{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ms-toolsai-prompty","slug":"prompty","name":"Prompty","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-toolsai.prompty","page_url":"https://unfragile.ai/prompty","categories":["code-editors"],"tags":["__ext_prompty","__ext_tracy","Azure OpenAI","keybindings","LLM","LLMOps","plaintext","Prompt","Prompt Engineering","prompty","tracy"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ms-toolsai-prompty__cap_0","uri":"capability://text.generation.language.prompt.playground.execution.with.llm.provider.integration","name":"prompt playground execution with llm provider integration","description":"Provides an interactive testing environment within VS Code where developers can write, execute, and iterate on prompts against configured LLM providers (Azure OpenAI, OpenAI, local models). The playground accepts prompt text input, routes execution requests to the selected provider via API calls, and returns model responses directly in the editor interface, enabling rapid prompt validation without context switching.","intents":["Test a prompt variation against an LLM without leaving VS Code","Compare outputs from different prompt versions in real-time","Validate prompt behavior before committing to production","Debug prompt engineering issues by iterating quickly"],"best_for":["Solo developers building LLM-powered applications","Prompt engineers optimizing model outputs","Teams prototyping AI features in early development"],"limitations":["Execution latency depends on external LLM provider response times (typically 1-10 seconds per request)","No built-in prompt versioning or history tracking — requires manual file management","Limited to single-prompt testing; no batch execution or comparative analysis UI","Requires valid API credentials for configured provider; no fallback to local inference"],"requires":["VS Code 1.80 or later","API key for Azure OpenAI, OpenAI, or compatible LLM provider","Network connectivity to LLM provider endpoint","Prompty file format (.prompty) for prompt definition"],"input_types":["plaintext prompt","structured prompt with variables","system message + user message pairs"],"output_types":["LLM text response","token usage metadata","execution timing"],"categories":["text-generation-language","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_1","uri":"capability://code.generation.editing.prompty.file.format.parsing.and.validation","name":"prompty file format parsing and validation","description":"Parses and validates Prompty-formatted files (.prompty) which define prompts in a standardized YAML/JSON-like structure containing metadata, system messages, user message templates, and model configuration. The extension provides syntax highlighting, schema validation, and error reporting for malformed Prompty files, ensuring prompt definitions conform to the specification before execution.","intents":["Write prompts in a portable, version-control-friendly format","Validate prompt syntax before execution to catch configuration errors early","Share prompt definitions across teams without embedding in application code","Ensure prompt definitions are compatible with Prompty tooling ecosystem"],"best_for":["Teams standardizing on Prompty format for prompt management","Developers building prompt libraries for reuse","Organizations requiring prompt portability across tools"],"limitations":["Prompty format specification is not fully documented in VS Code extension; requires external documentation at prompty.ai","No visual schema builder — developers must write Prompty syntax manually","Validation is client-side only; no server-side schema enforcement","No migration tools for converting existing prompts from other formats to Prompty"],"requires":["VS Code 1.80 or later","Understanding of Prompty file format specification",".prompty file extension"],"input_types":["plaintext .prompty file","YAML/JSON-structured prompt definition"],"output_types":["validation diagnostics","syntax highlighting","parsed prompt metadata"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_10","uri":"capability://safety.moderation.error.handling.and.execution.failure.diagnostics","name":"error handling and execution failure diagnostics","description":"Captures and displays errors from prompt execution failures (API errors, authentication failures, malformed requests, provider-specific errors) with diagnostic information to help developers understand and resolve issues. Error messages are displayed in the VS Code interface with context about what failed and potential remediation steps.","intents":["Understand why a prompt execution failed","Debug API authentication or configuration issues","Identify malformed prompts or invalid parameters","Resolve provider-specific errors (rate limits, invalid models, etc.)"],"best_for":["Developers troubleshooting prompt execution failures","Teams debugging LLM integration issues","Prompt engineers validating prompt correctness"],"limitations":["Error message quality depends on provider API error reporting — some providers may return vague error messages","No automatic error recovery or retry logic — developers must manually retry after fixing issues","Limited context about what caused the error — may require external debugging to understand root cause","Error messages may not be actionable — may require knowledge of provider-specific error codes","No error tracking or analytics — errors are not logged for later analysis"],"requires":["VS Code 1.80 or