{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-prompt-flow-prompt-flow","slug":"prompt-flow-for-vs-code","name":"Prompt flow for VS Code","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow","page_url":"https://unfragile.ai/prompt-flow-for-vs-code","categories":["code-editors"],"tags":["__ext_prompty","keybindings","llm","prompt","prompt-flow","prompty"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-prompt-flow-prompt-flow__cap_0","uri":"capability://code.generation.editing.dag.based.visual.flow.authoring.with.yaml.backed.persistence","name":"dag-based visual flow authoring with yaml-backed persistence","description":"Enables users to create and edit prompt flow definitions using a directed acyclic graph (DAG) model persisted as flow.dag.yaml files. The extension provides both a visual editor (triggered via Ctrl+K,V or code lens) and YAML text editing with inline code lens actions, allowing developers to define multi-step LLM workflows by composing nodes (prompts, tools, Python functions) and connecting them via data dependencies. The visual editor abstracts YAML complexity while maintaining full fidelity with the underlying DAG structure.","intents":["I want to visually design a multi-step LLM workflow without writing YAML by hand","I need to define a prompt flow that chains multiple LLM calls and tool invocations together","I want to edit flow definitions in YAML while seeing real-time visual feedback"],"best_for":["AI/ML engineers building LLM-based applications in VS Code","Teams prototyping multi-step prompt chains and agentic workflows","Developers migrating from ad-hoc prompt scripts to structured flow definitions"],"limitations":["Visual editor only available in VS Code desktop; not supported in web-based VS Code (vscode.dev)","DAG structure enforces acyclic dependencies — no loops or cycles allowed, limiting certain workflow patterns","No built-in version control or diff visualization for flow.dag.yaml changes across commits"],"requires":["VS Code (version unknown, likely 1.60+)","vscode-python extension installed and active","Python 3.9+ interpreter selected in VS Code","promptflow SDK installed in active Python environment"],"input_types":["YAML (flow.dag.yaml structure)","Node definitions (prompt templates, tool references, Python function paths)"],"output_types":["flow.dag.yaml file (DAG serialization)","Visual flow diagram (rendered in editor)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_1","uri":"capability://code.generation.editing.interactive.flow.debugging.with.breakpoints.and.step.execution","name":"interactive flow debugging with breakpoints and step execution","description":"Provides a debugging interface (triggered via F5 keybinding) that executes a prompt flow step-by-step with breakpoint support, allowing developers to inspect intermediate outputs, variable states, and node execution results. The debugger integrates with VS Code's standard debug protocol, displaying execution traces and enabling pause/resume/step-through workflows. This capability surfaces runtime behavior of LLM calls and tool invocations, helping developers identify logic errors, unexpected model outputs, or data transformation issues within their flows.","intents":["I need to debug why my LLM prompt is producing unexpected outputs in a multi-step flow","I want to inspect intermediate data between nodes to verify transformations are correct","I need to step through a flow execution to understand control flow and variable values at each stage"],"best_for":["AI engineers troubleshooting complex prompt chains with multiple LLM calls","Developers optimizing prompt performance by analyzing token usage and latency per node","Teams validating data pipelines that feed into LLM prompts"],"limitations":["Debugging only works locally; cannot debug flows deployed to cloud endpoints","Breakpoint support limited to node-level granularity; cannot break within prompt templates or tool code","No time-travel debugging or execution history replay — only forward step-through","Requires active Python interpreter; debugging performance depends on local machine resources"],"requires":["VS Code (version unknown)","vscode-python extension with debugging support enabled","Python 3.9+ interpreter with promptflow SDK installed","flow.dag.yaml file in active workspace"],"input_types":["flow.dag.yaml (flow definition)","Input data (JSON or structured format, passed to flow entry points)"],"output_types":["Execution trace (node-by-node outputs)","Variable inspection data (intermediate values, LLM responses)","Debug console output (logs, errors)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_10","uri":"capability://safety.moderation.telemetry.collection.and.usage.tracking","name":"telemetry collection and usage tracking","description":"Collects usage data about extension interactions (flow creation, debugging, testing, connection management) and sends telemetry to Microsoft for product improvement and analytics. The telemetry system tracks feature adoption, error rates, and user workflows to inform development priorities. While telemetry is enabled by default, users can disable it via VS Code settings, providing opt-out capability. This capability enables Microsoft to understand how developers use prompt flows and identify areas for improvement.","intents":["Microsoft wants to understand how developers use prompt flows to prioritize features","Teams need