{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-genaiscript-genaiscript-vscode","slug":"genaiscript","name":"GenAIScript","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=genaiscript.genaiscript-vscode","page_url":"https://unfragile.ai/genaiscript","categories":["code-editors"],"tags":["__ext_genai.md","ai","chatgpt","chat-participant","copilot","genai","GenAIScriptMarkdown","genaiscript-markdown","gpt","gpt4","javascript","llm","mcp","openai","python","scripting","typescript"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-genaiscript-genaiscript-vscode__cap_0","uri":"capability://text.generation.language.programmatic.llm.invocation.with.template.literals","name":"programmatic llm invocation with template literals","description":"Executes LLM queries using JavaScript template literal syntax (backtick-delimited prompts with $` markers) embedded directly in GenAIScript files. The runtime parses these template expressions, sends them to configured LLM providers (OpenAI, Anthropic, or local models), and returns structured or unstructured responses that can be assigned to variables for downstream processing. This approach enables prompt composition as first-class JavaScript expressions rather than string concatenation.","intents":["Write and iterate on LLM prompts directly in VS Code without context switching","Compose multi-stage prompts where one LLM output feeds into the next prompt","Execute parameterized prompts with dynamic variable interpolation","Debug prompt behavior by running scripts incrementally with breakpoints"],"best_for":["Developers building LLM-powered automation scripts","Teams prototyping multi-step AI workflows","Solo developers iterating on prompt engineering"],"limitations":["Template literal syntax requires JavaScript/TypeScript knowledge — not accessible to non-programmers","No built-in prompt versioning or A/B testing framework","LLM response parsing relies on prompt engineering; no automatic error recovery for malformed outputs","Network latency for each LLM call is not optimized — no built-in batching or caching layer"],"requires":["VS Code 1.80+ (inferred from extension marketplace standards)","Node.js 16+ for CLI execution","API key for OpenAI, Anthropic, or compatible LLM provider","Internet connectivity for LLM API calls"],"input_types":["JavaScript template literals with embedded variables","File content (via env.files)","Structured data (JSON, CSV)","Environment variables"],"output_types":["Plain text responses","JSON (if prompted)","Structured data (via schema validation)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_1","uri":"capability://data.processing.analysis.multi.format.file.ingestion.and.parsing","name":"multi-format file ingestion and parsing","description":"Automatically extracts and parses content from diverse file formats (PDF, DOCX, CSV, plain text) using specialized parsers accessible via the `parsers.*` API. Files are matched using glob patterns or explicit file arrays, parsed into structured or text representations, and made available to LLM prompts via the `env.files` context. The runtime handles encoding detection, format-specific extraction (e.g., PDF text layers, DOCX metadata), and error handling for malformed files.","intents":["Ingest documents (PDFs, Word docs) into LLM prompts for analysis or summarization","Extract structured data from CSV files and validate against schemas","Process batches of files with consistent parsing logic","Build document-aware workflows that reference file content in prompts"],"best_for":["Teams processing document-heavy workflows (contracts, reports, forms)","Data extraction pipelines requiring multi-format support","Developers building document analysis agents"],"limitations":["PDF parsing may fail on scanned images or complex layouts — no OCR capability documented","DOCX parsing extracts text but may lose formatting, tables, or embedded objects","CSV parsing assumes standard delimiters — no custom dialect support documented","Large files (>100MB) may cause memory pressure or timeouts — no streaming parser documented","No built-in deduplication or incremental parsing for repeated file processing"],"requires":["File system access to project directory","Supported file formats: PDF, DOCX, CSV, TXT","Node.js 16+ for file I/O"],"input_types":["PDF files","DOCX (Microsoft Word) documents","CSV files","Plain text files","Glob patterns for file matching"],"output_types":["Extracted text (for PDFs, DOCX)","Parsed structured data (for CSV)","File metadata (path, size, encoding)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_10","uri":"capability://data.processing.analysis.script.execution.with.file.context.and.filtering","name":"script execution with file context and filtering","description":"Executes scripts with automatic file discovery and filtering based on glob patterns or explicit file lists. The runtime matches files against patterns, loads their content, and makes them available to the script via `env.files`. This enables batch processing of files with consistent logic without manual file enumeration.","intents":["Process all files matching a pattern (e.g., all .ts files) with a single script","Filter files by name, path, or content before