Automated summary
Adjusts photo brightness, contrast, and saturation based on natural language prompts, using GPT-5 to interpret user intent.
Strengths
- Flexible natural language interpretation
- Predefined styles (vivid, natural, dramatic) for consistent results
- Never overwrites original files
- Error handling with safe default values
Limitations
- Requires OpenRouter API key and GPT-5 availability
- Results depend on photo analysis quality
- Limited to JPG/PNG formats and multipliers between 0.5 and 2.0
Use this skill to quickly enhance photos with simple instructions, without needing a graphical editor.
Avoid it for precise pixel-level edits or unsupported file formats.
Security analysis
SafeThe skill only processes images locally using Python, calls an external text API for suggestions, and applies safe color adjustments. It never overwrites original files and validates multipliers within a safe range. No destructive or exfiltrating actions are present.
No concerns found
How this analysis is madeExamples
Make the colors much more vibrant and vividCreate a dramatic cinematic look with high contrastFix the washed out appearance and add warmthname: photo-color description: Adjust photo brightness, contrast, and saturation based on natural language prompts using GPT-5 text analysis. Use when the user asks to adjust brightness, change contrast, modify saturation, apply a specific style, or fix photo issues like washed out colors.
Photo Color
When to Use
Use this skill when the user asks to:
- Adjust brightness (e.g., "make it brighter", "darken the photo")
- Change contrast (e.g., "increase contrast", "make it more dramatic")
- Modify saturation (e.g., "make colors more vibrant", "desaturate")
- Apply a specific style (e.g., "cinematic look", "natural enhancement")
- Fix photo issues (e.g., "fix washed out colors", "add warmth")
Instructions
This skill uses GPT-5 text API to interpret natural language and calculate color adjustments. Always:
- Validate input - Ensure photo exists and is JPG/PNG format
- Analyze photo - Calculate current brightness, contrast, saturation scores
- Call GPT-5 - Get enhancement suggestions based on user's prompt and photo analysis
- Select best match - Choose suggestion that best matches prompt keywords (vivid/natural/dramatic)
- Apply enhancements - Adjust colors and save to new file (never overwrite original)
- Report results - Show multipliers applied and GPT-5's reasoning
Usage
from photo_color import main
result = main(
photo_path="photo.jpg",
prompt="Make the colors more vibrant and warm"
)
# Returns: "photo-enhanced.jpg"
Command Line
python photo_color.py <photo_path> "<prompt>" [output_path]
Examples
Vivid colors:
Make the colors much more vibrant and vivid
Increase saturation by 50%
Make colors pop
Natural enhancement:
Apply a subtle natural enhancement
Keep it looking natural and soft
Cinematic/dramatic:
Create a dramatic cinematic look with high contrast
Make it bold and intense
Fix issues:
Fix the washed out appearance
Increase saturation to make colors more realistic
Add warmth to the photo
Specific adjustments:
Brighten and increase contrast
Make it darker and more moody
Output
- Returns path to enhanced photo
- Never overwrites original
- Format:
{original_name}-enhanced.{ext} - Multipliers in safe range (0.5-2.0)
- Displays style name and GPT-5's reasoning
Enhancement Styles
GPT-5 suggests these style categories:
- Vivid: High saturation (1.3-1.5), increased brightness and contrast
- Natural: Subtle adjustments (1.05-1.2), minimal changes
- Dramatic: High contrast (1.5-1.8), bold adjustments
- Custom: Tailored to specific prompt requirements
Error Handling
- Falls back to safe defaults (×1.2 saturation, ×1.1 brightness, ×1.1 contrast) if GPT-5 unavailable
- Validates multipliers (0.5-2.0 range)
- Clear error messages for missing files or invalid formats
Requirements
- OPENROUTER_API_KEY environment variable must be set
- Photo must be JPG or PNG format
- Photo under 50MB recommended
Technical Implementation
See references/implementation.md for:
- GPT-5 text API integration
- Photo analysis algorithms
- Color enhancement styles
- Prompt matching strategy
Examples
See examples/common-use-cases.md for:
- Vivid color enhancement
- Natural subtle adjustment
- Dramatic cinematic look
- Fix washed out photos
- Add warmth
- Before/after comparisons
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/photo-color-adjustment-mmbizkpp' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/photo-color-adjustment-mmbizkpp/download' --output '.skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md.part' && mv '.skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md.part' '.skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Original excerpt: Requirements
- OPENROUTER_API_KEY environment variable must be set
- Photo must be JPG or PNG format
- Photo under 50MB recommended
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md' && test -s '.skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/photo-color' && cp -R '.skill-downloads/photo-color-adjustment-mmbizkpp' '.claude/skills/photo-color'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/photo-color-adjustment-mmbizkpp'openskills read 'photo-color-adjustment-mmbizkpp'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/photo-color-adjustment-mmbizkpp/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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