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implement-rule-engine

Define and enforce business rules with a YAML DSL for the Firefly Rule Engine.

Updated Aug 23, 2026
Search Tags:#validation#java#audit-trails#rule-engine#yaml-dsl#reactive-evaluation#ast-parser
One-click install
npx skills add https://github.com/fireflyframework/fireflyframework-claude-skills --skill implement-rule-engine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: implement-rule-engine
Source: https://github.com/fireflyframework/fireflyframework-claude-skills/tree/main/skills/implement-rule-engine
Command: npx skills add https://github.com/fireflyframework/fireflyframework-claude-skills --skill implement-rule-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Firefly Rule Engine provides a YAML DSL and multi-module architecture to define, parse, evaluate, and audit business rules with a reactive runtime and optional Python compilation.

Core Features & Use Cases

  • YAML DSL for rule definitions, including inputs, constants, when/then/else, and complex conditions.
  • AST-based parsing and evaluation with support for audits, validation, and REST APIs.
  • Multi-module structure (interfaces, models, core, web, sdk) and a Python compilation option for offline execution.
  • Use cases include defining credit checks, eligibility workflows, and batch rule evaluations across services.

Quick Start

Provide a YAML rule that encodes a simple business decision and evaluate it using the Rule Engine to obtain outputs and audit trails.

Frequently Asked Questions about implement-rule-engine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define business rules using a YAML DSL for evaluation?▼

Yes, you can compile YAML business rules into Python for offline execution. The engine generates runtime-ready Python code from your AST-parsed rule definitions, allowing batch evaluation and validation outside the primary application environment.

How does AST-based parsing work for business rule validation?▼

AST-based parsing transforms your YAML DSL rules into an abstract syntax tree for structured evaluation. This mechanism enables reactive runtime checks, produces audit trails for compliance, and supports complex condition validation across multi-module architectures.

What is the best way to audit business rule evaluations in a reactive system?▼

The best way to audit reactive rule evaluations is using an engine that generates audit trails during AST parsing. This approach captures decision paths, inputs, and outputs automatically, satisfying compliance requirements for credit checks and eligibility workflows.

Can I compile YAML rules to Python for offline execution?▼

Yes, you can compile YAML business rules into Python for offline execution. The engine generates runtime-ready Python code from your AST-parsed rule definitions, allowing batch evaluation and validation outside the primary application environment.

Does the rule engine support REST endpoints for batch rule evaluations?▼

Yes, the rule engine supports REST endpoints through its web module for batch rule evaluations. You can submit YAML DSL definitions to the API, trigger reactive evaluation, and retrieve outputs alongside detailed audit trails for each processed request.

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