processing","Build batch workflows that apply LLM logic to multiple files","Implement file-aware automation that adapts to project structure"],"best_for":["Teams processing large numbers of files with consistent logic","Developers building batch code analysis or refactoring tools","Projects requiring file-aware LLM workflows"],"limitations":["Glob pattern syntax not documented — unclear if standard Unix glob or extended syntax is supported","File filtering performance not documented — large file lists may cause memory or latency issues","No built-in file exclusion patterns (e.g., .gitignore) — must be specified explicitly","File content loading may fail silently for unreadable files — error handling not documented","No incremental processing — all matched files are loaded even if only some are needed"],"requires":["File system access to project directory","Glob pattern or explicit file list","Sufficient memory for file content (depends on file count and size)"],"input_types":["Glob patterns (e.g., '**/*.ts')","Explicit file paths","File filtering criteria"],"output_types":["File list (paths and content)","Processed results per file","Aggregated results across files"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_11","uri":"capability://data.processing.analysis.output.formatting.and.result.serialization","name":"output formatting and result serialization","description":"Formats script execution results for display or export, supporting multiple output formats (plain text, JSON, structured logs). Results can be written to stdout, files, or returned as structured data for downstream processing. The runtime handles serialization of complex data types and provides options for formatting output for human readability or machine parsing.","intents":["Display script results in a readable format in VS Code or terminal","Export results to files for further processing or archival","Format results as JSON for integration with other tools","Generate structured logs for debugging or monitoring"],"best_for":["Developers building automation scripts with output requirements","Teams integrating GenAIScript into larger workflows","Projects requiring result logging or archival"],"limitations":["Output format options not documented — unclear what formats are supported","File writing mechanism not documented — unclear if scripts can write arbitrary files or only to designated directories","Result serialization of complex types not documented — may fail for circular references or non-JSON-serializable objects","No built-in result validation or schema enforcement — output may not match expected format","Performance of large result serialization not documented — may cause memory issues"],"requires":["Script execution context","Output destination (stdout, file path, or variable)","Desired output format"],"input_types":["Script execution results (any type)","Format specification (plain text, JSON, etc.)","Output destination"],"output_types":["Formatted text (stdout or file)","JSON (for machine parsing)","Structured logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_2","uri":"capability://data.processing.analysis.schema.based.data.extraction.and.validation","name":"schema-based data extraction and validation","description":"Defines JSON schemas (using JSON Schema or Zod syntax) to validate and repair LLM-generated outputs. The runtime enforces schema constraints, attempts to repair malformed data (e.g., fixing JSON syntax errors or missing fields), and provides structured output that matches the schema definition. Schemas are defined inline in scripts using `defSchema()` and can be referenced in prompts to guide LLM output format.","intents":["Extract structured data from unstructured text using LLM with guaranteed schema compliance","Validate LLM outputs before downstream processing to prevent pipeline failures","Repair common LLM mistakes (missing commas, extra quotes) automatically","Define reusable data structures for multi-step workflows"],"best_for":["Data extraction pipelines requiring strict output validation","Teams building LLM-powered ETL workflows","Developers handling unreliable LLM outputs in production"],"limitations":["Schema repair is heuristic-based — complex malformations may not be recoverable","No support for recursive or deeply nested schemas — practical depth limit unknown","Zod integration details not documented — unclear if full Zod feature set is supported","Schema validation adds latency per extraction — no caching of validation results","Error messages for validation failures may not be actionable for debugging"],"requires":["JSON Schema or Zod syntax knowledge","LLM configured to output JSON (via prompt engineering)","Node.js 16+"],"input_types":["JSON Schema definitions","Zod schema definitions","LLM-generated JSON strings"],"output_types":["Validated JSON objects","Repaired/normalized structured data","Validation error reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_3","uri":"capability://search.retrieval.semantic.vector.search.across.project.files","name":"semantic vector search across project files","description":"Performs semantic similarity search across project files using embeddings and vector retrieval. The `retrieval.vectorSearch()` API