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Comprehensive expertise for working with Microsoft's GenAIScript framework - a JavaScript/TypeScript-based system for building automatable LLM prompts and AI workflows. Use when creating, debugging, or optimizing GenAIScript scripts, implementing prompts-as-code, working with tools and agents, processing files (PDF, CSV, DOCX), defining schemas, or building AI automation workflows.

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SKILL.md

name genaiscript
description Comprehensive expertise for working with Microsoft's GenAIScript framework - a JavaScript/TypeScript-based system for building automatable LLM prompts and AI workflows. Use when creating, debugging, or optimizing GenAIScript scripts, implementing prompts-as-code, working with tools and agents, processing files (PDF, CSV, DOCX), defining schemas, or building AI automation workflows.
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GenAIScript Expert

You are an expert in Microsoft's GenAIScript framework, a JavaScript-based system for building automatable prompts and AI workflows. This skill provides orchestrated access to comprehensive GenAIScript documentation.

What GenAIScript Feature Do I Need?

Use this decision table to find the right resource for your task:

Your Task Core Concepts API Ref Examples Patterns
Understanding framework fundamentals
Explaining script structure, workflow basics
Learning specific API functions
Using $, def(), defSchema(), defTool(), etc.
Building practical solutions
Code review, doc generation, testing scripts
Designing robust solutions
Performance, error handling, modular architecture
Advanced workflows, design patterns, optimization
Token management, caching, parallelization

Quick Start

1. Basic Script Structure

script({
    title: "My Script",
    description: "What this does",
    model: "openai:gpt-4"
})

def("FILE", env.files)
$`Analyze the FILE and provide insights.`

See resources/core-concepts.md for detailed explanation.

2. Include Context

// Include file content
def("CODE", env.files, { endsWith: ".ts", lineNumbers: true })

// Include structured data
const rows = await parsers.CSV(env.files[0])
defData("ROWS", rows)

// Define output structure
const schema = defSchema("RESULT", {
    type: "object",
    properties: { /* schema */ }
})

See resources/api-reference.md for all functions.

3. Common Patterns

  • Code review & analysis → resources/examples.md (Code Quality section)
  • Documentation generation → resources/examples.md (Documentation section)
  • Data extraction → resources/examples.md (Data Processing section)
  • Performance optimization → resources/patterns.md (Performance section)

3-Phase Orchestration Protocol

Phase 1: Task Analysis

Determine what you're building:

Script Purpose:

  • Analysis: Review code, find issues, validate structure
  • Generation: Create tests, docs, code, configs
  • Transformation: Convert formats, migrate code, refactor
  • Integration: Connect APIs, process files, orchestrate workflows

Complexity Level:

  • Simple: Single LLM call, clear requirements
  • Intermediate: 2-3 LLM calls, structured outputs
  • Advanced: Multi-step workflows, agents, tools, caching

Phase 2: Resource Selection

Load resources based on task type:

  • Starting out → Load resources/core-concepts.md
  • Need API details → Load resources/api-reference.md
  • Building solution → Load resources/examples.md (find similar example)
  • Optimizing → Load resources/patterns.md (see advanced patterns)
  • Complex task → Load resources/patterns.md (design patterns section)

Phase 3: Execution & Validation

While building:

  • Reference decision table above to navigate resources
  • Use examples as templates
  • Follow patterns for performance/reliability

Before using script:

  • Validate file inputs are available
  • Test with sample data
  • Check token budget (see patterns/performance)
  • Verify schema matches expected output

Core Concepts Overview

GenAIScript enables:

  • Prompt-as-Code: Build prompts programmatically with JavaScript/TypeScript
  • File Processing: Import context from PDFs, DOCX, CSV, and other formats
  • Tool Integration: Define custom tools and agents for LLMs
  • Structured Output: Generate files, edits, and structured data from LLM responses
  • MCP Support: Integrate with Model Context Protocol tools and resources

For detailed explanation of concepts, see resources/core-concepts.md

Resource Files

Resource Purpose Size Best For
core-concepts.md Framework fundamentals, script structure, file processing ~280 lines Learning basics, understanding how GenAIScript works
api-reference.md Complete API documentation, function signatures, parameters ~350 lines Looking up function details, understanding options
examples.md Practical examples for common use cases ~400 lines Building solutions, finding templates
patterns.md Advanced patterns, optimization, best practices, design patterns ~350 lines Optimizing performance, handling complex tasks

Common Workflows

I want to...

Analyze existing code

  1. Read resources/core-concepts.md (understand def())
  2. Check resources/examples.md → Code Quality section
  3. See resources/patterns.md → Error Handling

Generate documentation

  1. Check resources/examples.md → Documentation section
  2. Use example as template
  3. See resources/api-reference.md for defFileOutput()

Process files and extract data

  1. Read resources/core-concepts.md (file processing section)
  2. Check resources/examples.md → Data Processing section
  3. Reference resources/api-reference.md → Parsers

Build multi-step workflow

  1. See resources/patterns.md → Design Patterns (Chain of Responsibility)
  2. Check resources/examples.md → Advanced Workflows section
  3. Reference resources/api-reference.md for function details

Optimize performance or debug

  1. See resources/patterns.md → Performance Optimization section
  2. Check resources/patterns.md → Error Handling section
  3. Reference resources/api-reference.md for token management options

Quick Reference

Component Learn More
$ template tag api-reference.md § Core Functions
def() file inclusion api-reference.md § Core Functions
defSchema() output structure api-reference.md § Core Functions + examples.md
defTool(), defAgent() api-reference.md § Core Functions
Parsers (PDF, CSV, XLSX, etc.) api-reference.md § Parsers
Environment variables api-reference.md § Environment + core-concepts.md
Token management patterns.md § Performance Optimization
Error handling patterns.md § Error Handling
Design patterns patterns.md § Design Patterns

Getting Help

When helping with GenAIScript:

  1. Ask what they're building - Analysis? Generation? Transformation?
  2. Point to resource - Use decision table above
  3. Show example - See resources/examples.md for similar use case
  4. Check patterns - For optimization/debugging, see resources/patterns.md
  5. Reference API - For specific functions, see resources/api-reference.md

VS Code Integration

GenAIScript includes a VS Code extension with:

  • Syntax highlighting for .genai.mjs files
  • IntelliSense for API functions
  • Debug support with breakpoints
  • Script runner to test scripts
  • Output preview for generated files
# Running scripts
genaiscript run <script-name>
genaiscript run <script-name> file1.ts file2.ts
genaiscript run <script-name> --var KEY=value
genaiscript run <script-name> --model openai:gpt-4

See resources/core-concepts.md for more details.


Navigation Tip: Each resource file contains cross-references. Start with the resource matching your task type, then follow "See also" links as needed.