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agentica-prompts

Generate structured Agentica/REPL prompt templates with explicit action verbs.

3.9k|296|Updated Dec 23, 2025
Search Tags:#prompt engineering#llm#ai agents#orchestration#reliability#agentica
One-click install
npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill agentica-prompts-parcadei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentica-prompts
Source: https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/agentica-prompts
Command: npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill agentica-prompts-parcadei

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/REPL agents reliably follow prompts, thereby improving the success rate of multi-agent orchestrations.

Core Features & Use Cases

  • Reliable Prompt Engineering: Provides templates and patterns to create clear, unambiguous prompts for AI agents.
  • Orchestration Patterns: Details proven workflows for complex agent coordination, including research, planning, validation, implementation, and review phases.
  • Directory Handoff: Implements a robust mechanism for agents to communicate via the filesystem, preserving context and avoiding transcript pollution.
  • Use Case: When building a complex AI system that requires multiple agents to collaborate on a task (e.g., software development, research analysis), this Skill ensures each agent understands its role and the task precisely, leading to more predictable and successful outcomes.

Quick Start

Use the agentica-prompts skill to generate a system prompt template 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 write reliable prompts for AI agents that actually follow instructions?▼

Reliable prompts for AI agents use structured templates and explicit action verbs like RETRIEVE and WRITE to mitigate LLM instruction ambiguity, improving agent compliance from 60% to over 95%.

What is the best way to coordinate multiple LLM agents in an orchestration workflow?▼

Multi-agent orchestration workflows coordinate effectively through pattern-specific prompts for research, coordination, generation, critique, and voting, defining clear communication protocols via directory handoffs.

How do AI agents communicate and share context without polluting the transcript?▼

AI agents communicate without transcript pollution by using a directory handoff mechanism, passing context and preserving state via the filesystem rather than appending to shared chat history.

Why does my LLM agent fail to follow multi-step instructions reliably?▼

LLM agents fail multi-step instructions due to prompt ambiguity, which is resolved by enforcing explicit commands and structured templates that clearly define the agent's role and task parameters.

Can I use structured prompt templates for complex software development agents?▼

Structured prompt templates support complex software development agents by detailing proven workflows for planning, validation, implementation, and review phases to ensure predictable task outcomes.

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