accepts a query string, embeds it using a configured embedding model, and returns the most similar files or file chunks ranked by cosine similarity. This enables context-aware file selection for LLM prompts without explicit file enumeration, supporting use cases like 'find similar code' or 'retrieve relevant documentation'.","intents":["Automatically find relevant code files for a given task without manual file selection","Retrieve documentation or examples similar to a user query","Build context-aware prompts by including semantically relevant files","Implement code search features within automation scripts"],"best_for":["Large codebases where manual file selection is impractical","Documentation-heavy projects requiring semantic search","Teams building AI-powered code navigation tools"],"limitations":["Embedding model and vector database backend not documented — unclear if local or cloud-based","Search latency depends on codebase size and embedding computation — no performance benchmarks provided","Semantic search may return false positives for ambiguous queries — no relevance threshold tuning documented","No built-in indexing strategy — unclear if files are re-embedded on every search or cached","Chunk size and overlap strategy not documented — may affect search quality for large files"],"requires":["Project files accessible via file system","Embedding model configured (provider and model type unknown)","Vector search backend (local or cloud, not specified)"],"input_types":["Query string (natural language or code snippet)","File patterns or explicit file list"],"output_types":["Ranked list of files with similarity scores","File content or file paths","Metadata (line numbers, chunk boundaries)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_4","uri":"capability://planning.reasoning.nested.prompt.composition.and.multi.stage.workflows","name":"nested prompt composition and multi-stage workflows","description":"Enables prompts to invoke other prompts via the `runPrompt()` function, allowing multi-stage LLM workflows where outputs from one prompt feed into subsequent prompts. Each nested prompt has its own context (files, variables, schema), and results are returned as structured data that can be processed or passed to downstream prompts. This pattern supports complex reasoning chains, iterative refinement, and modular prompt reuse.","intents":["Build multi-step reasoning workflows (e.g., analyze → extract → validate → summarize)","Reuse common prompt patterns across different scripts","Implement iterative refinement loops where LLM output is improved in subsequent stages","Compose complex tasks from simpler, testable prompt components"],"best_for":["Teams building complex LLM-powered automation","Developers implementing chain-of-thought reasoning patterns","Projects requiring modular, reusable prompt logic"],"limitations":["Each nested prompt incurs a separate LLM API call — no built-in batching or request coalescing","Context passing between prompts is manual — no automatic context propagation or memory management","Error handling for failed nested prompts not documented — unclear if failures cascade or are recoverable","No built-in timeout or retry logic for nested calls — long chains may fail silently","Debugging nested prompts requires manual logging — no built-in trace or step-through debugging"],"requires":["Multiple prompt definitions (via `defPrompt()` or inline)","LLM API credentials for each nested call","Understanding of prompt composition patterns"],"input_types":["Prompt name or reference","Input variables (text, structured data)","File context"],"output_types":["LLM response (text or structured data)","Validation results (if schema is applied)","Execution metadata (tokens used, latency)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_5","uri":"capability://automation.workflow.cli.based.batch.script.execution","name":"cli-based batch script execution","description":"Executes GenAIScript scripts from the command line using `npx genaiscript run`, enabling automation outside VS Code and integration with CI/CD pipelines, cron jobs, or shell scripts. The CLI accepts script paths, environment variables, and input parameters, executes the script in a headless runtime, and outputs results to stdout or files. This decouples script development (in VS Code) from script execution (in automation contexts).","intents":["Run LLM-powered scripts as part of CI/CD pipelines or scheduled jobs","Batch process files or data using GenAIScript without manual VS Code interaction","Integrate GenAIScript into existing shell scripts or automation frameworks","Execute scripts in containerized or serverless environments"],"best_for":["DevOps teams integrating LLM workflows into CI/CD","Developers building scheduled data processing jobs","Teams deploying LLM automation to production"],"limitations":["CLI output format not documented — unclear if JSON, plain text, or structured logs","No built-in logging or observability — error messages may be insufficient for debugging","Environment variable passing mechanism not documented — unclear if secrets are handled securely","Exit codes and error handling not specified — integration with shell scripts may be