ordo-jit-optimization — engine ordo-jit-optimization, community, engine, ide skills, high-performance, rule-engine, rules-engine, Claude Code, Cursor, Windsurf

v1.0.0

이 스킬 정보

고성능 에이전트가 고급 JIT 컴파일 및 스키마 인식 직접 메모리 액세스를 최적의 규칙 엔진 실행을 위해 필요로 할 때 적합합니다. Ordo JIT compilation and performance optimization guide. Includes Schema-aware JIT, TypedContext derive macro, Cranelift compilation, performance tuning. Use for optimizing rule execution performance, reducing latency, increasing throughput.

# Core Topics

Pama-Lee Pama-Lee
[24]
[2]
Updated: 3/10/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
44
Canonical Locale
en
Detected Body Locale
en

고성능 에이전트가 고급 JIT 컴파일 및 스키마 인식 직접 메모리 액세스를 최적의 규칙 엔진 실행을 위해 필요로 할 때 적합합니다. Ordo JIT compilation and performance optimization guide. Includes Schema-aware JIT, TypedContext derive macro, Cranelift compilation, performance tuning. Use for optimizing rule execution performance, reducing latency, increasing throughput.

이 스킬을 사용하는 이유

에이전트가 Cranelift 기반의 JIT 컴파일을 사용하여 스키마 인식 직접 메모리 액세스와 Expr AST를 활용하여 20-30배의 성능 개선을 실현하고, Rust 기반 개발 및 금융 애플리케이션을 지원합니다.

최적의 용도

고성능 에이전트가 고급 JIT 컴파일 및 스키마 인식 직접 메모리 액세스를 최적의 규칙 엔진 실행을 위해 필요로 할 때 적합합니다.

실행 가능한 사용 사례 for ordo-jit-optimization

JIT 컴파일을 사용하여 규칙 엔진 성능을 최적화하는 것
스키마 인식 직접 메모리 액세스를 사용하여 고성능 금융 모델을 생성하는 것
Expr AST를 디버깅하여 실행 효율을 향상시키는 것

! 보안 및 제한 사항

  • Rust 프로그래밍 언어가 필요합니다
  • Cranelift 기반의 JIT 컴파일러에 의존합니다
  • 스키마 인식 직접 메모리 액세스가 최적의 성능을 달성하기 위해 필요합니다

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ordo-jit-optimization?

고성능 에이전트가 고급 JIT 컴파일 및 스키마 인식 직접 메모리 액세스를 최적의 규칙 엔진 실행을 위해 필요로 할 때 적합합니다. Ordo JIT compilation and performance optimization guide. Includes Schema-aware JIT, TypedContext derive macro, Cranelift compilation, performance tuning. Use for optimizing rule execution performance, reducing latency, increasing throughput.

How do I install ordo-jit-optimization?

Run the command: npx killer-skills add Pama-Lee/Ordo/ordo-jit-optimization. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ordo-jit-optimization?

Key use cases include: JIT 컴파일을 사용하여 규칙 엔진 성능을 최적화하는 것, 스키마 인식 직접 메모리 액세스를 사용하여 고성능 금융 모델을 생성하는 것, Expr AST를 디버깅하여 실행 효율을 향상시키는 것.

Which IDEs are compatible with ordo-jit-optimization?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ordo-jit-optimization?

Rust 프로그래밍 언어가 필요합니다. Cranelift 기반의 JIT 컴파일러에 의존합니다. 스키마 인식 직접 메모리 액세스가 최적의 성능을 달성하기 위해 필요합니다.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add Pama-Lee/Ordo/ordo-jit-optimization. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ordo-jit-optimization immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

ordo-jit-optimization

Install ordo-jit-optimization, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

Ordo JIT Compilation and Performance Optimization

Schema-Aware JIT

Ordo's JIT compiler is based on Cranelift, supporting Schema-aware direct memory access with 20-30x performance improvement.

Core Architecture

                    ┌─────────────────┐
                    │   Expr AST      │
                    └────────┬────────┘
                             │
                    ┌────────▼────────┐
                    │ SchemaJITCompiler│
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
     ┌────────▼────────┐    │    ┌────────▼────────┐
     │  Field Offset   │    │    │  Native Code    │
     │   Resolution    │    │    │   Generation    │
     └─────────────────┘    │    └─────────────────┘
                    ┌───────▼────────┐
                    │ Machine Code   │
                    │ ldr d0, [ptr+N]│
                    └────────────────┘

TypedContext Derive Macro

rust
1use ordo_derive::TypedContext; 2 3#[derive(TypedContext)] 4struct UserContext { 5 age: i64, 6 balance: f64, 7 vip_level: i64, 8 #[typed_context(skip)] // Skip non-numeric fields 9 name: String, 10}

The generated Schema contains field offsets, JIT compiler directly generates memory load instructions.

