cross model prompt adapter
Standardize creative briefs into model-native prompts for Midjourney, DALL-E, and video generators.
$5
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentscross model prompt adapter
Example session with this skill installed
Adapt this brief for Midjourney v6 and DALL-E 3: A high-tech cyberpunk street in Tokyo at night, neon rain, a lonely cyborg cat with glowing blue eyes, 16:9 aspect ratio, cinematic lighting.
- Read your context and instructions
- Compiled the cross model prompt
I have processed the creative brief for a cyberpunk Tokyo scene into two model-specific prompt packages while locking the subject, atmosphere, and aspect ratio.
Cross-Model Prompt Adaptation
Source Brief
A nocturnal cyberpunk Tokyo street scene featuring a lonely cyborg cat with glowing blue eyes. The visual style requires neon-reflecting rain, cinematic lighting, and a 16:9 cinematic framing.
Invariant Ledger
| Requirement | Status | Locked interpretation |
|---|---|---|
| Subject | Locked | Cyborg cat with glowing blue eyes |
| Environment | Locked | Tokyo street, night, neon rain |
| Framing | Locked | 16:9 Aspect Ratio |
| Lighting | Locked | Cinematic, high-contrast neon |
Target Prompts
Midjourney — v6.0
Prompt
A lonely cyborg cat with glowing blue eyes, sitting on a cyberpunk Tokyo street at night, neon reflections in the rain, cinematic atmosphere, high-tech aesthetic, futuristic urban environment --ar 16:9 --v 6.0 --stylize 250
Native controls
--ar 16:9
--v 6.0
--stylize 250
Adaptation notes
Converted descriptive lighting into MJ-specific styling flags and appended version-appropriate aspect ratio syntax.
DALL-E 3 — OpenAI API
Prompt
A cinematic, wide-angle shot of a lonely cyborg cat with glowing blue eyes. The cat is situated on a high-tech cyberpunk street in Tokyo at night. The ground is wet with rain, reflecting vibrant neon signs from the surrounding buildings. The lighting is moody and dramatic, emphasizing the cat's mechanical features.
Native controls
size: 1792x1024
Adaptation notes
Expanded the brief into a dense, natural language descriptive paragraph as preferred by the DALL-E 3 transformer.
Validation Matrix
| Target | Semantic equivalence | Compatibility | Parameter contamination | Result |
|---|---|---|---|---|
| Midjourney v6 | Pass | Pass | Pass | Ready |
| DALL-E 3 | Pass | Pass | Pass | Ready |
Next steps
- Copy the Midjourney prompt into Discord or the alpha website.
- Use the DALL-E 3 prompt via the ChatGPT interface or API endpoint.
- Compare visual consistency regarding the cat's mechanical details.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Writing a creative brief once only to have it fail across different AI models is a major bottleneck. Manually translating syntax, aspect ratios, and model-specific parameters between Midjourney, DALL-E 3, and Stable Diffusion is error-prone and leads to inconsistent visual results.
What it does
- Extracts a creative brief into a "Semantic Invariant Ledger" to lock core subjects, lighting, and composition.
- Translates natural language descriptions into model-native prompt structures and syntax.
- Isolates parameters to prevent cross-model contamination like Midjourney flags appearing in DALL-E prompts.
- Validates semantic equivalence to ensure the core intent remains identical across all target generators.
- Identifies version-specific constraints and unsupported features instead of guessing.
Why this beats prompting it yourself
DIY prompting often leads to "prompt drift" where the creative vision changes slightly each time you adapt it for a new tool. This skill enforces strict semantic locking, ensuring that your subject, palette, and framing stay constant while the underlying technical implementation changes for every API or UI.
Use cases
- Generating consistent marketing assets across multiple image and video models.
- Migrating legacy prompt libraries to modern generator versions or different providers.
- A/B testing creative concepts across different model architectures using a single source of truth.
- Preparing production-ready prompts for automated pipelines using isolated parameter namespaces.
Known limitations
Does not execute the actual generation. It requires the user to specify target generators and versions for the most accurate syntax mapping.
Requires user-specified target generators and versions for syntax accuracy. Does not perform image generation or API calls; provides copy-ready prompts and config only.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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