visibility into extension usage patterns across their organization","Users want to understand what data is collected and have control over telemetry"],"best_for":["Microsoft product teams analyzing feature adoption and usage patterns","Organizations with telemetry governance requirements","Users concerned about privacy and data collection"],"limitations":["Telemetry collection mechanism not specified; unclear what data is collected beyond 'usage data'","Data retention and deletion policies not documented","No granular control over which telemetry events are collected; only binary opt-in/opt-out","Performance impact of telemetry collection not quantified","No transparency into how telemetry data is used or shared within Microsoft"],"requires":["VS Code (version unknown)","Prompt flow extension installed","Network connectivity to Microsoft telemetry endpoints"],"input_types":["Extension usage events (flow creation, debugging, testing, etc.)","Error and exception data","Performance metrics (execution time, resource usage)"],"output_types":["Telemetry data sent to Microsoft","Analytics dashboards (internal to Microsoft)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_11","uri":"capability://automation.workflow.local.only.execution.environment.with.no.remote.deployment.support","name":"local-only execution environment with no remote deployment support","description":"Restricts flow execution to local development machines, with explicit non-support for remote execution environments (SSH, containers, WSL, web-based VS Code). Flows execute within the selected local Python interpreter, limiting deployment to development and testing scenarios. This design choice prioritizes simplicity and local debugging experience over production deployment capabilities, positioning the extension as a development tool rather than a production orchestration platform.","intents":["I want to develop and test flows locally before deploying them elsewhere","I need a lightweight development environment without remote infrastructure setup","I want to debug flows on my local machine with full IDE integration"],"best_for":["Individual developers and small teams building flows locally","Organizations with separate deployment pipelines (flows developed locally, deployed via CI/CD)","Developers prioritizing fast local iteration over integrated deployment"],"limitations":["Cannot execute flows in remote Python environments (SSH, containers, WSL)","Cannot use web-based VS Code (vscode.dev); desktop VS Code only","No built-in deployment or export capabilities for production environments","Scaling limited by local machine resources; no distributed execution","Requires local Python environment setup; no containerized execution"],"requires":["VS Code desktop (not web-based)","Local Python 3.9+ interpreter","vscode-python extension configured for local interpreter"],"input_types":["flow.dag.yaml (local file)","Input data (local or passed via stdin)"],"output_types":["Execution results (local output, console logs)","Debug traces (local VS Code debug protocol)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_2","uri":"capability://automation.workflow.flow.testing.and.batch.execution.with.result.comparison","name":"flow testing and batch execution with result comparison","description":"Enables running test suites against prompt flows via Shift+F5 keybinding, executing flows against predefined test datasets and comparing outputs against expected results. The testing framework supports batch execution of flows with multiple input variations, collecting metrics (latency, token usage, success/failure rates) and surfacing test results in VS Code's test explorer. This capability allows developers to validate flow behavior across diverse inputs and detect regressions when modifying prompts or node logic.","intents":["I want to run my flow against a test dataset to verify it handles edge cases correctly","I need to compare outputs from different prompt versions to measure quality improvements","I want to track performance metrics (latency, cost) across flow executions to optimize efficiency"],"best_for":["ML engineers building evaluation frameworks for LLM applications","Teams implementing CI/CD pipelines for prompt flows with automated testing","Developers iterating on prompt quality with quantitative feedback"],"limitations":["Test result comparison requires manual definition of expected outputs; no built-in LLM-based evaluation (e.g., semantic similarity scoring)","Batch execution performance limited by local machine resources; no distributed testing across multiple workers","Test data format not specified; unclear if supports CSV, JSON, or other formats","No integration with external evaluation platforms or metric aggregation services"],"requires":["VS Code (version unknown)","vscode-python extension","Python 3.9+ with promptflow SDK","Test data files in workspace (format unknown)"],"input_types":["flow.dag.yaml (flow definition)","Test dataset (format unknown, likely JSON or CSV)","Expected output definitions"],"output_types":["Test results (pass/fail per test case)","Execution metrics (latency, token count, cost)","Comparison reports (output vs expected)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_3","uri":"capability://tool.use.integration.external.service.connection.management.with.credential.abstraction","name":"external service connection management with credential