Using JIT Evaluator

rust
1use ordo_core::expr::jit::{SchemaJITCompiler, SchemaJITEvaluator}; 2 3// Create compiler 4let mut compiler = SchemaJITCompiler::new()?; 5 6// Compile expression (with Schema) 7let schema = UserContext::schema(); 8let compiled = compiler.compile_with_schema(&expr, &schema)?; 9 10// Execute 11let ctx = UserContext { age: 25, balance: 1000.0, vip_level: 3 }; 12let result = unsafe { compiled.call_typed(&ctx)? };

Performance Comparison

MethodLatencyUse Case
Interpreter~1.63 µsDynamic rules, development/debugging
Bytecode VM~200 nsGeneral purpose
Schema JIT~50-80 nsHigh-frequency execution, fixed Schema

Optimization Strategies

1. Expression Pre-compilation

rust
1// Pre-compile expressions when loading rules 2let mut ruleset = RuleSet::from_json(json)?; 3ruleset.compile()?; // Pre-compile all expressions to bytecode 4 5// Or use one-step loading 6let ruleset = RuleSet::from_json_compiled(json)?;

2. Batch Execution

rust
1use ordo_core::prelude::*; 2 3let executor = RuleExecutor::new(); 4 5// Batch execution (reduces lock contention) 6let inputs: Vec<Value> = load_batch(); 7let results = executor.execute_batch(&ruleset, inputs)?;

3. Vectorized Evaluation

rust
1use ordo_core::expr::VectorizedEvaluator; 2 3let evaluator = VectorizedEvaluator::new(); 4let contexts: Vec<Context> = prepare_contexts(); 5let results = evaluator.eval_batch(&expr, &contexts)?;

4. Function Fast Path

Common functions (len, sum, max, min, abs, count, is_null) have inline fast paths, avoiding HashMap lookups.

Compiler Configuration

JIT Compiler Options

rust
1let mut compiler = SchemaJITCompiler::new()?; 2 3// View compilation statistics 4let stats = compiler.stats(); 5println!("Successful compiles: {}", stats.successful_compiles); 6println!("Total code size: {} bytes", stats.total_code_size);

Feature Flags

Configure in Cargo.toml:

toml
1[dependencies] 2ordo-core = { version = "0.2", features = ["jit"] } 3 4# Full features 5ordo-core = { version = "0.2", features = ["default"] } # jit + signature + derive

Note: JIT is not available on WASM targets (Cranelift doesn't support wasm32).

Performance Tuning Checklist

Compile-time Optimization

  • Build with --release mode
  • Enable LTO: lto = true
  • Set codegen-units = 1

Runtime Optimization

  • Pre-compile rule expressions
  • Use JIT for fixed Schema
  • Batch execution to reduce overhead
  • Set reasonable max_depth

Server Optimization

  • Set RUST_LOG=warn or info
  • Disable unnecessary tracing
  • Use connection pooling
  • Configure appropriate worker count

Benchmarking

Run built-in benchmarks:

bash
1# Basic benchmarks 2cargo bench --package ordo-core 3 4# JIT comparison tests 5cargo bench --package ordo-core --bench jit_comparison_bench 6 7# Schema JIT tests 8cargo bench --package ordo-core --bench schema_jit_bench

Typical Results (Apple Silicon)

expression/eval/simple_compare    time: [79.234 ns]
expression/eval/function_call     time: [211.45 ns]
rule/simple_ruleset              time: [1.6312 µs]
jit/schema_aware/numeric         time: [52.341 ns]

Key Files

  • crates/ordo-core/src/expr/jit/schema_compiler.rs - Schema JIT compiler
  • crates/ordo-core/src/expr/jit/schema_evaluator.rs - JIT evaluator
  • crates/ordo-core/src/expr/jit/typed_context.rs - Typed context
  • crates/ordo-derive/src/lib.rs - TypedContext derive macro
  • crates/ordo-core/benches/ - Benchmarks

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