Add Genaiscript for quantifier callbacks podcast generation

Co-authored-by: NikolajBjorner <3085284+NikolajBjorner@users.noreply.github.com>

genaisrc/quantifier_callbacks_podcast.genai.mts

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+script({
+    title: "Quantifier Instantiation Callbacks Podcast Generator",
+    description: "Generate an engaging podcast script about Z3's quantifier instantiation callback feature",
+    systemSafety: true,
+    parameters: {
+        style: {
+            type: "string",
+            description: "Podcast style: conversational, technical, or educational",
+            default: "conversational"
+        },
+        duration: {
+            type: "string", 
+            description: "Target duration: short (5-10 min), medium (15-20 min), or long (25-30 min)",
+            default: "medium"
+        }
+    },
+})
+
+const { style, duration } = env.vars
+
+// Read the documentation and examples
+const documentation = def("DOCUMENTATION", 
+    await workspace.readText("doc/quantifier_instantiation_callback.md"), {
+    language: "markdown",
+    maxTokens: 8000
+})
+
+const pythonExample = def("PYTHON_EXAMPLE",
+    await workspace.readText("examples/python/quantifier_instantiation_callback.py"), {
+    language: "python", 
+    maxTokens: 4000
+})
+
+const cppExample = def("CPP_EXAMPLE",
+    await workspace.readText("examples/c++/quantifier_instantiation_callback.cpp"), {
+    language: "cpp",
+    maxTokens: 4000
+})
+
+const usageGuide = def("USAGE_GUIDE",
+    await workspace.readText("examples/README_quantifier_callbacks.md"), {
+    language: "markdown",
+    maxTokens: 3000
+})
+
+$`## Task
+
+You are a podcast script writer creating an engaging and informative podcast episode about Z3's quantifier instantiation callback feature. This is an advanced feature added in Z3 version 4.15.3 that allows fine-grained control over the theorem prover's quantifier instantiation process.
+
+## Context
+
+Based on the provided documentation (${documentation}), Python examples (${pythonExample}), C++ examples (${cppExample}), and usage guide (${usageGuide}), create a podcast script that makes this advanced feature accessible and interesting.
+
+## Podcast Format
+
+Style: ${style}
+Target Duration: ${duration}
+
+## Instructions
+
+Create a ${style} podcast script with the following structure:
+
+### Introduction (2-3 minutes)
+- Hook: Start with an interesting problem that quantifier instantiation callbacks solve
+- Brief introduction to Z3 and quantifiers
+- What this episode will cover
+
+### Main Content
+- **What are quantifier instantiation callbacks?**
+  - Explain in accessible terms what quantifiers are in Z3
+  - How Z3 normally handles quantifier instantiation
+  - What the callback feature adds
+
+- **Why do we need this feature?**
+  - Performance optimization scenarios
+  - Custom solving strategies
+  - Debugging and analysis use cases
+  - Real-world examples where this matters
+
+- **How to use it in practice**
+  - Walk through a simple Python example
+  - Show the callback signature and return values
+  - Demonstrate the control flow
+
+- **Advanced use cases**
+  - Pattern-based filtering
+  - Instantiation limiting strategies
+  - Integration with larger solving workflows
+
+- **Implementation across languages**
+  - Python API (most accessible)
+  - C++ API (for performance)
+  - C API (for low-level integration)
+
+### Conclusion (1-2 minutes)
+- Recap of key benefits
+- When to consider using this feature
+- Resources for learning more
+
+## Tone and Style Guidelines
+
+For **conversational** style:
+- Use a friendly, approachable tone
+- Include analogies and real-world comparisons
+- Have natural dialogue flow
+- Make complex concepts accessible
+
+For **technical** style:
+- Focus on precise terminology
+- Include more code examples
+- Discuss implementation details
+- Target experienced developers
+
+For **educational** style:
+- Build concepts step by step
+- Include learning checkpoints
+- Provide clear explanations
+- Suitable for academic or training contexts
+
+## Speaker Format
+
+Use this format for dialogue:
+**[Speaker Name]:** [Content]
+
+Suggest 2-3 speakers:
+- **Host:** Main presenter who guides the conversation
+- **Expert:** Z3 developer or advanced user who provides technical insights
+- **Newcomer:** Someone learning about the feature (for conversational style) or **Developer:** Implementation-focused perspective (for technical style)
+
+## Technical Accuracy
+
+- Ensure all technical details match the documentation
+- Use correct API signatures and examples
+- Explain the relationship to Z3's overall architecture
+- Be precise about version requirements (4.15.3+)
+
+Generate a complete podcast script that would result in approximately ${duration === "short" ? "5-10" : duration === "medium" ? "15-20" : "25-30"} minutes of content when spoken.`