Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Control Blender directly from Hermes via socket connection to the blender-mcp addon. Create 3D objects, materials, animations, and run arbitrary Blender Python (bpy) code. Use when user wants to create or modify anything in Blender.
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安装前可以查看交给 AI 代理的指令内容。
Control a running Blender instance from Hermes via socket on TCP port 9876.
curl -sL https://raw.githubusercontent.com/ahujasid/blender-mcp/main/addon.py -o ~/Desktop/blender_mcp_addon.py
In Blender: Edit > Preferences > Add-ons > Install > select blender_mcp_addon.py Enable "Interface: Blender MCP"
Press N in Blender viewport to open sidebar. Find "BlenderMCP" tab and click "Start Server".
nc -z -w2 localhost 9876 && echo "OPEN" || echo "CLOSED"
Plain UTF-8 JSON over TCP -- no length prefix.
Send: {"type": "<command>", "params": {<kwargs>}} Receive: {"status": "success", "result": <value>} {"status": "error", "message": "<reason>"}
| type | params | description |
|---|---|---|
| execute_code | code (str) | Run arbitrary bpy Python code |
| get_scene_info | (none) | List all objects in scene |
| get_object_info | object_name (str) | Details on a specific object |
| get_viewport_screenshot | (none) | Screenshot of current viewport |
Use this inside execute_code tool calls:
import socket, json
def blender_exec(code: str, host="localhost", port=9876, timeout=15):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((host, port))
s.settimeout(timeout)
payload = json.dumps({"type": "execute_code", "params": {"code": code}})
s.sendall(payload.encode("utf-8"))
buf = b""
while True:
try:
chunk = s.recv(4096)
if not chunk:
break
buf += chunk
try:
json.loads(buf.decode("utf-8"))
break
except json.JSONDecodeError:
continue
except socket.timeout:
break
s.close()
return json.loads(buf.decode("utf-8"))
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete()
bpy.ops.mesh.primitive_uv_sphere_add(radius=1, location=(0, 0, 0))
bpy.ops.mesh.primitive_cube_add(size=2, location=(3, 0, 0))
bpy.ops.mesh.primitive_cylinder_add(radius=0.5, depth=2, location=(-3, 0, 0))
mat = bpy.data.materials.new(name="MyMat")
mat.use_nodes = True
bsdf = mat.node_tree.nodes.get("Principled BSDF")
bsdf.inputs["Base Color"].default_value = (R, G, B, 1.0)
bsdf.inputs["Roughness"].default_value = 0.3
bsdf.inputs["Metallic"].default_value = 0.0
obj.data.materials.append(mat)
obj.location = (0, 0, 0)
obj.keyframe_insert(data_path="location", frame=1)
obj.location = (0, 0, 3)
obj.keyframe_insert(data_path="location", frame=60)
bpy.context.scene.render.filepath = "/tmp/render.png"
bpy.context.scene.render.engine = 'CYCLES'
bpy.ops.render.render(write_still=True)
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
中文简介正在准备中。当前显示原文说明。
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
中文简介正在准备中。当前显示原文说明。