The Editorial AI Media & Prompt Template Library.

Create a new production component named {component_name} in {file_path}. Requirements: {requirements_list} Target Design Style: Dark glassmorphism with cyan accents.

Analyze the following complex reasoning puzzle / algorithm problem: {problem_statement} Apply step-by-step chain-of-thought analysis in <think> tags, then present the final verified solution in {output_language}.

Generate a hyper-realistic Flux 1.0 image prompt featuring {subject} in a {environment} setting. Camera Setup: - Camera: {camera_body} - Lens: {lens_focal_length} - Lighting: {lighting_style} - Color Grade: {color_grade}

Generate a production Docker execution script and GenAI-Perf benchmarking command for deploying {model_name} on {gpu_hardware} hardware. Optimization Target: {optimization_target}

Analyze the following article draft about {topic_name}. Generate: 1. A 2-sentence direct answer GEO blockquote for Perplexity AI. 2. A scannable markdown comparison table for {primary_entities}. 3. JSON-LD FAQ schema for 3 search intent questions. Article Text: {article_text}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

<prospect_profile> Company: {{company_name}} Role: {{prospect_role}} Pain Point: {{pain_point}} Product Offer: {{product_offer}} </prospect_profile>

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

<agent_specification> Agent Role: {{agent_role}} Domain & Tech Stack: {{tech_stack}} Operating Constraints: {{constraints}} </agent_specification>

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

<product_context> Product: {{product_name}} Core Value: {{value_prop}} Target Audience: {{audience}} </product_context>

AVAILABLE TOOLS: - `search_database(query: string)`: Searches internal DB. - `execute_python(script: string)`: Runs Python code. USER REQUEST: {{user_request}} If a tool call is required, respond ONLY with: { "tool": "tool_name", "arguments": { "arg_name": "value" } }

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

<task> Solve the following complex problem: {{problem_statement}} </task> <output_format> First, present step-by-step reasoning inside <scratchpad> tags. Second, present the final output inside <final_solution> tags. </output_format>

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

# Task Request Create a modular React component named {{component_name}} that handles {{primary_function}} with responsive Tailwind styling. # Context & API Requirements - API Endpoint: {{api_endpoint}} - State Management: {{state_management}}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

### RAW INPUT User Request: {{raw_user_prompt}} ### REFINEMENT PROCESS 1. Identify missing context, ambiguous terms, and missing formatting directives. 2. Formulate a crisp System Role and Instruction set. 3. Define dynamic input variables in {{VARIABLE}} syntax. 4. Add clear negative constraints and step-by-step reasoning commands.

<input_candidates> <candidate_a model="GPT-4o"> {{candidate_a_output}} </candidate_a> <candidate_b model="Claude-3.5-Sonnet"> {{candidate_b_output}} </candidate_b> <candidate_c model="DeepSeek-R1"> {{candidate_c_output}} </candidate_c> </input_candidates>

<json_schema> { "chart_type": "string", "data_series": [{ "label": "string", "value": "number" }], "trend_summary": "string" } </json_schema> <image_payload> {{uploaded_chart_image}} </image_payload>

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

<example_1> <input>John Doe is a 34 year old Software Engineer living in Austin, TX.</input> <output>{"name":"John Doe","age":34,"role":"Software Engineer","city":"Austin","state":"TX"}</output> </example_1> <live_task> <input>{{user_input_text}}</input> <output>

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

### MODULE INSTRUCTION: SECTION GENERATOR - Section Title: {{section_title}} - Key Points: {{key_points}} - Keywords: {{section_keywords}} - Target Word Count: {{word_count}} REQUIREMENTS: 1. Provide actionable instructions and concrete code/table examples. 2. Use a friendly, authoritative tone.

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

### INPUT PARAMETERS - Topic: {{topic}} - Primary Keyword: {{primary_keyword}} - Secondary Keywords: {{secondary_keywords}} - Target Audience: {{audience}} ### REQUIREMENTS 1. H1 title under 60 chars containing {{primary_keyword}}. 2. Meta description under 160 chars. 3. 4-6 H2 sections with 2-3 H3 sub-headings. 4. Dedicated Featured Snippet direct-answer box.

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

<user_request> Topic: {{article_topic}} Primary Keyword: {{primary_keyword}} Target Audience: {{audience}} </user_request>

<task> Create a modular prompt template for: {{target_task}} Target Audience: {{target_audience}} Output Format: {{desired_format}} </task>

Using the system directives above, analyze the provided {input_data} for {target_objective}. Produce a structured output adhering to {output_format}. Input Context: {input_data}

### INSTRUCTION Transform raw topic inputs into refined SEO Meta Titles and Descriptions. ### EXEMPLAR 1 Input: Python Web Scraping Output: Meta Title: Master Python Web Scraping: Step-by-Step Tutorial Meta Description: Learn how to scrape websites efficiently using Python, BeautifulSoup, and Requests. ### EXEMPLAR 2 Input: {{user_input}} Output:

Candid editorial portrait photograph of a {{age_and_gender}}, {{profession_or_action}}, set in {{location_and_environment}}. Shot on {{camera_model}}, {{lens_and_aperture}}, {{lighting_style}}, natural skin micro-texture, subtle film grain --ar 16:9 --style raw --v 6.1

<task> Build a {{component_or_feature}} for a {{project_domain}} application. </task> <specifications> - Require state management for: {{state_requirements}} - UI requirements: {{ui_specifications}} </specifications>

# INPUT PARAMETERS - Target Product: {{product_name}} - Value Proposition: {{core_value_prop}} - Primary Target Persona: {{target_persona}} - Key Benefit 1: {{benefit_1}} - Key Benefit 2: {{benefit_2}} # OUTPUT DELIVERABLES 1. 3 Hero Headline Options (Problem-Agitate-Solve framework) 2. Subheadline (Under 25 words) 3. 3 Feature Bullet Points with active verbs 4. Primary CTA button copy

<target_output_sample> {{target_output_text}} </target_output_sample> <instructions> 1. Identify the AI persona, tone, and domain expertise. 2. Extract all structural rules (e.g. Markdown headers, XML tags, bullet constraints). 3. Reconstruct a reusable meta-prompt template with {{variables}} for PromptOptima. </instructions>