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prompt-enhance

Enhance and improve existing AI prompts using professional techniques from a 2,500+ prompt database. Adds detail, fixes structure, optimizes for specific models, and applies proven patterns from top-performing prompts. Use when user says "enhance my prompt", "improve this prompt", "make this prompt better", "optimize prompt", "fix my prompt", or "upgrade this prompt".

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gabrielmoreira/agent-skills-mirror

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  • SKILL.md2.9 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Prompt Enhancer

Take any existing prompt and supercharge it using techniques from 2,500+ curated prompts.

Enhancement Workflow

Step 1: Analyze the Input Prompt

Evaluate the user's prompt on these dimensions:

  • Specificity (1-5): How detailed is the subject description?
  • Technical quality (1-5): Camera, lighting, composition details?
  • Style clarity (1-5): Is the aesthetic clearly defined?
  • Model optimization (1-5): Uses model-specific syntax correctly?
  • Length appropriateness (1-5): Right length for the target model?

Present a brief score card before enhancing.

Step 2: Find Similar Top Prompts

Search for high-quality reference prompts:

python3 {PROMPT_ENGINE_DIR}/scripts/search_prompts.py "KEY_TERMS" --limit 5

Step 3: Apply Enhancement Techniques

Choose from these enhancement strategies based on what's missing:

Detail Injection (for low specificity):

  • Add material textures ("brushed aluminum", "weathered leather")
  • Add environmental details ("dust particles in light", "morning dew")
  • Add character details ("freckled skin", "calloused hands")

Technical Elevation (for missing camera/lighting):

  • Add camera specs ("shot on Canon R5, 85mm f/1.2")
  • Add lighting ("golden hour backlighting", "Rembrandt lighting")
  • Add film stocks ("Kodak Portra 400 colors", "Fuji Velvia saturation")

Style Anchoring (for unclear aesthetic):

  • Add photographer/artist references ("in the style of Annie Leibovitz")
  • Add film/era references ("Y2K aesthetic", "1970s Kodachrome")
  • Add mood keywords ("moody", "ethereal", "gritty")

Negative Refinement (for models that support it):

  • Add negative prompts to exclude unwanted elements
  • Specify what NOT to include ("no text", "no watermark")

Structure Optimization (for poor organization):

  • Reorder elements: subject first, then environment, then style
  • Group related modifiers together
  • Remove redundant or conflicting terms

Step 4: Present Enhanced Version

Output format:

  1. Original prompt (for comparison)
  2. Enhanced prompt (the improved version)
  3. What changed (bullet list of specific improvements)
  4. Score improvement (before -> after on 5-point scale)
  5. Variations (2-3 alternative enhanced versions)

Enhancement Rules

  • Never remove the user's core intent/subject
  • Preserve the user's preferred style if stated
  • Keep enhancements relevant to the target model
  • Don't over-engineer: some prompts are intentionally minimal
  • Always explain WHY each change was made

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