later","Prompt execution that results in an error"],"input_types":["error response from LLM provider"],"output_types":["error message","diagnostic information","potential remediation suggestions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.model.selection.and.configuration","name":"multi-provider llm model selection and configuration","description":"Allows developers to configure and switch between multiple LLM providers (Azure OpenAI, OpenAI, local models) within the extension settings, specifying API endpoints, authentication credentials, and model selection. The playground respects these configurations and routes prompt execution requests to the selected provider, enabling provider-agnostic prompt testing and comparison across different model backends.","intents":["Test a prompt against multiple LLM providers to compare quality and cost","Switch between Azure OpenAI and OpenAI without reconfiguring prompts","Use local models for development and cloud models for production testing","Manage API credentials securely within VS Code settings"],"best_for":["Teams evaluating multiple LLM providers","Developers optimizing for cost vs. quality tradeoffs","Organizations with hybrid cloud/local LLM deployments"],"limitations":["No built-in credential encryption — API keys stored in VS Code settings (use VS Code's credential storage or environment variables for security)","Provider switching requires manual configuration; no automatic provider failover on API errors","Limited to providers with compatible API schemas; custom provider support unknown","No cost tracking or usage analytics across providers"],"requires":["VS Code 1.80 or later","API credentials for at least one LLM provider (Azure OpenAI, OpenAI, or compatible endpoint)","Network access to provider endpoints"],"input_types":["provider configuration (endpoint URL, API key, model name)","model selection from dropdown or text input"],"output_types":["active provider configuration","available models list","execution routed to selected provider"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_3","uri":"capability://text.generation.language.prompt.variable.substitution.and.templating","name":"prompt variable substitution and templating","description":"Supports variable placeholders within prompts (e.g., {{variable_name}}) that can be substituted with values at execution time. The playground provides an interface to input variable values before execution, enabling developers to test prompts with different inputs without modifying the prompt definition itself. Variables are resolved and injected into the prompt before sending to the LLM provider.","intents":["Test a prompt template with multiple input variations","Parameterize prompts for reuse across different contexts","Avoid hardcoding example data in prompt definitions","Enable non-technical users to test prompts by filling in variable values"],"best_for":["Developers building prompt templates for production use","Teams sharing prompts across different use cases","Prompt engineers testing prompt behavior with diverse inputs"],"limitations":["Variable syntax and scoping rules not fully documented in extension; requires reference to Prompty specification","No type validation for variables — any string value accepted regardless of expected type","No conditional logic or variable transformation functions (e.g., uppercase, truncate)","Variable history not persisted between sessions; values must be re-entered each time"],"requires":["VS Code 1.80 or later","Prompty file with variable placeholders defined","Understanding of variable syntax ({{variable_name}})"],"input_types":["variable placeholder in prompt template","variable value input by user"],"output_types":["prompt with variables substituted","execution request with resolved values"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_4","uri":"capability://code.generation.editing.syntax.highlighting.and.intellisense.for.prompty.files","name":"syntax highlighting and intellisense for prompty files","description":"Provides VS Code language support for .prompty files including syntax highlighting, code completion, and inline documentation. The extension registers a language definition for Prompty format, enabling developers to write and edit prompts with visual feedback and autocomplete suggestions for valid Prompty syntax elements (e.g., metadata fields, message roles, model parameters).","intents":["Write Prompty files with visual syntax guidance","Discover available Prompty syntax options via autocomplete","Reduce syntax errors when defining prompts","Improve readability of prompt definitions"],"best_for":["Developers new to Prompty format","Teams standardizing on Prompty for prompt management","Anyone writing .prompty files in VS Code"],"limitations":["IntelliSense completeness depends on Prompty schema definition; may not cover all valid syntax options","No semantic validation (e.g., checking if referenced models exist) — only syntactic validation","Autocomplete suggestions may not reflect provider-specific model parameters","No hover documentation for Prompty syntax elements (requires external documentation lookup)"],"requires":["VS Code 1.80 or later",".prompty file extension"],"input_types":["plaintext .prompty file"],"output_types":["syntax highlighting","autocomplete suggestions","diagnostic errors/warnings"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_5","uri":"capability://memory.knowledge.prompt.execution.history.and.output.inspection","name":"prompt