LLVM Backend

Expert skill for LLVM integration including IR generation, optimization passes, and native code emission

509 stars

Best use case

LLVM Backend is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Expert skill for LLVM integration including IR generation, optimization passes, and native code emission

Teams using LLVM Backend should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/llvm-backend/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/programming-languages/skills/llvm-backend/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/llvm-backend/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How LLVM Backend Compares

Feature / AgentLLVM BackendStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Expert skill for LLVM integration including IR generation, optimization passes, and native code emission

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# LLVM Backend Skill

## Overview

Expert skill for LLVM integration including IR generation, optimization passes, and native code emission.

## Capabilities

- Generate LLVM IR from high-level AST/IR
- Configure and run LLVM optimization passes
- Implement custom LLVM passes
- Handle LLVM type system mapping
- Generate debug information (DWARF)
- Configure target machine and code generation options
- Implement LLVM JIT (ORC, MCJIT) integration
- Handle cross-compilation target triples

## Target Processes

- code-generation-llvm.js
- jit-compiler-development.js
- debugger-adapter-development.js
- ir-design.js

## Dependencies

- LLVM C++ API
- llvm-sys bindings
- Inkwell (Rust LLVM bindings)

## Usage Guidelines

1. **Type Mapping**: Establish clear mapping between source types and LLVM types
2. **SSA Form**: Leverage LLVM's SSA form; generate clean IR and let LLVM optimize
3. **Debug Info**: Generate debug info from the start using DIBuilder
4. **Optimization Levels**: Test with -O0 first, then enable optimizations incrementally
5. **Target Configuration**: Abstract target-specific code behind target triple configuration

## Output Schema

```json
{
  "type": "object",
  "properties": {
    "llvmVersion": { "type": "string" },
    "targetTriple": { "type": "string" },
    "optimizationLevel": {
      "type": "string",
      "enum": ["O0", "O1", "O2", "O3", "Os", "Oz"]
    },
    "passes": {
      "type": "array",
      "items": { "type": "string" }
    },
    "generatedFiles": {
      "type": "array",
      "items": { "type": "string" }
    }
  }
}
```