Why are examples critical in system prompt engineering?

Examples shape model behavior through concrete scenarios, showing the agent how to handle common inputs, edge cases, and requests that are out of scope.

Should I include detailed workflows in a system prompt?

No, it is better to provide high-level guidelines and constraints to allow the agent autonomy, as over-prescriptive formats can over-constrain the AI's effectiveness.

How do I define an agent's identity using this skill?

The skill uses a 'Purpose' section to define exactly who the agent is, its specific expertise area, and its primary functional role in clear, direct statements.

What is the difference between a system prompt and a user prompt?

A system prompt establishes the persistent identity and rules for all conversations, having a much higher impact on behavior, whereas a user prompt is specific to a single request.

System Prompt Engineering

Name: System Prompt Engineering
Author: melodic-software

bymelodic-software

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Data Science & ML

Architects high-impact system prompts to define agent identity, rules, and behavior patterns for custom AI assistants.

About

The System Prompt Engineering skill provides a rigorous framework for designing and validating the system prompts that govern custom AI agents. It guides developers through a structured process of establishing agent identity, defining persistent rules, and crafting high-fidelity examples that shape model behavior more effectively than standard user prompts. By focusing on a clear architecture consisting of Purpose, Instructions, and Examples, this skill ensures that specialized assistants remain within their domain expertise while handling edge cases and boundary requests with consistency.

Key Features

Structured 'Purpose-Instructions-Examples' prompt architecture
Rule-based boundary definition and constraint mapping
1 GitHub stars
Expertise establishment for domain-specific agent types
Four-step design process: Identity, Boundaries, Examples, Validation
Behavior-shaping via few-shot example templates

Use Cases

Standardizing output formats and tones across custom AI agent workflows
Defining persistent operational boundaries and safety rules for enterprise assistants
Building specialized agents for focused domains like security auditing or code review

About

Key Features

Structured 'Purpose-Instructions-Examples' prompt architecture
Rule-based boundary definition and constraint mapping
1 GitHub stars
Expertise establishment for domain-specific agent types
Four-step design process: Identity, Boundaries, Examples, Validation
Behavior-shaping via few-shot example templates

Use Cases

Standardizing output formats and tones across custom AI agent workflows
Defining persistent operational boundaries and safety rules for enterprise assistants
Building specialized agents for focused domains like security auditing or code review