Use structured MCP tools (get_scene_info, screenshot) for quick inspection.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionblender-mcpExecute the skills CLI command in your project's root directory to begin installation:
Fetches blender-mcp from vladmdgolam/agent-skills 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-mcp. Access via /blender-mcp 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.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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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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.
get_scene_info — verify connection (default port 9876)execute_python with print("ok") — verify Python worksscreenshot — verify viewport capture worksIf MCP is unresponsive, check that the Blender MCP addon is enabled and the socket server is running.
This is the end-to-end linear narrative. Follow these steps in order. Do not skip steps.
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.
Run the full hierarchy extraction to understand what you're working with:
import bpy, json
def extract_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 else None,
})
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 is None:
scene_data["objects"].append(extract_hierarchy(obj))
print(json.dumps(scene_data, indent=2))
Look for:
material_slots)Run the material extraction to catch export-lossy setups before committing to an export:
import bpy, json
def extract_materials():
materials = []
for mat in bpy.data.materials:
if not mat.use_nodes:
continue
info = {"name": mat.name, "nodes": [], "warnings": []}
has_principled = False
for node in mat.node_tree.nodes:
node_data = {"type": node.type, "name": node.name}
if node.type == 'BSDF_PRINCIPLED':
has_principled = True
for inp in node.inputs:
if inp.is_linked:
node_data[inp.name] = "linked"
elif hasattr(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.type in ('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
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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4.6★★★★★34 reviews- NNeel Jain★★★★★Dec 24, 2024
blender-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLuis Thomas★★★★★Nov 15, 2024
blender-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- RRahul Santra★★★★★Nov 3, 2024
blender-mcp is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- PPratham Ware★★★★★Oct 22, 2024
Keeps context tight: blender-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
- OOmar Zhang★★★★★Oct 6, 2024
We added blender-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMia Reddy★★★★★Oct 2, 2024
Useful defaults in blender-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- YYash Thakker★★★★★Sep 17, 2024
blender-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
- YYuki Ramirez★★★★★Sep 9, 2024
blender-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
- NNaina Thomas★★★★★Aug 28, 2024
Solid pick for teams standardizing on skills: blender-mcp is focused, and the summary matches what you get after install.
- DDhruvi Jain★★★★★Aug 8, 2024
Solid pick for teams standardizing on skills: blender-mcp is focused, and the summary matches what you get after install.
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