (username, password, TOTP code)","TOTP code (time-based one-time password)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-skyvern__cap_9","uri":"capability://tool.use.integration.rest.api.and.cli.interface.for.task.and.workflow.management","name":"rest api and cli interface for task and workflow management","description":"Skyvern exposes a REST API (Agent Protocol compatible) and CLI for creating, executing, and monitoring tasks and workflows. The API supports task creation with parameters, workflow execution with input data, and real-time status monitoring. The CLI provides commands for initializing projects, managing LLM configuration, running tasks, and managing workflows. Both interfaces abstract the underlying agent and workflow execution, enabling integration with external systems and user-friendly command-line interaction.","intents":["Integrate Skyvern automation into external applications via REST API","Manage automation tasks and workflows from the command line","Monitor workflow execution status and retrieve results programmatically"],"best_for":["Developers building applications that need to trigger Skyvern automation","DevOps teams managing automation infrastructure via CLI","Systems integrating Skyvern with other tools (CI/CD, webhooks, etc.)"],"limitations":["REST API latency depends on network and server load — not suitable for sub-second response requirements","CLI is Python-specific — requires Python runtime and dependencies","API authentication requires API key management — adds operational complexity","Polling for task status is inefficient — no built-in WebSocket or event streaming"],"requires":["Skyvern server running (REST API endpoint)","Python 3.9+ (for CLI)","API key or authentication credentials"],"input_types":["task definition (JSON with task type, parameters)","workflow definition (JSON with block DAG)","CLI command (task creation, execution, monitoring)"],"output_types":["task/workflow execution result (JSON)","execution status (running, completed, failed)","task output (extracted data, artifacts)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Playwright or compatible browser automation library","Vision LLM API access (Claude, GPT-4V, or compatible provider)","Display server or virtual display (Xvfb) for screenshot generation","LLM API access (OpenAI, Anthropic, or compatible provider with function calling support)","Playwright browser instance","Task description and success criteria in natural language","LLM API access (for workflow generation)","Task description (natural language)","Available block definitions (for workflow generation to reference)","ContextManager instance"],"failure_modes":["Vision LLM inference latency adds 1-3 seconds per page analysis vs instant DOM queries","Requires screenshot capability — cannot work with headless-only environments without display server","Vision model accuracy degrades on complex, densely-packed UIs or non-English content","Cost scales with number of page interactions (each screenshot = API call to vision model)","Each step requires LLM inference (1-3 second latency per decision), making workflows slower than pre-compiled scripts","LLM decision quality depends on prompt engineering and context window size — complex workflows may exceed token limits","No built-in error recovery beyond LLM retry logic — requires careful prompt design to handle edge cases","Costs accumulate with workflow length (N steps = N LLM API calls)","Generated workflows may be suboptimal — LLM reasoning doesn't guarantee efficient execution","Workflow generation adds latency (LLM inference + block instantiation) before execution starts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"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":"2026-05-03T14:00:15.503Z","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=skyvern","compare_url":"https://unfragile.ai/compare?artifact=skyvern"}},"signature":"VNfYrYaPIpnUPTGH93kaHn01lSoHON5kZflzTimdq2snUyN+qZpzEJs4qmJpRbqVGTxpPKkQM/02sNK4TRPUBw==","signedAt":"2026-06-21T01:10:57.656Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/skyvern","artifact":"https://unfragile.ai/skyvern","verify":"https://unfragile.ai/api/v1/verify?slug=skyvern","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"}}