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sharaf/agent-prompt-engineer

Write or audit AI agent system prompts component-by-component across identity, instruction architecture, behavioral constraints, tools, examples, context strategy, output format, and error handling. Use when the user wants to design a new agent prompt, write a system prompt, review an existing agent prompt, fix tool-use instructions, audit prompt structure, improve context strategy, tune output formats, or define error handling for single-agent or multi-agent systems.

100

1.33x
Quality

100%

Does it follow best practices?

Impact

100%

1.33x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

Overview
Quality
Evals
Security
Files

Evaluation results

100%

32%

Agent Prompt Design: Customer Support Triage Agent

Write-mode system prompt for a customer support agent

Criteria
Without context
With context

First Actions section

0%

100%

Design Decisions section

87%

100%

System Prompt section

100%

100%

Scope boundaries

100%

100%

Tool definitions

70%

100%

Escalation triggers

100%

100%

Tool failure handling

100%

100%

Context strategy

37%

100%

Output format

100%

100%

Critical rule sandwich

37%

100%

How to Iterate

12%

100%

Positive framing

66%

100%

100%

35%

Agent Prompt Audit: Research Assistant

Audit-mode review of an existing agent system prompt

Criteria
Without context
With context

Audit title and First Actions

40%

100%

Component Scores

10%

100%

Highest-impact fixes

100%

100%

Specific evidence

80%

100%

Concrete rewrite blocks

100%

100%

Tool definition diagnosis

25%

100%

Retrieval failure handling

100%

100%

Output format diagnosis

37%

100%

Constraint framing

87%

100%

Priority labels

100%

100%

Open Questions

0%

100%

No vague findings

100%

100%

100%

7%

Multi-Agent Prompt Design: Sales Intelligence Workflow

Multi-agent prompt architecture for a sales intelligence workflow

Criteria
Without context
With context

First Actions and mode

37%

100%

Agent-specific prompts

100%

100%

Role boundaries

100%

100%

Orchestrator constraints

100%

100%

Worker contracts

100%

100%

Verifier rubric

100%

100%

Typed handoff schema

100%

100%

Context isolation

87%

100%

Unsupported claim handling

100%

100%

Error handling

100%

100%

Implementation-ready output

100%

100%

How to Iterate

75%

100%

Evaluated
Agent
Claude
Model
Claude Sonnet 4.6

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