abstraction","description":"Provides a connection management system (accessible via sidebar 'Connections' section) that abstracts credentials and API endpoints for external services (e.g., Azure OpenAI, custom APIs). Developers define connections via YAML templates (create/update_{ConnectionType}_connection.yaml) with code lens guidance, and flows reference connections by name rather than embedding credentials. The extension handles credential storage and injection at runtime, supporting multiple connection types through a generic connection framework. This decouples flow definitions from environment-specific secrets and enables reuse across development, staging, and production environments.","intents":["I want to configure my flow to use Azure OpenAI without hardcoding API keys in flow.dag.yaml","I need to manage different API endpoints for development vs production without modifying flow definitions","I want to share flow definitions with teammates without exposing sensitive credentials"],"best_for":["Teams managing multiple environments (dev/staging/prod) with different API endpoints","Organizations requiring credential isolation and secure secret management","Developers building reusable flow templates for distribution"],"limitations":["Supported connection types not enumerated in documentation; unclear which services are natively supported vs require custom implementation","Credential storage mechanism not specified; unclear if credentials are encrypted or stored in plaintext in YAML files","No built-in secret rotation or expiration management","Connection validation only occurs at flow execution time; no pre-flight validation in editor","No audit logging for connection access or credential usage"],"requires":["VS Code (version unknown)","vscode-python extension","Python 3.9+ environment","API credentials for external services (format and required fields unknown)"],"input_types":["Connection YAML templates (create/update_{ConnectionType}_connection.yaml)","Credential values (API keys, endpoints, authentication tokens)"],"output_types":["Connection configuration files (persisted YAML)","Connection references in flow.dag.yaml (by-name references)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_4","uri":"capability://planning.reasoning.node.composition.and.dependency.management.for.multi.step.workflows","name":"node composition and dependency management for multi-step workflows","description":"Allows developers to add nodes to flows (via Ctrl+Cmd+N keybinding or visual editor) and define data dependencies between them, creating a directed acyclic graph of operations. Nodes represent discrete units of work: LLM prompts, tool invocations, Python functions, or data transformations. The extension manages node inputs/outputs, type checking, and data flow routing, ensuring outputs from upstream nodes correctly feed into downstream node inputs. This capability abstracts the complexity of orchestrating multiple LLM calls and tool invocations into a declarative dependency graph.","intents":["I want to chain multiple LLM calls together where each call uses the output of the previous one","I need to invoke external tools (APIs, functions) as part of my LLM workflow","I want to define conditional logic or parallel execution paths in my prompt flow"],"best_for":["AI engineers building agentic workflows with tool use and multi-turn reasoning","Teams implementing retrieval-augmented generation (RAG) pipelines with LLM orchestration","Developers composing complex prompt chains with data transformations between steps"],"limitations":["DAG structure enforces acyclic dependencies; no loops or cycles, limiting iterative workflows","No built-in conditional branching or dynamic routing based on runtime values","Node type system not fully specified; unclear which node types are supported (LLM, tool, Python function, data transform, etc.)","No support for parallel execution or fan-out/fan-in patterns","Input/output type validation mechanism not documented; unclear if type mismatches are caught at definition or runtime"],"requires":["VS Code (version unknown)","vscode-python extension","Python 3.9+ with promptflow SDK","flow.dag.yaml file in workspace"],"input_types":["Node definitions (type, configuration, input schema)","Connection references (for external service nodes)","Data dependency specifications (output of node A → input of node B)"],"output_types":["flow.dag.yaml with node definitions and edges","Visual flow diagram showing node connections"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_5","uri":"capability://automation.workflow.python.environment.integration.and.dependency.management","name":"python environment integration and dependency management","description":"Integrates with VS Code's Python extension to detect and manage the active Python interpreter, enabling flows to execute within a specific Python environment with isolated dependencies. The extension provides an 'Install dependencies' action in the sidebar that installs promptflow and promptflow-tools packages into the selected environment. This capability ensures flows run with correct package versions and allows developers to manage environment-specific dependencies (e.g., custom tool packages) without affecting system Python or other projects.","intents":["I want to ensure my flow runs with the correct version of promptflow and dependencies","I need to use custom Python packages or tools in my