Blender Web Pipeline skill provides workflows for exporting 3D models and animations from Blender to web-optimized formats (primarily glTF 2.0). It covers Python scripting for batch processing, optimization techniques for web performance, and integration with web 3D libraries like Three.js and Babylon.js.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionblender-web-pipelineExecute the skills CLI command in your project's root directory to begin installation:
Fetches blender-web-pipeline from freshtechbro/claudedesignskills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate blender-web-pipeline. Access via /blender-web-pipeline in your agent's command palette.
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.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Blender Web Pipeline skill provides workflows for exporting 3D models and animations from Blender to web-optimized formats (primarily glTF 2.0). It covers Python scripting for batch processing, optimization techniques for web performance, and integration with web 3D libraries like Three.js and Babylon.js.
When to use this skill:
Key capabilities:
Why glTF for Web:
glTF vs GLB:
.gltf = JSON + external .bin + external textures
.glb = Single binary file (recommended for web)
Access Blender data and operations via Python:
import bpy
# Access scene data
scene = bpy.context.scene
objects = bpy.data.objects
# Modify objects
obj = bpy.data.objects['Cube']
obj.location = (0, 0, 1)
obj.scale = (2, 2, 2)
# Export glTF
bpy.ops.export_scene.gltf(
filepath='/path/to/model.glb',
export_format='GLB'
)
Target Metrics:
# Blender Python Console or script
import bpy
# Select objects to export (optional - exports all if none selected)
bpy.ops.object.select_all(action='DESELECT')
bpy.data.objects['MyModel'].select_set(True)
# Export as GLB
bpy.ops.export_scene.gltf(
filepath='/path/to/output.glb',
export_format='GLB', # Binary format
use_selection=True, # Export selected only
export_apply=True, # Apply modifiers
export_texcoords=True, # UV coordinates
export_normals=True, # Normals
export_materials='EXPORT', # Export materials
export_colors=True, # Vertex colors
export_cameras=False, # Skip cameras
export_lights=False, # Skip lights
export_animations=True, # Include animations
export_draco_mesh_compression_enable=True, # Compress geometry
export_draco_mesh_compression_level=6, # 0-10 (6 recommended)
export_draco_position_quantization=14, # 8-14 bits
export_draco_normal_quantization=10, # 8-10 bits
export_draco_texcoord_quantization=12 # 8-12 bits
)
#!/usr/bin/env blender --background --python
"""
Batch export all .blend files in a directory to glTF
Usage: blender --background --python batch_export.py -- /path/to/blend/files
"""
import bpy
import os
import sys
# Get command line arguments after --
argv = sys.argv
argv = argv[argv.index("--") + 1:] if "--" in argv else []
input_dir = argv[0] if argv else "/path/to/models"
output_dir = argv[1] if len(argv) > 1 else input_dir + "_gltf"
# Create output directory
os.makedirs(output_dir, exist_ok=True)
# Find all .blend files
blend_files = [f for f in os.listdir(input_dir) if f.endswith('.blend')]
print(f"Found {len(blend_files)} .blend files")
for blend_file in blend_files:
input_path = os.path.join(input_dir, blend_file)
output_name = blend_file.replace('.blend', '.glb')
output_path = os.path.join(output_dir, output_name)
print(f"Processing: {blend_file}")
# Open blend file
bpy.ops.wm.open_mainfile(filepath=input_path)
# Export as GLB with optimizations
bpy.ops.export_scene.gltf(
filepath=output_path,
export_format='GLB',
export_apply=True,
export_draco_mesh_compression_enable=True,
export_draco_mesh_compression_level=6
)
print(f" Exported: {output_name}")
print("Batch export complete!")
Run batch script:
blender --background --python batch_export.py -- /models/source /models/output
import bpy
def optimize_mesh(obj, target_ratio=0.5):
"""Reduce polygon count using decimation modifier."""
if obj.type != 'MESH':
return
# Add Decimate modifier
decimate = obj.modifiers.new(name='Decimate', type='DECIMATE')
decimate.ratio = target_ratio # 0.5 = 50% of original polygons
decimate.use_collapse_triangulate = True
# Apply modifier
bpy.context.view_layer.objects.active = obj
bpy.ops.object.modifier_apply(modifier='Decimate')
print(f"Optimized {obj.name}: {len(obj.data.polygons)} polygons")
# Optimize all selected meshes
for obj in bpy.context.selected_objects:
optimize_mesh(obj, target_ratio=0.3)
import bpy
def bake_textures(obj, resolution=1024):
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›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
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.7★★★★★57 reviews- EEmma Sharma★★★★★Dec 28, 2024
blender-web-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.
- CChaitanya Patil★★★★★Dec 20, 2024
blender-web-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.
- JJames Huang★★★★★Dec 12, 2024
Useful defaults in blender-web-pipeline — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- HHassan Farah★★★★★Dec 8, 2024
I recommend blender-web-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- TTariq Desai★★★★★Nov 27, 2024
blender-web-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AArjun Jain★★★★★Nov 23, 2024
blender-web-pipeline is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- LLi Khanna★★★★★Nov 19, 2024
I recommend blender-web-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AAnika Bhatia★★★★★Nov 19, 2024
Keeps context tight: blender-web-pipeline is the kind of skill you can hand to a new teammate without a long onboarding doc.
- PPiyush G★★★★★Nov 11, 2024
I recommend blender-web-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AArjun Zhang★★★★★Nov 3, 2024
Registry listing for blender-web-pipeline matched our evaluation — installs cleanly and behaves as described in the markdown.
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