Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.
End-to-end smoke test skill for DeerFlow. Guides through: 1) Pulling latest code, 2) Docker OR Local installation and deployment (user preference, default to Local if Docker network issues), 3) Service availability verification, 4) Health check, 5) Final test report. Use when the user says "run smoke test", "smoke test deployment", "verify installation", "test service availability", "end-to-end test", or similar.
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.
Generate a personalized SOUL.md through a warm, adaptive onboarding conversation. Trigger when the user wants to create, set up, or initialize their AI partner's identity — e.g., "create my SOUL.md", "bootstrap my agent", "set up my AI partner", "define who you are", "let's do onboarding", "personalize this AI", "make you mine", or when a SOUL.md is missing. Also trigger for updates: "update my SOUL.md", "change my AI's personality", "tweak the soul".
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Use this skill when the user wants a systematic literature review, survey, or synthesis across multiple academic papers on a topic. Also covers annotated bibliographies and cross-paper comparisons. Searches arXiv and outputs reports in APA, IEEE, or BibTeX format. Not for single-paper tasks — use academic-paper-review for reviewing one paper.
| name | image-generation |
| description | Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation. |
This skill generates high-quality images using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing image generation with optional reference images.
When a user requests image generation, identify:
/mnt/user-dataGenerate a structured JSON file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}.json
Call the Python script:
python /mnt/skills/public/image-generation/scripts/generate.py \
--prompt-file /mnt/user-data/workspace/prompt-file.json \
--reference-images /path/to/ref1.jpg /path/to/ref2.png \
--output-file /mnt/user-data/outputs/generated-image.jpg
--aspect-ratio 16:9
Parameters:
--prompt-file: Absolute path to JSON prompt file (required)--reference-images: Absolute paths to reference images (optional, space-separated)--output-file: Absolute path to output image file (required)--aspect-ratio: Aspect ratio of the generated image (optional, default: 16:9)[!NOTE] Do NOT read the python file, just call it with the parameters.
User request: "Create a Tokyo street style woman character in 1990s"
Create prompt file: /mnt/user-data/workspace/asian-woman.json
{
"characters": [{
"gender": "female",
"age": "mid-20s",
"ethnicity": "Japanese",
"body_type": "slender, elegant",
"facial_features": "delicate features, expressive eyes, subtle makeup with emphasis on lips, long dark hair partially wet from rain",
"clothing": "stylish trench coat, designer handbag, high heels, contemporary Tokyo street fashion",
"accessories": "minimal jewelry, statement earrings, leather handbag",
"era": "1990s"
}],
"negative_prompt": "blurry face, deformed, low quality, overly sharp digital look, oversaturated colors, artificial lighting, studio setting, posed, selfie angle",
"style": "Leica M11 street photography aesthetic, film-like rendering, natural color palette with slight warmth, bokeh background blur, analog photography feel",
"composition": "medium shot, rule of thirds, subject slightly off-center, environmental context of Tokyo street visible, shallow depth of field isolating subject",
"lighting": "neon lights from signs and storefronts, wet pavement reflections, soft ambient city glow, natural street lighting, rim lighting from background neons",
"color_palette": "muted naturalistic tones, warm skin tones, cool blue and magenta neon accents, desaturated compared to digital photography, film grain texture"
}
Execute generation:
python /mnt/skills/public/image-generation/scripts/generate.py \
--prompt-file /mnt/user-data/workspace/cyberpunk-hacker.json \
--output-file /mnt/user-data/outputs/cyberpunk-hacker-01.jpg \
--aspect-ratio 2:3
With reference images:
{
"characters": [{
"gender": "based on [Image 1]",
"age": "based on [Image 1]",
"ethnicity": "human from [Image 1] adapted to Star Wars universe",
"body_type": "based on [Image 1]",
"facial_features": "matching [Image 1] with slight weathered look from space travel",
"clothing": "Star Wars style outfit - worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with holster",
"accessories": "blaster pistol on hip, comlink device on wrist, goggles pushed up on forehead, satchel with supplies, personal vehicle based on [Image 2]",
"era": "Star Wars universe, post-Empire era"
}],
"prompt": "Character inspired by [Image 1] standing next to a vehicle inspired by [Image 2] on a bustling alien planet street in Star Wars universe aesthetic. Character wearing worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with blaster holster. The vehicle adapted to Star Wars aesthetic with weathered metal panels, repulsor engines, desert dust covering, parked on the street. Exotic alien marketplace street with multi-level architecture, weathered metal structures, hanging market stalls with colorful awnings, alien species walking by as background characters. Twin suns casting warm golden light, atmospheric dust particles in air, moisture vaporators visible in distance. Gritty lived-in Star Wars aesthetic, practical effects look, film grain texture, cinematic composition.",
"negative_prompt": "clean futuristic look, sterile environment, overly CGI appearance, fantasy medieval elements, Earth architecture, modern city",
"style": "Star Wars original trilogy aesthetic, lived-in universe, practical effects inspired, cinematic film look, slightly desaturated with warm tones",
"composition": "medium wide shot, character in foreground with alien street extending into background, environmental storytelling, rule of thirds",
"lighting": "warm golden hour lighting from twin suns, rim lighting on character, atmospheric haze, practical light sources from market stalls",
"color_palette": "warm sandy tones, ochre and sienna, dusty blues, weathered metals, muted earth colors with pops of alien market colors",
"technical": {
"aspect_ratio": "9:16",
"quality": "high",
"detail_level": "highly detailed with film-like texture"
}
}
python /mnt/skills/public/image-generation/scripts/generate.py \
--prompt-file /mnt/user-data/workspace/star-wars-scene.json \
--reference-images /mnt/user-data/uploads/character-ref.jpg /mnt/user-data/uploads/vehicle-ref.jpg \
--output-file /mnt/user-data/outputs/star-wars-scene-01.jpg \
--aspect-ratio 16:9
Use different JSON schemas for different scenarios.
Character Design:
Scene Generation:
Product Visualization:
Read the following template file only when matching the user request.
After generation:
/mnt/user-data/outputs/For scenarios where visual accuracy is critical, use the image_search tool first to find reference images before generation.
Recommended scenarios for using image_search tool:
Example workflow:
image_search tool to find suitable reference images:
image_search(query="Japanese woman street photography 1990s", size="Large")
--reference-images parameter in the generation scriptThis approach significantly improves generation quality by providing the model with concrete visual guidance rather than relying solely on text descriptions.