synapse

Inject contextual rules and state configurations into AI prompts via an 8-layer pipeline.

Updated Apr 16, 2026

Search Tags:#automation #workflow #context #agent #rules #engine

Ready to run

npx skills add https://github.com/fabiolpgomes/lodgra --skill synapse-fabiolpgomes

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: synapse
Source: https://github.com/fabiolpgomes/lodgra/tree/main/.agents/skills/synapse
Command: npx skills add https://github.com/fabiolpgomes/lodgra --skill synapse-fabiolpgomes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

The SYNAPSE engine solves the problem of fragmented AI context by providing a structured, 8-layer processing pipeline that ensures consistent, rule-based behavior across complex agentic workflows.

Core Features & Use Cases

Context Injection: Automatically injects constitution, global, and agent-specific rules into every prompt via a hook-based architecture.
Adaptive Processing: Dynamically adjusts rule injection volume based on remaining context window brackets (FRESH to CRITICAL).
Domain Management: Provides a robust CRUD interface for managing scoped domains, star-commands, and workflow-specific logic.
Use Case: Use this to enforce strict coding standards or project-specific constraints across multiple AI agents without manual repetition.

Quick Start

Use the synapse skill to check the current engine status and active domains by typing the status command.

Frequently Asked Questions about synapse

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

FAQPage Schema

How do I enforce consistent rules across multiple AI agents without repeating context manually?▼

To enforce consistent rules across multiple AI agents, you need a context engine that automatically injects global and agent-specific rules into prompts. This ensures deterministic behavior without manual repetition across complex workflows.

How does context-aware rule injection handle shrinking token budgets?▼

Context-aware rule injection dynamically adjusts the volume of rules injected into AI prompts based on remaining token context window brackets. It scales processing volume from FRESH to CRITICAL states to prevent exceeding token budget limits.

What is the best way to manage project-specific constraints for complex agentic workflows?▼

The best way to manage project-specific constraints is using a domain management system with a CRUD interface. This allows you to define scoped domains, execute star-commands, and apply workflow-specific logic to maintain consistent agent behavior.

Can I track session state and enforce deterministic behavior for automated workflows?▼

Yes, you can track session state and enforce deterministic behavior by processing prompts through a structured multi-layer pipeline. This architecture guarantees strict rule enforcement and consistent state tracking for automated agentic systems.

Why does my AI agent lose its project context during long automated workflows?▼

AI agents lose project context during long workflows due to fragmented context injection and lack of session state tracking. A unified context engine solves this by maintaining consistent state configurations and applying adaptive rule enforcement.