flow nodes","I want to isolate flow dependencies from other Python projects in my workspace"],"best_for":["Python developers managing multiple projects with different dependency versions","Teams using virtual environments or conda for environment isolation","Developers building custom tools or extensions for prompt flows"],"limitations":["Requires vscode-python extension; behavior if extension is disabled or not installed is undefined","No built-in environment creation; developers must manually create virtual environments or conda environments","Dependency installation mechanism not specified; unclear if it uses pip, conda, or other package managers","No dependency lock file or reproducibility guarantees; installed versions may vary across machines","No support for remote Python interpreters (SSH, containers, WSL) — only local interpreters"],"requires":["VS Code (version unknown)","vscode-python extension installed and active","Python 3.9+ interpreter (local, not remote)","pip or conda package manager in Python environment"],"input_types":["Python interpreter path (selected via vscode-python)","Dependency specifications (promptflow, promptflow-tools, custom packages)"],"output_types":["Installed packages in Python environment","Environment metadata (Python version, installed package list)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_6","uri":"capability://automation.workflow.sidebar.quick.access.panel.for.flow.operations","name":"sidebar quick access panel for flow operations","description":"Provides a dedicated sidebar panel (labeled 'Prompt flow') with quick access to common flow operations: creating new flows, installing dependencies, managing connections, and accessing flow documentation. The sidebar organizes operations into subsections (Quick access, Connections, Dependencies) with one-click actions, reducing the need to navigate command palette or context menus. This capability centralizes flow-related operations in a discoverable UI location, improving accessibility for developers new to prompt flows.","intents":["I want a centralized place to access all prompt flow operations without memorizing keybindings","I need to quickly install dependencies or create a new flow without opening the command palette","I want to see all my configured connections in one place and manage them easily"],"best_for":["Developers new to prompt flows who benefit from discoverable UI","Teams standardizing on prompt flow with onboarding requirements","Users preferring GUI-based workflows over command palette"],"limitations":["Sidebar only available in VS Code desktop; not accessible in web-based VS Code","No customization of sidebar layout or operation ordering","Sidebar state not persisted across VS Code sessions (unclear if this is a limitation or expected behavior)","No drag-and-drop or advanced interaction patterns; limited to click-based actions"],"requires":["VS Code (version unknown, likely 1.60+)","Prompt flow extension installed and active"],"input_types":["User clicks on sidebar actions"],"output_types":["Flow creation dialogs","Dependency installation execution","Connection management UI"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_7","uri":"capability://automation.workflow.context.menu.integration.for.directory.level.flow.creation","name":"context menu integration for directory-level flow creation","description":"Enables right-click context menu actions on directories and files to create new flows directly in the selected location. The 'New flow in this directory' action creates a flow.dag.yaml template in the chosen directory, initializing the flow structure with default nodes and connections. This capability reduces friction for creating new flows by eliminating the need to manually create directories or YAML files, and provides a familiar file-system-based workflow for developers.","intents":["I want to create a new flow in a specific directory without using the command palette","I need to quickly scaffold a flow template with default structure","I want to organize flows by directory structure matching my project layout"],"best_for":["Developers preferring file-system-based workflows","Teams organizing flows by feature or domain in directory hierarchies","Users new to prompt flows who benefit from scaffolding"],"limitations":["Context menu only available in VS Code desktop file explorer; not available in web-based VS Code","Default flow template structure not customizable; no way to use custom templates","No validation of directory permissions or write access before creating flow","Flow naming convention not specified; unclear if directory name is used as flow name"],"requires":["VS Code (version unknown)","Prompt flow extension installed","Write access to target directory"],"input_types":["Directory path (selected via file explorer context menu)"],"output_types":["flow.dag.yaml file with default template","Associated connection and configuration files (if applicable)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_8","uri":"capability://code.generation.editing.code.lens.actions.for.flow.dag.yaml.editing","name":"code lens actions for flow.dag.yaml editing","description":"Displays inline code lens actions above flow.dag.yaml files and connection configuration files, providing quick access to operations like 'Open visual editor', 'Run debug', 'Run test', and 'Create/update connection'. Code lens actions reduce context switching