ordo-jit-optimization — engine ordo-jit-optimization, community, engine, ide skills, high-performance, rule-engine, rules-engine

v1.0.0

Об этом навыке

Идеально подходит для агентов с высокими производительными характеристиками, требующих расширенной JIT-компиляции и прямого доступа к памяти с учетом схемы для оптимизированной работы движка правил. 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

Идеально подходит для агентов с высокими производительными характеристиками, требующих расширенной JIT-компиляции и прямого доступа к памяти с учетом схемы для оптимизированной работы движка правил. 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.

Зачем использовать этот навык

Позволяет агентам использовать компиляцию JIT на основе Cranelift для повышения производительности на 20-30%, используя прямой доступ к памяти с учетом схемы и Expr AST для оптимизированной работы движка правил, поддерживая разработку на основе Rust и финансовые приложения.

Подходит лучше всего

Идеально подходит для агентов с высокими производительными характеристиками, требующих расширенной JIT-компиляции и прямого доступа к памяти с учетом схемы для оптимизированной работы движка правил.

Реализуемые кейсы использования for ordo-jit-optimization

Оптимизация производительности движка правил с помощью компиляции JIT
Генерация высокопроизводительных финансовых моделей с использованием прямого доступа к памяти с учетом схемы
Отладка и оптимизация Expr AST для повышения эффективности выполнения

! Безопасность и ограничения

  • Требует языка программирования Rust
  • Зависит от компилятора JIT на основе Cranelift
  • Прямой доступ к памяти с учетом схемы необходим для оптимальной производительности

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 imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

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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?

Идеально подходит для агентов с высокими производительными характеристиками, требующих расширенной JIT-компиляции и прямого доступа к памяти с учетом схемы для оптимизированной работы движка правил. 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: Оптимизация производительности движка правил с помощью компиляции JIT, Генерация высокопроизводительных финансовых моделей с использованием прямого доступа к памяти с учетом схемы, Отладка и оптимизация Expr AST для повышения эффективности выполнения.

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?

Требует языка программирования Rust. Зависит от компилятора JIT на основе Cranelift. Прямой доступ к памяти с учетом схемы необходим для оптимальной производительности.

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.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

ordo-jit-optimization

Install ordo-jit-optimization, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
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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