execution history and output inspection","description":"Captures execution history of prompts run in the playground, storing outputs and metadata (execution time, token usage, model used, timestamp). Developers can inspect previous executions to compare outputs, review token consumption, and debug prompt behavior over time. History is accessible within the VS Code interface, likely in a sidebar panel or output window.","intents":["Review previous prompt outputs without re-executing","Compare outputs from multiple prompt iterations","Monitor token usage and execution performance","Debug why a prompt produced unexpected output"],"best_for":["Prompt engineers iterating on prompt quality","Developers optimizing for token efficiency","Teams reviewing prompt behavior changes"],"limitations":["History persistence scope unknown — may be session-only or persisted to workspace; unclear if history survives VS Code restart","No export functionality documented for history (e.g., CSV, JSON export)","History size limits unknown — may accumulate unbounded or have automatic pruning","No filtering or search within history — may be difficult to find specific executions in large histories","Token usage accuracy depends on provider API reporting; may not reflect actual billing"],"requires":["VS Code 1.80 or later","At least one prompt execution in the playground"],"input_types":["prompt execution results from LLM provider"],"output_types":["execution history list","output text","metadata (tokens, latency, model, timestamp)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_6","uri":"capability://automation.workflow.keybinding.customization.for.prompt.execution.and.navigation","name":"keybinding customization for prompt execution and navigation","description":"Allows developers to configure custom keyboard shortcuts for common playground actions such as executing a prompt, clearing output, switching providers, or navigating between prompts. Keybindings are configurable via VS Code's keybindings.json file, enabling power users to optimize their workflow with custom shortcuts tailored to their preferences.","intents":["Execute prompts with a single keyboard shortcut instead of clicking buttons","Speed up prompt iteration by reducing mouse usage","Customize shortcuts to match existing muscle memory from other tools","Enable hands-on-keyboard workflow for prompt engineering"],"best_for":["Power users spending significant time in the prompt playground","Developers with strong keyboard-driven workflows","Teams standardizing on consistent keybindings across tools"],"limitations":["Keybinding configuration requires manual editing of VS Code keybindings.json; no UI for binding configuration","Keybindings may conflict with other VS Code extensions or built-in shortcuts; conflict resolution is user's responsibility","No preset keybinding profiles (e.g., Vim, Emacs, IntelliJ) — all customization is manual","Keybinding documentation incomplete; specific command names and available actions not fully documented"],"requires":["VS Code 1.80 or later","Access to VS Code keybindings.json configuration file","Knowledge of VS Code keybinding syntax"],"input_types":["keybinding configuration (key combination + command)"],"output_types":["keyboard shortcut mapped to playground action"],"categories":["automation-workflow","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_7","uri":"capability://memory.knowledge.workspace.aware.prompt.context.and.file.integration","name":"workspace-aware prompt context and file integration","description":"Integrates with VS Code's workspace context, allowing prompts to reference or access files within the current workspace. Developers can potentially include file content, code snippets, or project structure as context in prompts, enabling prompts to be aware of the surrounding codebase. The exact scope of file access and integration mechanism is not fully documented but likely includes file picker UI or path-based file references.","intents":["Include code snippets from the current project in a prompt for code review or generation","Reference project structure or configuration files in prompts","Test prompts with actual project files without manual copy-paste","Enable context-aware prompt testing based on workspace content"],"best_for":["Developers building code-aware LLM features","Teams using LLMs for code analysis or generation within their projects","Prompt engineers testing prompts with real project context"],"limitations":["File access scope and permissions not documented — unclear if all workspace files are accessible or if there are restrictions","No automatic project structure indexing — developers may need to manually reference files","Large file handling unknown — may have size limits or performance issues with large files","No built-in code parsing or semantic understanding — file content is treated as plain text","Security implications of file access not documented — unclear if sensitive files (credentials, secrets) are protected"],"requires":["VS Code 1.80 or later","Active VS Code workspace with files"],"input_types":["file path reference","file content from workspace"],"output_types":["file content injected into prompt","prompt execution with file context"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_8","uri":"capability://data.processing.analysis.prompt.comparison.and.a.b.testing.interface","name":"prompt comparison and a/b testing interface","description":"Enables side-by-side comparison of outputs from