prompt-creator

Every prompt created should be clear, specific, and optimized for the target model.

<quick_start>

1. Clarify purpose: What should the prompt accomplish?
2. Identify model: Claude, GPT, or other (techniques vary slightly)
3. Select techniques: Choose from core techniques based on task complexity
4. Structure content: Use XML tags (Claude) or markdown (GPT) for organization
5. Add examples: Include few-shot examples for format-sensitive outputs
6. Define success: Add clear success criteria
7. Test and iterate: Refine based on outputs

<core_structure> Every effective prompt has:

<context>
Background information the model needs
</context>

<task>
Clear, specific instruction of what to do
</task>

<requirements>
- Specific constraints
- Output format
- Edge cases to handle
</requirements>

<examples>
Input/output pairs demonstrating expected behavior
</examples>

<success_criteria>
How to know the task was completed correctly
</success_criteria>

</core_structure> </quick_start>

<core_techniques> Priority: Always apply first

• State exactly what you want
• Avoid ambiguous language ("try to", "maybe", "generally")
• Use "Always..." or "Never..." instead of "Should probably..."
• Provide specific output format requirements

See: references/clarity-principles.md

Claude was trained with XML tags. Use them for:

• Separating sections: <context>, <task>, <output>
• Wrapping data: <document>, <schema>, <example>
• Defining boundaries: Clear start/end of sections

See: references/xml-structure.md

Provide 2-4 input/output pairs:

<examples>
<example number="1">
<input>User clicked signup button</input>
<output>track('signup_initiated', { source: 'homepage' })</output>
</example>
</examples>

See: references/few-shot-patterns.md

Add explicit reasoning instructions:

• "Think step by step before answering"
• "First analyze X, then consider Y, finally conclude Z"
• Use <thinking> tags for Claude's extended thinking

See: references/reasoning-techniques.md

System prompts set the foundation:

• Define Claude's role and expertise
• Set constraints and boundaries
• Establish output format expectations

See: references/system-prompt-patterns.md

Start Claude's response to guide format:

Assistant: {"result":

Forces JSON output without preamble.

For Claude 4.5 with context awareness:

• Inform about automatic context compaction
• Add state tracking (JSON, progress.txt, git)
• Use test-first patterns for complex implementations
• Enable autonomous task completion across context windows

See: references/context-management.md </core_techniques>

<prompt_creation_workflow> <step_0> Gather requirements using AskUserQuestion:

1. What is the prompt's purpose?

   • Generate content
   • Analyze/extract information
   • Transform data
   • Make decisions
   • Other

2. What model will use this prompt?

   • Claude (use XML tags)
   • GPT (use markdown structure)
   • Other/multiple

3. What complexity level?

   • Simple (single task, clear output)
   • Medium (multiple steps, some nuance)
   • Complex (reasoning, edge cases, validation)

4. Output format requirements?

   • Free text
   • JSON/structured data
   • Code
   • Specific template </step_0>

<step_1> Draft the prompt using this template:

<context>
[Background the model needs to understand the task]
</context>

<objective>
[Clear statement of what to accomplish]
</objective>

<instructions>
[Step-by-step process, numbered if sequential]
</instructions>

<constraints>
[Rules, limitations, things to avoid]
</constraints>

<output_format>
[Exact structure of expected output]
</output_format>

<examples>
[2-4 input/output pairs if format matters]
</examples>

<success_criteria>
[How to verify the task was done correctly]
</success_criteria>

</step_1>

<step_2> Apply relevant techniques based on complexity:

• Simple: Clear instructions + output format
• Medium: Add examples + constraints
• Complex: Add reasoning steps + edge cases + validation </step_2>

<step_3> Review checklist:

• Is the task clearly stated?
• Are ambiguous words removed?
• Is output format specified?
• Are edge cases addressed?
• Would a person with no context understand it? </step_3> </prompt_creation_workflow>

<anti_patterns> ❌ "Help with the data" ✅ "Extract email addresses from the CSV, remove duplicates, output as JSON array"

See: references/anti-patterns.md </anti_patterns>

<reference_guides> Core principles:

• references/clarity-principles.md - Being clear and direct
• references/xml-structure.md - Using XML tags effectively

Techniques:

• references/few-shot-patterns.md - Example-based prompting
• references/reasoning-techniques.md - Chain of thought, step-by-step
• references/system-prompt-patterns.md - System prompt templates
• references/context-management.md - Context windows, long-horizon reasoning, state tracking

Best practices by vendor:

• references/anthropic-best-practices.md - Claude-specific techniques
• references/openai-best-practices.md - GPT-specific techniques

Quality:

• references/anti-patterns.md - Common mistakes to avoid
• references/prompt-templates.md - Ready-to-use templates </reference_guides>

<success_criteria> A well-crafted prompt has:

• Clear, unambiguous objective
• Specific output format with example
• Relevant context provided
• Edge cases addressed
• No vague language (try, maybe, generally)
• Appropriate technique selection for task complexity
• Success criteria defined </success_criteria>