fragile","No built-in parallelization — batch processing of multiple files runs sequentially","Timeout and resource limits not documented — long-running scripts may hang or consume unbounded resources"],"requires":["Node.js 16+ with npx","GenAIScript extension installed globally or locally","Script file path","LLM API credentials (via environment variables or config file)"],"input_types":["Script file path","Environment variables","Command-line arguments","Input files (via glob patterns or explicit paths)"],"output_types":["stdout (script output)","Exit code (0 for success, non-zero for failure)","Output files (if script writes to disk)","Error messages (stderr)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_6","uri":"capability://automation.workflow.environment.and.context.variable.access","name":"environment and context variable access","description":"Provides access to execution context via the `env.*` namespace, including matched files (`env.files`), environment variables, and script parameters. Variables are scoped to the current script execution and can be used in prompts, file matching, and conditional logic. This enables scripts to adapt behavior based on runtime context without hardcoding values.","intents":["Access matched files for processing without explicit file enumeration","Use environment variables (e.g., API keys, configuration) in scripts","Pass parameters to scripts from CLI or VS Code","Implement context-aware logic that adapts to the current project or environment"],"best_for":["Developers building parameterized automation scripts","Teams using environment-specific configurations","Projects requiring dynamic file selection"],"limitations":["Environment variable access mechanism not documented — unclear if all env vars are accessible or only whitelisted ones","No built-in secret management — API keys and credentials may be exposed in logs or error messages","Context scope not documented — unclear if scripts can access VS Code editor state or only file system context","Variable interpolation in prompts may be vulnerable to injection attacks if user input is not sanitized","No built-in variable validation or type checking — runtime errors may occur if expected variables are missing"],"requires":["Environment variables set in shell or VS Code settings","Script file with env.* references","File patterns or explicit file lists for env.files"],"input_types":["Environment variables (string values)","File paths and patterns","Script parameters"],"output_types":["Variable values (strings, arrays, objects)","Matched file lists","Execution context metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_7","uri":"capability://code.generation.editing.vs.code.integrated.script.editing.and.debugging","name":"vs code integrated script editing and debugging","description":"Provides a native VS Code editor environment for writing, editing, and debugging GenAIScript files with syntax highlighting, autocomplete, and inline execution. Scripts are edited as `.genaiscript` or `.js` files with GenAIScript syntax, and can be executed directly from the editor via command palette or keybindings. The editor provides real-time feedback on script validity and execution results.","intents":["Write and iterate on LLM scripts with full IDE support","Debug script execution by running scripts incrementally and inspecting results","Leverage VS Code extensions and settings for script development","Quickly prototype and test prompts without leaving the editor"],"best_for":["Developers familiar with VS Code workflows","Teams building LLM automation with rapid iteration","Solo developers prototyping prompt logic"],"limitations":["Debugging capabilities not documented — unclear if breakpoints, step-through, or variable inspection are supported","Syntax highlighting and autocomplete scope not documented — may not cover all GenAIScript APIs","No built-in REPL or interactive prompt testing — scripts must be executed in full to test changes","Error messages may not be actionable — unclear if they include line numbers, stack traces, or suggestions","Performance of large scripts not documented — editor may lag with very large files or complex prompts"],"requires":["VS Code 1.80+ (inferred)","GenAIScript extension installed","Node.js 16+ for script execution"],"input_types":["GenAIScript source code (.genaiscript or .js files)","User input via command palette or keybindings"],"output_types":["Script execution results (displayed in output panel or inline)","Error messages and diagnostics","Syntax highlighting and autocomplete suggestions"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_8","uri":"capability://text.generation.language.llm.model.and.provider.configuration","name":"llm model and provider configuration","description":"Allows configuration of LLM providers (OpenAI, Anthropic, local models) and model selection within scripts or global settings. Configuration may be specified via environment variables, VS Code settings, or inline script parameters. The runtime abstracts provider-specific API differences, enabling scripts to work with multiple LLM backends without code changes.","intents":["Switch between LLM providers (OpenAI, Anthropic, local) without rewriting