different prompt variations or different LLM providers. Developers can run multiple prompt versions or use different models and view outputs in a comparative view, facilitating A/B testing and prompt optimization. The interface likely displays outputs side-by-side with metadata (tokens, latency, model) for each execution.","intents":["Compare outputs from two prompt variations to determine which is better","Test the same prompt against different LLM models to evaluate quality differences","Measure token usage differences between prompt variations","Make data-driven decisions about which prompt version to use in production"],"best_for":["Prompt engineers optimizing prompt quality","Teams evaluating different LLM models","Developers making cost vs. quality tradeoffs"],"limitations":["Comparison interface details not documented — unclear if it supports more than 2-way comparison","No statistical significance testing or automated quality metrics — comparison is visual/manual","No built-in scoring or ranking system — developers must manually evaluate which output is better","Comparison results not persisted — no history of past A/B tests or results tracking","Limited to textual comparison — no support for comparing structured outputs or metrics"],"requires":["VS Code 1.80 or later","Multiple prompt executions to compare"],"input_types":["two or more prompt execution results"],"output_types":["side-by-side comparison view","metadata comparison (tokens, latency, model)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-prompty__cap_9","uri":"capability://data.processing.analysis.prompt.metadata.and.model.parameter.configuration","name":"prompt metadata and model parameter configuration","description":"Allows configuration of prompt metadata (name, description, version) and model-specific parameters (temperature, max_tokens, top_p, frequency_penalty) within the Prompty file format. These parameters are parsed from the Prompty file and applied when executing prompts, enabling developers to fine-tune model behavior without modifying the prompt text itself. Configuration is declarative and portable across different environments.","intents":["Configure model parameters (temperature, max_tokens) for a specific prompt","Store prompt metadata (name, description, version) alongside the prompt definition","Ensure consistent model parameters across different environments (dev, staging, prod)","Document prompt intent and usage through metadata fields"],"best_for":["Developers fine-tuning LLM behavior for specific use cases","Teams standardizing on consistent model parameters","Prompt engineers optimizing for quality, cost, or latency"],"limitations":["Parameter validation is limited — no warnings if parameters are outside recommended ranges","Provider-specific parameters may not be supported — limited to common parameters (temperature, max_tokens, etc.)","No parameter presets or templates — developers must manually configure each parameter","Parameter impact not documented in extension — developers must reference LLM provider documentation","No automatic parameter tuning or optimization — all tuning is manual"],"requires":["VS Code 1.80 or later","Prompty file with metadata and parameter configuration"],"input_types":["metadata fields (name, description, version)","model parameters (temperature, max_tokens, top_p, frequency_penalty)"],"output_types":["parsed metadata","applied model parameters in execution request"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.80 or later","API key for Azure OpenAI, OpenAI, or compatible LLM provider","Network connectivity to LLM provider endpoint","Prompty file format (.prompty) for prompt definition","Understanding of Prompty file format specification",".prompty file extension","Prompt execution that results in an error","API credentials for at least one LLM provider (Azure OpenAI, OpenAI, or compatible endpoint)","Network access to provider endpoints","Prompty file with variable placeholders defined"],"failure_modes":["Execution latency depends on external LLM provider response times (typically 1-10 seconds per request)","No built-in prompt versioning or history tracking — requires manual file management","Limited to single-prompt testing; no batch execution or comparative analysis UI","Requires valid API credentials for configured provider; no fallback to local inference","Prompty format specification is not fully documented in VS Code extension; requires external documentation at prompty.ai","No visual schema builder — developers must write Prompty syntax manually","Validation is client-side only; no server-side schema enforcement","No migration tools for converting existing prompts from other formats to Prompty","Error message quality depends on provider API error reporting — some providers may return vague error messages","No automatic error recovery or retry logic — developers must manually retry after fixing issues","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.53,"quality":0.32,"ecosystem":0.35000000000000003,"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=prompty","compare_url":"https://unfragile.ai/compare?artifact=prompty"}},"signature":"w3lYD6RtXtDWq0NvUI0luvmXXnMS/4d9V40/8I7lb/5QpCZPEIF7grBaojyLeyyWsa5In5Ym0nJ3mpNsx0/qBw==","signedAt":"2026-06-21T10:39:59.573Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prompty","artifact":"https://unfragile.ai/prompty","verify":"https://unfragile.ai/api/v1/verify?slug=prompty","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"}}