AI Image Prompt Master: Translating Fuzzy Needs into Visual Specs

Where this fits

Alice, a DTC brand manager, needs a set of hero images for a new lipstick launch. She knows the product, the audience, and the tone, but every prompt she types into an image model returns generic or unusable results. This skill sits at that friction point: it takes her raw inputs and converts them into explicit visual variables—composition, lighting, materials, text placement, aspect ratio—while also producing negative constraints and reference-image guidance. For developers, it's a drop-in module that turns a user's half-formed ask into a structured spec that can be handed to an image API.

Why agents benefit

• Structurally parses ambiguous requirements (e.g., "premium feel") into discrete, checkable attributes like lighting type, camera angle, and material finish.
• Generates bilingual prompts (English/Chinese) with negative constraints, reducing trial-and-error loops and wasted API calls.
• Recommends specific generation models based on the use case (e-commerce, short drama, social) and cost/quality tradeoffs.
• Integrates directly with AI-HIVE's API to upload reference images, snapshot pricing, route (COST_FIRST/SPEED_FIRST/SUCCESS_FIRST), poll async jobs, and download results—all within the agent's context.
• Includes an approach for breaking down a reference case into an original prompt, helping agents adapt existing creative without copying.

Practical scenarios

E-commerce product shot generation
A merchant wants a clean, lifestyle image for a water bottle. The skill captures the product's material, desired background, lighting, and text overlay rules, then outputs a prompt plus negative constraints (no distorted reflections, no extra objects) and a model suggestion optimized for product photography.

Short-drama poster design
For a short-drama cover, the agent needs to convey dramatic emotion with specific characters and a vertical 9:16 ratio. The skill structures the scene, expression, color grading, and text placement, then uses the reference-image strategy to maintain character consistency across frames.

Social media feed for a beauty brand
A brand wants a series of "clean" skincare images with a consistent aesthetic. The skill turns the brief into a batch of prompts with shared style tokens, negative constraints for unrealistic skin, and a queue of generation tasks via the API, all while respecting the platform's content rules.

Add it to your agent workflow

Install with:

npx skills add wubin1836/ai-hive-agent-skills --skill ai-image-prompt-master-ai-hive

Example usage: the user says, "I need a hero image for a green tea pack, on a wooden table, warm morning light, text 'Pure Leaf', 1:1 ratio." The skill returns a structured prompt, negative prompt, model recommendation, and then—if configured—calls the AI-HIVE API to generate and poll for results.

Compare before adopting

Look at the skill's quality signals: does it actually reduce prompt iterations? Check the repo's star count (currently 2) and commit freshness. Compare with other prompt-engineering skills that only output text but don't integrate with an API. Consider whether you need the API integration or just a prompt builder. Also test it on your typical input format—does it ask the right clarifying questions without a long questionnaire?

Why it is worth tracking

The skill addresses a common pain point: turning fuzzy creative briefs into machine-readable specifications. Its integration with AI-HIVE adds practical speed for agents that need to hand off to an image API. The community is small but active—monitor the repo for updates and user feedback. Evaluate it when your agent handles image generation requests that require more than a one-line prompt, especially for commerce or content production workflows. For a deeper look, see the listing on OpenAgentSkill.