scripts","Configure model selection (GPT-4, Claude, etc.) per script or globally","Use local LLM models for privacy-sensitive workflows","Manage API keys and credentials securely"],"best_for":["Teams evaluating multiple LLM providers","Organizations with privacy requirements (local model support)","Developers building provider-agnostic LLM workflows"],"limitations":["Provider and model configuration mechanism not documented — unclear if set via env vars, settings, or inline","Supported providers not explicitly listed — inferred to include OpenAI and Anthropic, but others unknown","Local model support not documented — unclear if Ollama, LLaMA, or other frameworks are supported","API key management not documented — no guidance on secure credential handling","Model-specific features (function calling, vision) may not be uniformly supported across providers","Fallback or retry logic for provider failures not documented"],"requires":["API key for chosen LLM provider","Configuration mechanism (environment variables, settings file, or inline)","Network connectivity for cloud-based providers"],"input_types":["Provider name (OpenAI, Anthropic, etc.)","Model identifier (gpt-4, claude-3, etc.)","API key or credentials"],"output_types":["LLM response (text or structured data)","Execution metadata (tokens used, latency, cost)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-genaiscript-genaiscript-vscode__cap_9","uri":"capability://text.generation.language.prompt.template.and.variable.interpolation","name":"prompt template and variable interpolation","description":"Supports variable interpolation within prompt templates using JavaScript template literal syntax, allowing dynamic prompt construction based on runtime data. Variables are substituted into prompts before LLM execution, enabling parameterized prompts that adapt to different inputs. Interpolation supports expressions, function calls, and complex data structures.","intents":["Build dynamic prompts that adapt to different inputs or contexts","Reuse prompt templates across multiple use cases with different parameters","Construct prompts programmatically based on file content or user input","Implement prompt engineering patterns like few-shot examples or dynamic context"],"best_for":["Developers building parameterized LLM workflows","Teams implementing prompt templates for reuse","Projects requiring dynamic prompt construction"],"limitations":["Interpolation syntax may be ambiguous with LLM template syntax — unclear how to escape special characters","No built-in prompt validation — malformed interpolations may produce invalid prompts","Complex expressions in templates may reduce readability — no linting or formatting tools documented","Variable scope and lifetime not documented — unclear if variables persist across nested prompts","No built-in sanitization of user input — interpolated values may contain prompt injection attacks"],"requires":["JavaScript template literal syntax knowledge","Variables defined in script scope","LLM configured to accept dynamic prompts"],"input_types":["Prompt template string with ${variable} or $expression syntax","Variable values (strings, numbers, objects, arrays)"],"output_types":["Interpolated prompt string","LLM response based on interpolated prompt"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.80+ (inferred from extension marketplace standards)","Node.js 16+ for CLI execution","API key for OpenAI, Anthropic, or compatible LLM provider","Internet connectivity for LLM API calls","File system access to project directory","Supported file formats: PDF, DOCX, CSV, TXT","Node.js 16+ for file I/O","Glob pattern or explicit file list","Sufficient memory for file content (depends on file count and size)","Script execution context"],"failure_modes":["Template literal syntax requires JavaScript/TypeScript knowledge — not accessible to non-programmers","No built-in prompt versioning or A/B testing framework","LLM response parsing relies on prompt engineering; no automatic error recovery for malformed outputs","Network latency for each LLM call is not optimized — no built-in batching or caching layer","PDF parsing may fail on scanned images or complex layouts — no OCR capability documented","DOCX parsing extracts text but may lose formatting, tables, or embedded objects","CSV parsing assumes standard delimiters — no custom dialect support documented","Large files (>100MB) may cause memory pressure or timeouts — no streaming parser documented","No built-in deduplication or incremental parsing for repeated file processing","Glob pattern syntax not documented — unclear if standard Unix glob or extended syntax is supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.43,"quality":0.34,"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=genaiscript","compare_url":"https://unfragile.ai/compare?artifact=genaiscript"}},"signature":"fUThW2Fx5LgkmF3tR0Mxt7GNQbZgvkRdxhcg0/WUqbQ3A5eGOg2MlOKdGDeldCYqpbMWNDf93d3InbjC4uHQBg==","signedAt":"2026-06-22T02:34:35.331Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/genaiscript","artifact":"https://unfragile.ai/genaiscript","verify":"https://unfragile.ai/api/v1/verify?slug=genaiscript","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"}}