Fetches blender-mcp from vladmdgolam/agent-skills 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?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/blender-mcp
Restart Cursor to activate blender-mcp. Access via /blender-mcp 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.
Use structured MCP tools (get_scene_info, screenshot) for quick inspection.
Use execute_python for anything non-trivial: hierarchy traversal, material extraction, animation baking, bulk operations. It gives full bpy API access and avoids tool schema limitations.
Use headless CLI for GLTF exports — the MCP server times out on export operations.
Health Check (Always First)
get_scene_info — verify connection (default port 9876)
execute_python with print("ok") — verify Python works
screenshot — verify viewport capture works
If MCP is unresponsive, check that the Blender MCP addon is enabled and the socket server is running.
Complete Export Workflow
This is the end-to-end linear narrative. Follow these steps in order. Do not skip steps.
Step 1: Health Check
Confirm MCP is alive before touching anything else:
# In MCP tool call:get_scene_info
execute_python: print("ok")screenshot
If any step fails, stop and fix MCP connectivity first. See Known Errors.
Step 2: Inspect Scene
Run the full hierarchy extraction to understand what you're working with:
import bpy, json
defextract_hierarchy(obj, depth=0): data ={"name": obj.name,"type": obj.type,"location":list(obj.location),"rotation":list(obj.rotation_euler),"scale":list(obj.scale),"visible":not obj.hide_viewport,"children":[],}if obj.type=='MESH'and obj.data: data["vertices"]=len(obj.data.vertices) data["faces"]=len(obj.data.polygons) data["materials"]=[slot.material.name for slot in obj.material_slots if slot.material]if obj.type=='LIGHT': data["light_type"]= obj.data.type data["energy"]= obj.data.energy
data["color"]=list(obj.data.color)for mod in obj.modifiers:if mod.type=='ARRAY': data.setdefault("modifiers",[]).append({"type":"ARRAY","count": mod.count,"offset_object": mod.offset_object.name if mod.offset_object elseNone,})for child in obj.children: data["children"].append(extract_hierarchy(child, depth +1))return data
scene_data ={"name": bpy.context.scene.name,"fps": bpy.context.scene.render.fps,"frame_start": bpy.context.scene.frame_start,"frame_end": bpy.context.scene.frame_end,"objects":[],}for obj in bpy.context.scene.objects:if obj.parent isNone: scene_data["objects"].append(extract_hierarchy(obj))print(json.dumps(scene_data, indent=2))
Look for:
Array modifiers (will balloon file size if baked — must replicate at runtime)
Objects with many vertices (risk of slow export or large GLB)
Hidden objects you may or may not want to export
Missing materials (empty material_slots)
Step 3: Verify Materials
Run the material extraction to catch export-lossy setups before committing to an export:
import bpy, json
defextract_materials(): materials =[]for mat in bpy.data.materials:ifnot mat.use_nodes:continue info ={"name": mat.name,"nodes":[],"warnings":[]} has_principled =Falsefor node in mat.node_tree.nodes: node_data ={"type": node.type,"name": node.name}if node.type=='BSDF_PRINCIPLED': has_principled =Truefor inp in node.inputs:if inp.is_linked: node_data[inp.name]="linked"elifhasattr(inp,'default_value'): val = inp.default_value
try: node_data[inp.name]=list(val)except TypeError: node_data[inp.name]=float(val)if node.type=='TEX_IMAGE'and node.image: node_data["image"]= node.image.filepath
node_data["size"]=[node.image.size[0], node.image.size[1]]if node.image.size[0]>2048: info["warnings"].append(f"Large texture: {node.image.filepath} ({node.image.size[0]}x{node.image.size[1]})")if node.typein('TEX_NOISE','TEX_VORONOI','TEX_WAVE','TEX_MUSGRAVE'): info["warnings"].append(f"Procedural texture node '{node.name}' ({node.
✓
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
