Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sandraschi/blender-mcp --skill autonomous-modelinggit clone --depth 1 https://github.com/sandraschi/blender-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sandraschi/blender-mcp/autonomous-modeling)<a href="https://agentmods.dev/skills/sandraschi/blender-mcp/autonomous-modeling"><img src="https://agentmods.dev/badge/skills/sandraschi/blender-mcp/autonomous-modeling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sandraschi/blender-mcp/autonomous-modeling"><img src="https://agentmods.dev/badge/skills/sandraschi/blender-mcp/autonomous-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00708 |
| Opus 5.5 | $0.00014 | $0.00283 |
| Sonnet 5.5 | $0.00007 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
autonomous-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 16d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autonomous Modeling Skill
Description: Expert Blender 3D modeler for autonomous procedural generation, parametric modeling, topology optimization, and script-based creation. Covers multi-step modeling workflows, procedural generation pipelines, and geometry node-based asset creation.
Trigger Phrases
- "Model a [object] with [features]"
- "Generate a procedural [part/asset]"
- "Create a parametric [object] with adjustable parameters"
- "Design a [style] [object] with detailed [elements]"
- "Optimize topology on this mesh"
- "Generate a low-poly version of [object]"
- "Create a modular kit of [theme] parts"
Tools
agentic_blender_workflow(workflow_prompt="...")— Autonomous multi-step modeling via natural language. Plans and executes: create, modify, texture, validate.intelligent_3d_processing(prompt="...", context="...")— Refine existing models: add modifiers, adjust materials, fix geometry issues.blender_status()— Current scene state: object count, active object, render engine, viewport mode.blender_list_tools()— Available Blender MCP capabilities and their parameters.create_cube(),create_cylinder(),create_cone()— Add primitive mesh objects (one op per primitive).apply_modifier(modifier_type="...", ...)— Apply modifiers (subdivision, mirror, boolean, bevel, solidify).set_material(object_name="...", material="...", color=[...])— Assign or create materials.
Workflow
- Discovery: Call
blender_status()to assess current scene state. Useblender_list_tools()if unsure of available operations. - Planning: Use
agentic_blender_workflow(workflow_prompt="...")to generate a high-level modeling plan with step breakdown. - Execution: Create base geometry with
create_cube()/create_cylinder(), refine withapply_modifier(), texture withset_material(). - Refinement: If the initial model is too simple, use
intelligent_3d_processing(prompt="...")for detail pass — add edge loops, bevels, or extruded details. - Validation: Check for mesh integrity (non-manifold edges, zero-area faces, ngons) before finalization. Use Blender's 3D print toolbox if available.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 16d ago Changed da33d9a76916
- 1mo ago First seen · 48 lines · 36 tokens per session scan A 1b2a0ac78104
autonomous-modeling is a skill published in the GitHub repository sandraschi/blender-mcp (62 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 708 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
wireframe-to-3d
Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert…
fit-repair-optimizer
Turn multiview fit reports into a sequential or parallel repair queue for aligning Blender products to source-of-truth templates. Use after validation shows disalignment, when the agent must iteratively fix wireframe, texture, lighting, or projection mismatches, and when skill gaps should trigger self-refinement…
mascot-logo-reconstruction
Orchestrate a fail-gated, source-locked Blender reconstruction of mascots, logos, brand avatars, and stylized flat characters from wireframes, texture packs, and orthographic views. Use when the user requires a 1:1 brand/model match rather than a plausible stylized interpretation.
source-part-segmentation
Segment overlapping visual parts from source images, wireframes, texture atlases, and decals before mesh reconstruction. Use when a mascot/logo/template contains touching or overlapping components and exact structural part masks are needed before contour-to-mesh, UV fitting, or landmark repair.
skills
Generate a complete SVG file from a natural language description using SEP-1577 multi-step sampling.
atlas-uv-fitting
Detect texture-atlas regions and map each source part to its own UV rectangle or projected UV surface. Use when a texture pack must fit a Blender model 1:1, when textures look off/stretched, when alpha decals or lightmaps are provided, or before exporting a textured GLB for a brand mascot/logo.