Fetches stable-diffusion-image-generation from davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
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│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
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│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/stable-diffusion-image-generation
Restart Cursor to activate stable-diffusion-image-generation. Access via /stable-diffusion-image-generation in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Text Prompt → Text Encoder → Text Embeddings
↓
Random Noise → [Denoising Loop] ← Scheduler
↓
Predicted Noise
↓
VAE Decoder → Final Image
Core concepts
Pipelines
Pipelines orchestrate complete workflows:
Pipeline
Purpose
StableDiffusionPipeline
Text-to-image (SD 1.x/2.x)
StableDiffusionXLPipeline
Text-to-image (SDXL)
StableDiffusion3Pipeline
Text-to-image (SD 3.0)
FluxPipeline
Text-to-image (Flux models)
StableDiffusionImg2ImgPipeline
Image-to-image
StableDiffusionInpaintPipeline
Inpainting
Schedulers
Schedulers control the denoising process:
Scheduler
Steps
Quality
Use Case
EulerDiscreteScheduler
20-50
Good
Default choice
EulerAncestralDiscreteScheduler
20-50
Good
More variation
DPMSolverMultistepScheduler
15-25
Excellent
Fast, high quality
DDIMScheduler
50-100
Good
Deterministic
LCMScheduler
4-8
Good
Very fast
UniPCMultistepScheduler
15-25
Excellent
Fast convergence
Swapping schedulers
from diffusers import DPMSolverMultistepScheduler
# Swap for faster generationpipe.scheduler = DPMSolverMultistepScheduler.from_config( pipe.scheduler.config
)# Now generate with fewer stepsimage = pipe(prompt, num_inference_steps=20).images[0]
Generation parameters
Key parameters
Parameter
Default
Description
prompt
Required
Text description of desired image
negative_prompt
None
What to avoid in the image
num_inference_steps
50
Denoising steps (more = better quality)
guidance_scale
7.5
Prompt adherence (7-12 typical)
height, width
512/1024
Output dimensions (multiples of 8)
generator
None
Torch generator for reproducibility
num_images_per_prompt
1
Batch size
Reproducible generation
import torch
generator = torch.Generator(device="cuda").manual_seed(42)image = pipe( prompt="A cat wearing a top hat", generator=generator, num_inference_steps=50).images[0]
Negative prompts
image = pipe( prompt="Professional photo of a dog in a garden", negative_prompt="blurry, low quality, distorted, ugly, bad anatomy", guidance_scale=7.5).images[0]
Image-to-image
Transform existing images with text guidance:
from diffusers import AutoPipelineForImage2Image
from PIL import Image
pipe = AutoPipelineForImage2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")init_image = Image.open("input.jpg").resize((512,512))image = pipe( prompt="A watercolor painting of the scene", image=init_image, strength=0.75,# How much to transform (0-1) num_inference_steps=50).images[0]
Inpainting
Fill masked regions:
from diffusers import AutoPipelineForInpainting
from PIL import Image
pipe = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
).to("cuda")image = Image.open("photo.jpg")mask = Image.open("mask.png")# White = inpaint regionresult = pipe( prompt="A red car parked on the street", image=image, mask_image=mask, num_inference_steps=50).images[0]
ControlNet
Add spatial conditioning for precise control:
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
# Load ControlNet for edge conditioningcontrolnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16
)pipe = StableDiffusionControlNetPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")# Use Canny edge image as controlcontrol_image = get_canny_image(input_image)image = pipe( prompt="A beautiful house in the style of Van Gogh", image=control_image, num_inference_steps=30).images[0]
Available ControlNets
ControlNet
Input Type
Use Case
canny
Edge maps
Preserve structure
openpose
Pose skeletons
Human poses
depth
Depth maps
3D-aware generation
normal
Normal maps
Surface details
mlsd
Line segments
Architectural lines
scribble
Rough sketches
Sketch-to
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