by allowing developers to trigger operations directly from the YAML editor without opening sidebars or command palettes. This capability integrates flow operations into the natural code editing workflow, improving efficiency for developers who prefer text-based editing.","intents":["I want to open the visual editor for my flow without leaving the YAML editor","I need to quickly run debug or test from the YAML file without using keybindings","I want to validate and update connection configurations inline while editing YAML"],"best_for":["Developers comfortable with YAML editing who want quick access to operations","Teams using version control for flow.dag.yaml files and preferring text-based diffs","Power users optimizing for keyboard-based workflows"],"limitations":["Code lens actions only appear for flow.dag.yaml and connection files; not available for other file types","Code lens rendering may add latency to YAML editor responsiveness (performance impact unknown)","No customization of which code lens actions are displayed","Code lens actions context limited to current file; no cross-file operations"],"requires":["VS Code (version unknown, likely 1.60+)","YAML language support (built-in or via extension)","flow.dag.yaml or connection YAML file open in editor"],"input_types":["YAML file content (flow.dag.yaml or connection file)"],"output_types":["Triggered operations (visual editor, debug, test, connection update)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-prompt-flow-prompt-flow__cap_9","uri":"capability://automation.workflow.keybinding.based.flow.execution.and.debugging","name":"keybinding-based flow execution and debugging","description":"Provides keyboard shortcuts for common flow operations: F5 for debug execution, Shift+F5 for test execution, Ctrl+Cmd+N for adding nodes, and Ctrl+Cmd+K,V for opening visual editor. These keybindings enable rapid iteration on flows without mouse interaction or menu navigation, supporting keyboard-driven workflows. The keybindings follow VS Code conventions (F5 for debug, Shift+F5 for test) making them discoverable for developers familiar with VS Code's debugging paradigm.","intents":["I want to quickly debug my flow without clicking through menus","I need fast iteration on flow development using keyboard shortcuts","I want to follow VS Code conventions for debugging and testing"],"best_for":["Power users and experienced developers optimizing for keyboard efficiency","Teams with strong VS Code muscle memory wanting consistent keybindings","Developers iterating rapidly on flow logic and needing fast feedback loops"],"limitations":["Keybindings may conflict with other VS Code extensions or user custom keybindings","No customization of keybindings documented; unclear if users can rebind keys","Keybindings only work when flow.dag.yaml is active editor; context-dependent behavior may be confusing","No keybinding for creating new flows; requires command palette or context menu"],"requires":["VS Code (version unknown)","Prompt flow extension installed","flow.dag.yaml file open in active editor (for debug/test keybindings)"],"input_types":["Keyboard input (F5, Shift+F5, Ctrl+Cmd+N, Ctrl+Cmd+K,V)"],"output_types":["Flow execution (debug or test mode)","Visual editor window","Node addition dialog"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["VS Code (version unknown, likely 1.60+)","vscode-python extension installed and active","Python 3.9+ interpreter selected in VS Code","promptflow SDK installed in active Python environment","VS Code (version unknown)","vscode-python extension with debugging support enabled","Python 3.9+ interpreter with promptflow SDK installed","flow.dag.yaml file in active workspace","Prompt flow extension installed","Network connectivity to Microsoft telemetry endpoints"],"failure_modes":["Visual editor only available in VS Code desktop; not supported in web-based VS Code (vscode.dev)","DAG structure enforces acyclic dependencies — no loops or cycles allowed, limiting certain workflow patterns","No built-in version control or diff visualization for flow.dag.yaml changes across commits","Debugging only works locally; cannot debug flows deployed to cloud endpoints","Breakpoint support limited to node-level granularity; cannot break within prompt templates or tool code","No time-travel debugging or execution history replay — only forward step-through","Requires active Python interpreter; debugging performance depends on local machine resources","Telemetry collection mechanism not specified; unclear what data is collected beyond 'usage data'","Data retention and deletion policies not documented","No granular control over which telemetry events are collected; only binary opt-in/opt-out","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.59,"quality":0.34,"ecosystem":0.33,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","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=prompt-flow-for-vs-code","compare_url":"https://unfragile.ai/compare?artifact=prompt-flow-for-vs-code"}},"signature":"1YnHdZNJcIcY8rogJ8ODSJALm5z7ExMOHLz9g3OdPpijEXNHi+D6wvhxUv7JihaP+kXHoId2aokn+xTETvL9Aw==","signedAt":"2026-06-21T17:20:13.005Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prompt-flow-for-vs-code","artifact":"https://unfragile.ai/prompt-flow-for-vs-code","verify":"https://unfragile.ai/api/v1/verify?slug=prompt-flow-for-vs-code","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"}}