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 IvanYangYangXi/artclaw_bridge --skill blender-viewport-capturegit clone --depth 1 https://github.com/IvanYangYangXi/artclaw_bridgeWrote 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/ivanyangyangxi/artclaw_bridge/blender-viewport-capture)<a href="https://agentmods.dev/skills/ivanyangyangxi/artclaw_bridge/blender-viewport-capture"><img src="https://agentmods.dev/badge/skills/ivanyangyangxi/artclaw_bridge/blender-viewport-capture/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/ivanyangyangxi/artclaw_bridge/blender-viewport-capture"><img src="https://agentmods.dev/badge/skills/ivanyangyangxi/artclaw_bridge/blender-viewport-capture.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 | $0.00085 | $0.02444 |
| Opus 5 | $0.00043 | $0.01222 |
| Sonnet 5 | $0.00017 | $0.00489 |
| Haiku 4.5 | $0.00009 | $0.00244 |
Grade A, and why
blender-viewport-capture 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 12d 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blender 视口截图
捕获 Blender 3D 视口画面,用于 AI 视觉分析(构图、光照、场景检查等)。
⚠️ 仅适用于 Blender — 通过
run_python执行
核心方法
方法 1:OpenGL 渲染截图(推荐)
使用 bpy.ops.render.opengl() 进行视口 OpenGL 渲染,捕获当前 3D 视口画面。
import bpy
import os
import tempfile
# 保存原始设置
scene = bpy.context.scene
original_path = scene.render.filepath
original_format = scene.render.image_settings.file_format
original_quality = scene.render.image_settings.quality
# 设置输出
output_dir = os.path.join(tempfile.gettempdir(), "artclaw_captures")
os.makedirs(output_dir, exist_ok=True)
import time
timestamp = int(time.time())
output_path = os.path.join(output_dir, f"viewport_{timestamp}.png")
scene.render.filepath = output_path
scene.render.image_settings.file_format = 'PNG'
scene.render.image_settings.quality = 90
# 执行 OpenGL 渲染(视口截图)
bpy.ops.render.opengl(write_still=True)
# 恢复原始设置
scene.render.filepath = original_path
scene.render.image_settings.file_format = original_format
scene.render.image_settings.quality = original_quality
print(f"✅ 视口截图已保存: {output_path}")
方法 2:带自定义分辨率的截图
import bpy
import os
import tempfile
import time
scene = bpy.context.scene
# 保存原始设置
original_path = scene.render.filepath
original_format = scene.render.image_settings.file_format
original_quality = scene.render.image_settings.quality
original_res_x = scene.render.resolution_x
original_res_y = scene.render.resolution_y
original_pct = scene.render.resolution_percentage
# 设置截图分辨率
scene.render.resolution_x = 1920
scene.render.resolution_y = 1080
scene.render.resolution_percentage = 100
# 设置输出路径
output_dir = os.path.join(tempfile.gettempdir(), "artclaw_captures")
os.makedirs(output_dir, exist_ok=True)
timestamp = int(time.time())
output_path = os.path.join(output_dir, f"viewport_{timestamp}.jpg")
scene.render.filepath = output_path
scene.render.image_settings.file_format = 'JPEG'
scene.render.image_settings.quality = 85
# 执行截图
bpy.ops.render.opengl(write_still=True)
# 恢复所有原始设置
scene.render.filepath = original_path
scene.render.image_settings.file_format = original_format
scene.render.image_settings.quality = original_quality
scene.render.resolution_x = original_res_x
scene.render.resolution_y = original_res_y
scene.render.resolution_percentage = original_pct
print(f"✅ 视口截图已保存: {output_path}")
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.
- 12d ago First seen · 315 lines · 85 tokens per session scan A 29973c3501a0
blender-viewport-capture is a skill published in the GitHub repository IvanYangYangXi/artclaw_bridge (35 stars, last pushed 4mo ago), licensed MIT. It adds 85 tokens to every session and 2,444 once invoked, about $0.0004 per session on Opus 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.
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