llm-prompt-engineering
CommunityDesign prompts for reliable AI agents
Software Engineering#chain-of-thought#llm#agents#orchestrator#few-shot#prompt-engineering#system-prompt
Authorabzhaw
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Streamlines the creation and tuning of LLM prompts to maximize reliability and alignment of Julia's orchestrator, cowork-mcp, and sub-agents.
Core Features & Use Cases
- System prompts and role definitions anchor agent behavior and expectations.
- Few-shot templates and examples guide consistent responses across tasks.
- Chain-of-thought prompts and output constraints improve reasoning and ensure structured outputs.
- Use Case: When building or refining AI agents in Julia's architecture, design prompts that ensure predictable tool usage, safer outputs, and easier integration with the orchestration layer.
Quick Start
Draft a robust system prompt that defines the agent's role, context, capabilities, constraints, and output format for a given task.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: llm-prompt-engineering Download link: https://github.com/abzhaw/juliaz_agents/archive/main.zip#llm-prompt-engineering Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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