agentica-prompts

Generate structured Agentica/REPL agent prompts with directory-based handoffs.

8|1|Updated Jan 4, 2026

Search Tags:#workflow automation #agent communication #prompt engineering #ai orchestration #agentica #llm prompts

Ready to run

npx skills add https://github.com/scooter-lacroix/Maestro --skill agentica-prompts

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentica-prompts
Source: https://github.com/scooter-lacroix/Maestro/tree/main/maestro/skills/agentica-prompts
Command: npx skills add https://github.com/scooter-lacroix/Maestro --skill agentica-prompts

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the common issue of LLM instruction ambiguity, ensuring that Agentica agents reliably follow prompts, leading to more predictable and effective AI-assisted workflows.

Core Features & Use Cases

Structured Prompting: Provides templates and patterns for creating clear, unambiguous prompts.
Orchestration Patterns: Details proven workflows for multi-agent systems (e.g., Research -> Plan -> Implement -> Review).
Directory Handoff: Implements a robust file-system-based communication mechanism between agents to preserve context.
Use Case: When building a complex AI agent that needs to research a topic, plan a solution, and then implement it, these prompt structures ensure each agent understands its role and the expected output format, preventing misinterpretations.

Quick Start

Use the agentica-prompts skill to generate a system prompt for a planning agent.

Frequently Asked Questions about agentica-prompts

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

FAQPage Schema

How do I fix LLM instruction ambiguity in agent communication?▼

To fix LLM instruction ambiguity in agent communication, apply structured prompting patterns and system prompt templates that enforce clear agent roles and output formats. This approach mitigates misinterpretations and improves agent task success rates.

What is the best way to preserve context during multi-agent orchestration?▼

The best way to preserve context during multi-agent orchestration is implementing directory-based handoffs. This file-system communication mechanism passes context between agents reliably, preventing data loss during sequential workflow phases like research, planning, and implementation.

How do I structure prompts for a research, plan, implement, and review workflow?▼

To structure prompts for a research, plan, implement, and review workflow, use orchestration patterns with distinct system prompt templates for each phase. Define specific agent roles and expected output formats to ensure seamless handoffs between tasks.

Can I use directory handoffs to pass context between REPL agents?▼

Yes, you can use directory handoffs to pass context between REPL agents. This file-system-based mechanism allows agents to write and read context files sequentially, ensuring context-preserving orchestration across multi-agent systems.

Why do my AI agents fail to follow multi-step instructions consistently?▼

AI agents fail to follow multi-step instructions consistently due to common anti-patterns in agent communication and prompt ambiguity. Implementing structured prompting patterns and clear system prompt templates addresses these issues, improving success rates from 60% to over 95%.

Do I need specific agent frameworks to use structured prompt templates?▼

Structured prompt templates are designed for Agentica and REPL agents. They require no external dependencies, providing directory-based handoffs and system prompt templates directly to improve context-preserving orchestration and workflow automation.