prompt-engineering

Prompt Engineering

Craft effective system prompts for AI agents using structured templates, best practices, and iterative refinement.

When to Use

• Writing a new system prompt for an agent
• Improving an underperforming agent's instructions
• Creating role-specific prompts for multi-agent systems
• Reviewing prompts for anti-patterns and clarity issues
• Optimizing prompts to reduce token usage without losing quality

Available Operations

1. Write a structured system prompt from scratch
2. Audit an existing prompt for anti-patterns
3. Refine a prompt based on agent evaluation results
4. Create few-shot examples for a prompt
5. Optimize prompt token count

Multi-Step Workflow

Step 1: Define the Prompt Structure

Every agent system prompt should follow this 6-part structure:

1. IDENTITY   -- Who the agent is
2. OBJECTIVE  -- What it must achieve
3. TOOLS      -- What it has available
4. RULES      -- Non-negotiable constraints
5. FORMAT     -- How to structure output
6. EXAMPLES   -- Concrete demonstrations

Step 2: Write the System Prompt

Use the template below, filling in each section with specific details.

SYSTEM_PROMPT = """
## Identity
You are {role}, specialized in {specialty}.

## Objective
Your mission is {primary_objective}. You work within Maestro,
a development governance platform.

## Available Tools
{list_of_tools_with_descriptions}

## Rules
1. Always follow the {bundle_name} bundle for code standards
2. Every commit must reference the task: {task_id}
3. Work only in the designated worktree: {worktree_path}
4. Report progress at every significant step
5. Request human review for destructive operations

## Response Format
- For code: use fenced code blocks with language specified
- For decisions: justify with "why"
- For errors: include context and suggested fix

## Example
Task: "Create endpoint GET /api/v1/demands"
Action: Create controller, use case, repository following Clean Architecture
Branch: feature/backend-{task_id}
"""