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 sideshowroberto/vfx-agent-toolkit --skill blender-physics-simulationgit clone --depth 1 https://github.com/sideshowroberto/vfx-agent-toolkitWrote 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/sideshowroberto/vfx-agent-toolkit/blender-physics-simulation)<a href="https://agentmods.dev/skills/sideshowroberto/vfx-agent-toolkit/blender-physics-simulation"><img src="https://agentmods.dev/badge/skills/sideshowroberto/vfx-agent-toolkit/blender-physics-simulation/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/sideshowroberto/vfx-agent-toolkit/blender-physics-simulation"><img src="https://agentmods.dev/badge/skills/sideshowroberto/vfx-agent-toolkit/blender-physics-simulation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00051 | $0.02681 |
| Opus 5.5 | $0.00020 | $0.01072 |
| Sonnet 5.5 | $0.00010 | $0.00536 |
| Haiku 4.5 | $0.00005 | $0.00268 |
Grade A, and why
blender-physics-simulation scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Removed HTTP Bridge section and curl health-check How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blender Physics Simulation Skill
Version: 2.0.1 Last Updated: 2026-06-10 Dependencies: Blender 5.1+
API Stability (5.1+)
Physics system is stable across 4.x -> 5.1. All core APIs work via direct data access:
import bpy
# Particle system - direct API
psys = obj.modifiers.new("ParticleSystem", 'PARTICLE_SYSTEM')
settings = obj.particle_systems[-1].settings
# Rigid body - direct property access
obj.rigid_body.type = 'ACTIVE'
obj.rigid_body.mass = 1.0
# Cloth - direct modifier settings
cloth_mod = obj.modifiers.new("Cloth", 'CLOTH')
cloth_mod.settings.mass = 0.3
Note on fluid baking: Mantaflow baking cannot be triggered from Python - configure all settings via script, then bake in the Blender UI (Physics Properties > Fluid > Bake All).
QUICK START
Basic Particle System
import bpy
# Create emitter plane
vertices = [(-1,-1,0), (1,-1,0), (1,1,0), (-1,1,0)]
faces = [[0,1,2,3]]
mesh = bpy.data.meshes.new("Emitter")
mesh.from_pydata(vertices, [], faces)
mesh.update()
emitter = bpy.data.objects.new("ParticleEmitter", mesh)
bpy.context.scene.collection.objects.link(emitter)
# Add particle system
psys_mod = emitter.modifiers.new("ParticleSystem", 'PARTICLE_SYSTEM')
settings = emitter.particle_systems[-1].settings
# Configure
settings.count = 1000
settings.frame_start = 1
settings.frame_end = 250
settings.lifetime = 50
settings.physics_type = 'NEWTON'
settings.normal_factor = 1.0
settings.factor_random = 0.5
print(f"Particle system created: {settings.count} particles")
STANDARD WORKFLOWS
Workflow 1: Rain / Snow Particles
Use When: Precipitation effects, environmental particles
import bpy
# Elevated emitter for downward rain
vertices = [(-10,-10,5), (10,-10,5), (10,10,5), (-10,10,5)]
faces = [[0,1,2,3]]
mesh = bpy.data.meshes.new("RainEmitter")
mesh.from_pydata(vertices, [], faces)
mesh.update()
emitter = bpy.data.objects.new("RainEmitter", mesh)
bpy.context.scene.collection.objects.link(emitter)
psys_mod = emitter.modifiers.new("Rain", 'PARTICLE_SYSTEM')
settings = emitter.particle_systems[-1].settings
settings.count = 5000
settings.physics_type = 'NEWTON'
settings.effector_weights.gravity = 1.0
settings.normal_factor = -2.0 # Downward velocity
settings.particle_size = 0.01
# Collision ground
ground_mesh = bpy.data.meshes.new("Ground")
ground_mesh.from_pydata([(-10,-10,0),(10,-10,0),(10,10,0),(-10,10,0)], [], [[0,1,2,3]])
ground_mesh.update()
ground = bpy.data.objects.new("Ground", ground_mesh)
bpy.context.scene.collection.objects.link(ground)
ground.collision.use = True
ground.collision.damping = 0.5
print("Rain system configured")
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 351 lines · 51 tokens per session scan A ef23323959d6
blender-physics-simulation is a skill published in the GitHub repository sideshowroberto/vfx-agent-toolkit (16 stars, last pushed 22d ago), licensed MIT. It adds 51 tokens to every session and 2,681 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-02.
Other skills, from other repositories
dcc-cua
A routing guide for controlling application interfaces through the project’s DCC-CUA system. DCC applications are digital-content tools such as Maya, Blender, and Houdini.
mesh-ops
Cross-adapter polygon modeling recipe pack for the shared cross-DCC modeling vocabulary. Structured RECIPES.yaml entries for primitive creation, hard-surface edits, topology cleanup, UV generation, radial arraying, and material assignment. Reusable by every DCC adapter's mesh-ops skill (maya, blender, houdini, 3dsmax…
marketplace-publish-extension
Infrastructure skill — publish (register/update) an extension package to a marketplace catalog. Reads the extension's SKILL.md frontmatter, constructs a CatalogEntry, and upserts it into the target marketplace.json. Optionally commits and pushes when the catalog source is a git repository. Use after scaffolding an…
marketplace-create-extension
Infrastructure skill — scaffold a new marketplace extension package (SKILL.md + tools.yaml + scripts/) with MIT-0 licensing. Use when creating a publishable marketplace entry for any DCC host. Not for editing existing extensions or driving live DCC scenes — use domain skills for that.
clawhub-compat
Example skill — demonstrates full compatibility with the ClawHub/OpenClaw skill format. Use as a reference when creating skills for both the dcc-mcp-core ecosystem and ClawHub marketplace. Not intended for production use — this is an authoring reference only.
example-layered-skill
Example skill — reference implementation of the internal layered architecture pattern (Tools / Services / Utils) for complex skills with shared business logic. Use as a template when a skill outgrows a single scripts/execute.py file. Not intended for production use — see docs/guide/skills.md for the architectural…