blender-web-pipeline

freshtechbro/claudedesignskills · updated Apr 8, 2026

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$npx skills add https://github.com/freshtechbro/claudedesignskills --skill blender-web-pipeline
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summary

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.

skill.md

Blender Web Pipeline

Overview

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:

  • Exporting Blender models for web applications
  • Batch processing multiple 3D assets
  • Optimizing file sizes for web delivery
  • Automating repetitive Blender tasks
  • Creating production pipelines for 3D web content
  • Converting legacy formats to glTF

Key capabilities:

  • glTF 2.0 export with optimization
  • Python (bpy) automation scripts
  • Texture baking and compression
  • LOD (Level of Detail) generation
  • Batch processing workflows
  • Material and lighting optimization for web

Core Concepts

glTF 2.0 Format

Why glTF for Web:

  • Industry-standard 3D format for web
  • Efficient binary encoding (.glb)
  • PBR materials support
  • Animation and skinning
  • Extensible with custom data
  • Wide library support (Three.js, Babylon.js, etc.)

glTF vs GLB:

.gltf = JSON + external .bin + external textures
.glb  = Single binary file (recommended for web)

Blender Python API (bpy)

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'
)

Web Optimization Goals

Target Metrics:

  • File size: <5 MB per model (ideal <1 MB)
  • Polygon count: <50k triangles for real-time
  • Texture resolution: 2048x2048 max (1024x1024 preferred)
  • Draw calls: Minimize via texture atlases
  • Load time: <2 seconds on average connection

Common Patterns

1. Basic glTF Export (Manual)

# 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
)

2. Python Script for Batch Export

#!/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

3. Optimize Model for Web (Decimation)

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)

4. Texture Baking for Web

import bpy

def bake_textures(obj, resolution=1024):
    
how to use blender-web-pipeline

How to use blender-web-pipeline on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add blender-web-pipeline
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/freshtechbro/claudedesignskills --skill blender-web-pipeline

The skills CLI fetches blender-web-pipeline from GitHub repository freshtechbro/claudedesignskills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/blender-web-pipeline

Reload or restart Cursor to activate blender-web-pipeline. Access the skill through slash commands (e.g., /blender-web-pipeline) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

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

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.757 reviews
  • Emma Sharma· Dec 28, 2024

    blender-web-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chaitanya Patil· Dec 20, 2024

    blender-web-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • James 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.

  • Hassan Farah· Dec 8, 2024

    I recommend blender-web-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Tariq Desai· Nov 27, 2024

    blender-web-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Arjun Jain· Nov 23, 2024

    blender-web-pipeline is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Li Khanna· Nov 19, 2024

    I recommend blender-web-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Anika 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.

  • Piyush G· Nov 11, 2024

    I recommend blender-web-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Arjun 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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