What is the main benefit of using the Agentica orchestration pattern?

It reduces LLM instruction ambiguity by using explicit verb mappings and directory-based handoffs, increasing task success rates from approximately 60% to over 95%.

Can I use these templates for different types of agents?

Yes, the skill includes specialized templates for various patterns including Swarm (Research), Hierarchical (Coordinator), Generator/Critic, and Jury (Voter) agents.

Why does this skill suggest using 'RETRIEVE' instead of 'Read'?

Generic verbs like 'Read' can lead to summarization or hallucinations. 'RETRIEVE' explicitly instructs the agent to fetch the exact text, ensuring data integrity across the agent chain.

How does the directory handoff mechanism improve agent performance?

By using the filesystem for communication rather than passing full transcripts through TaskOutput, it preserves clean context and prevents 'context pollution' in long-running tasks.

Agentica Prompt Engineering

Name: Agentica Prompt Engineering
Author: scooter-lacroix

byscooter-lacroix

生産性とワークフロー

Optimizes agent orchestration by providing standardized, unambiguous prompt templates and directory-based handoff patterns for multi-agent systems.

概要

This skill provides a robust framework for building reliable multi-agent systems using the Agentica orchestration pattern. By replacing ambiguous natural language instructions with explicit verb mappings and structured directory handoffs, it significantly reduces LLM hallucinations and failures. It includes specialized templates for diverse agent roles—including researchers, coordinators, and critics—ensuring seamless context preservation and high-precision task execution within Claude Code environments. Whether you are building complex autonomous workflows or simple research agents, this skill provides the architectural blueprints to move from inconsistent results to production-grade reliability.

主な機能

0 GitHub stars
Ambiguity-reducing verb mappings to ensure explicit LLM actions
Specialized prompt templates for Swarm, Hierarchical, and Critic agents
Directory-based communication mechanism to prevent context pollution
Orchestration workflow patterns for Research, Planning, and Implementation
Integrated memory system management for long-term task persistence

ユースケース

Designing complex multi-agent workflows for autonomous software development
Standardizing communication between autonomous agents via a structured filesystem
Reducing failure rates in LLM-driven research and automated planning tasks

概要

主な機能

0 GitHub stars
Ambiguity-reducing verb mappings to ensure explicit LLM actions
Specialized prompt templates for Swarm, Hierarchical, and Critic agents
Directory-based communication mechanism to prevent context pollution
Orchestration workflow patterns for Research, Planning, and Implementation
Integrated memory system management for long-term task persistence

ユースケース

Designing complex multi-agent workflows for autonomous software development
Standardizing communication between autonomous agents via a structured filesystem
Reducing failure rates in LLM-driven research and automated planning tasks