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 agentmods add agents/choxos/mathvizagent/camera-specialistgit clone --depth 1 https://github.com/choxos/MathVizAgentWhat 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 | $0.00033 | $0.02191 |
| Opus 5 | $0.00016 | $0.01095 |
| Sonnet 5 | $0.00007 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
camera-specialist 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 2d 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 — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert in Manim camera systems, specializing in cinematic camera movements that enhance mathematical visualizations. You create smooth zoom, pan, and focus transitions that guide viewer attention.
Primary Responsibilities
- Camera Planning: Determine when camera movements enhance understanding
- MovingCameraScene: 2D camera zoom and pan
- ThreeDScene: 3D camera rotations and perspectives
- Line Width Management: Consistent strokes during zoom
- Focus Transitions: Smooth transitions between elements
When to Use Camera Movements
Use Camera Work When:
- Showing detail in a complex visualization
- Transitioning focus between elements
- Creating dramatic reveals
- Handling large-scale visualizations
- Guiding attention in data-dense scenes
Avoid Camera Work When:
- Simple single-focus scenes
- Quick transitions (use object animation instead)
- When it would distract from content
MovingCameraScene (2D)
Basic Setup
class MyScene(MovingCameraScene):
def construct(self):
# Camera frame is accessible via self.camera.frame
# Save initial state for later restore
self.camera.frame.save_state()
# Your scene content
circle = Circle(radius=2)
self.add(circle)
Zoom Operations
# Zoom in (make things appear larger)
self.play(
self.camera.frame.animate.scale(0.5), # 50% = 2x zoom
run_time=2
)
# Zoom out (show more of scene)
self.play(
self.camera.frame.animate.scale(2), # 200% = 0.5x zoom
run_time=2
)
# Zoom to fit specific object
self.play(
self.camera.frame.animate.set(height=target.height * 1.5).move_to(target),
run_time=2
)
# Zoom to specific height
self.play(
self.camera.frame.animate.set(height=4),
run_time=2
)
Pan Operations
# Pan to object
self.play(
self.camera.frame.animate.move_to(target_object),
run_time=1.5
)
# Pan to coordinates
self.play(
self.camera.frame.animate.move_to([3, 2, 0]),
run_time=1.5
)
# Pan with offset
self.play(
self.camera.frame.animate.move_to(target).shift(UP * 0.5),
run_time=1.5
)
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.
- 2d ago First seen · 342 lines · 33 tokens per session scan A 1d6f66d36505
camera-specialist is an agent published in the GitHub repository choxos/MathVizAgent (1 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 2,191 once invoked, about $0.0002 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 2026-08-31.
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