Individual skill
prompt-engineering
ComfyUI prompt engineering knowledge covering CLIP text encoding syntax, weight modifiers, model-specific prompting strategies, and best practices
plugin/skills/prompt-engineering/SKILL.md
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- agent-skills Repository topic
- ai-agent Repository topic
- claude-code Repository topic
- claude-plugin Repository topic
- comfyui Repository topic
- comfyui-extension Repository topic
- flux Repository topic
- image-generation Repository topic
- mcp Repository topic
- mcp-server Repository topic
- engineering Derived category
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# ComfyUI Prompt Engineering
## CLIP Text Encoding Fundamentals
ComfyUI uses CLIP (Contrastive Language-Image Pre-training) text encoders to convert text prompts into conditioning tensors. The `CLIPTextEncode` node takes a text string and a CLIP model, producing a `CONDITIONING` output for the KSampler.
### Token Limit
CLIP processes text in **77-token chunks**. Each word is typically 1-3 tokens. Prompts exceeding 77 tokens are silently truncated unless you use the BREAK token or a multi-clip encoding node.
## Weight Syntax
### Emphasis (Attention Weights)
