prompt-creator

<objective> Create highly effective prompts using proven techniques from Anthropic and OpenAI research. This skill covers all major prompting methodologies: clarity, structure, examples, reasoning, and advanced patterns.

Every prompt created should be clear, specific, and optimized for the target model. </objective>

<quick_start> <workflow>

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

<core_structure> Every effective prompt has:

xml

<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> <technique name="be_clear_and_direct"> 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 </technique>

<technique name="use_xml_tags"> **When**: Claude prompts, complex structure needed

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 </technique>

<technique name="few_shot_examples"> **When**: Output format matters, pattern recognition easier than rules

Provide 2-4 input/output pairs:

xml

<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 </technique>

<technique name="chain_of_thought"> **When**: Complex reasoning, math, multi-step analysis

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 </technique>

<technique name="system_prompts"> **When**: Setting persistent behavior, role, constraints

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 </technique>

<technique name="prefilling"> **When**: Enforcing specific output format (Claude-specific)

Start Claude's response to guide format:

Assistant: {"result":

Forces JSON output without preamble. </technique>

<technique name="context_management"> **When**: Long-running tasks, multi-session work, large context usage

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 </technique> </core_techniques>

<prompt_creation_workflow> <step_0> Gather requirements using AskUserQuestion:

What is the prompt's purpose?
- Generate content
- Analyze/extract information
- Transform data
- Make decisions
- Other
What model will use this prompt?
- Claude (use XML tags)
- GPT (use markdown structure)
- Other/multiple
What complexity level?
- Simple (single task, clear output)
- Medium (multiple steps, some nuance)
- Complex (reasoning, edge cases, validation)
Output format requirements?
- Free text
- JSON/structured data
- Code
- Specific template </step_0>

<step_1> Draft the prompt using this template:

xml

<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> <pitfall name="vague_instructions"> ❌ "Help with the data" ✅ "Extract email addresses from the CSV, remove duplicates, output as JSON array" </pitfall>

<pitfall name="negative_prompting"> ❌ "Don't use technical jargon" ✅ "Write in plain language suitable for a non-technical audience" </pitfall> <pitfall name="no_examples"> ❌ Describing format in words only ✅ Showing 2-3 concrete input/output examples </pitfall> <pitfall name="missing_edge_cases"> ❌ "Process the file" ✅ "Process the file. If empty, return []. If malformed, return error with line number." </pitfall>

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>

prompt-creator

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