Prompt Enhance
- 7 installs
- 100 repo stars
- Updated April 10, 2026
- agricidaniel/claude-prompts
prompt-enhance is a Claude Code skill that improves an existing AI generation prompt by scoring it and applying detail, technical, style, and structure enhancements.
About
prompt-enhance is a Claude Code skill that improves an existing AI generation prompt. It scores the input on specificity, technical quality, style, model optimization, and length, then applies targeted enhancements like camera specs, lighting, and style references. A developer uses it to upgrade a weak image, video, or text prompt without losing its original intent.
- Improves an existing AI generation prompt by scoring it on five dimensions and injecting detail
- Applies detail, technical, style-anchoring, and structure-optimization techniques from a 2,500+ prompt database
- Returns original vs enhanced prompt with a before/after score and 2-3 variations
Prompt Enhance by the numbers
- 7 all-time installs (skills.sh)
- Ranked #1,085 of 1,335 Generative Media skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
prompt-enhance capabilities & compatibility
- Capabilities
- prompt optimization · image generation · video generation
- Use cases
- image generation · video generation · copywriting
- Pricing
- Free
What prompt-enhance says it does
Enhance and improve existing AI prompts using professional techniques from a 2,500+ prompt database.
Take any existing prompt and supercharge it using techniques from 2,500+ curated prompts.
npx skills add https://github.com/agricidaniel/claude-prompts --skill prompt-enhanceAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 7 |
|---|---|
| repo stars | ★ 100 |
| Last updated | April 10, 2026 |
| Repository | agricidaniel/claude-prompts ↗ |
What it does
Improve an existing AI generation prompt by adding detail, technical specs, and structure while preserving intent.
Who is it for?
Upgrading a rough or minimal generation prompt into a detailed, model-optimized one
Skip if: Terminal, CLI, or code prompts
When should I use this skill?
you say enhance my prompt, improve this prompt, optimize prompt, or make this prompt better
What you get
An enhanced prompt with a before/after score, a change log, and variations.
- An enhanced prompt
- A before/after 5-point score
- 2-3 alternative enhanced versions
By the numbers
- 5 scoring dimensions
- 2,500+ prompt reference database
- 4-step enhancement workflow
Files
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 5Step 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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How does prompt-enhance decide what to change?
It scores the input on five dimensions and applies the enhancement strategies that address the lowest-scoring areas.
Will it change my subject?
No. It never removes the user's core intent or subject and preserves a stated preferred style.