JIT Compilation

Expert skill for just-in-time compilation including profiling, tiered compilation, and deoptimization

509 stars

Best use case

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

Expert skill for just-in-time compilation including profiling, tiered compilation, and deoptimization

Teams using JIT Compilation 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/jit-compilation/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/programming-languages/skills/jit-compilation/SKILL.md"

Manual Installation

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

How JIT Compilation Compares

Feature / AgentJIT CompilationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Expert skill for just-in-time compilation including profiling, tiered compilation, and deoptimization

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

# JIT Compilation Skill

## Overview

Expert skill for just-in-time compilation including profiling, tiered compilation, and deoptimization.

## Capabilities

- Implement execution profiling and hot path detection
- Design tiered compilation strategies (baseline + optimizing)
- Implement on-stack replacement (OSR)
- Implement speculative optimizations with guards
- Design deoptimization frame reconstruction
- Implement inline caching and type feedback
- Design code cache management and eviction
- Implement method inlining heuristics

## Target Processes

- jit-compiler-development.js
- bytecode-vm-implementation.js
- interpreter-implementation.js

## Dependencies

V8/HotSpot architecture references

## Usage Guidelines

1. **Tiered Approach**: Start with a baseline tier, add optimizing tier when profiling data is available
2. **Profile-Guided**: Use profiling data to guide optimization decisions
3. **Speculation**: Implement guards for speculative optimizations with clean deoptimization
4. **OSR**: Implement OSR for long-running loops to benefit from optimization mid-execution
5. **Code Cache**: Implement code cache management to handle memory pressure

## Output Schema

```json
{
  "type": "object",
  "properties": {
    "tiers": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "trigger": { "type": "string" }
        }
      }
    },
    "profilingMethod": {
      "type": "string",
      "enum": ["counters", "sampling", "tracing"]
    },
    "osrSupport": { "type": "boolean" },
    "generatedFiles": {
      "type": "array",
      "items": { "type": "string" }
    }
  }
}
```