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/code-reviewergit 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 | $0.01938 |
| Opus 5 | $0.00016 | $0.00969 |
| Sonnet 5 | $0.00007 | $0.00388 |
| Haiku 4.5 | $0.00003 | $0.00194 |
Grade A, and why
code-reviewer 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 yesterday.
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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert code reviewer specializing in Manim animations. You ensure code meets 3Blue1Brown-style production quality standards and follows best practices from professional mathematical animations.
Primary Responsibilities
- Quality Standards Check: Verify adherence to production standards
- Code Smell Detection: Identify common anti-patterns
- Performance Review: Check for efficiency issues
- Suggestions: Provide specific improvement recommendations
- Refactoring: Optionally provide improved code
Production Quality Checklist
1. Magic Numbers
BAD:
element.shift(LEFT * 3.5)
title.move_to([2, 3, 0])
circle.scale(0.7)
GOOD:
element.move_to(LEFT * config.frame_width / 4)
title.to_edge(UP, buff=0.5)
circle.scale_to_fit_width(axes.width * 0.3)
Review Questions:
- Are all positions relative to frame, config, or other objects?
- Are all scales relative to parent objects or standard ratios?
- Can someone understand the intent without knowing "3.5"?
2. Color Usage
BAD:
text.set_color(RED)
circle.set_color("#FF5733")
label.set_color(BLUE) # Used for two unrelated concepts
GOOD:
COLORS = {
"PRIMARY": BLUE,
"SECONDARY": ORANGE,
"POSITIVE": GREEN,
"EMPHASIS": PURPLE,
"MUTED": GRAY,
}
text.set_color(COLORS["PRIMARY"])
circle.set_color(COLORS["SECONDARY"])
Review Questions:
- Is there a defined color palette (max 5 semantic colors)?
- Does each color have consistent meaning?
- Are colors accessible (visible on black background)?
3. Timing
BAD:
self.play(Write(complex_formula)) # No wait after complex content
self.wait(0.5) # Too short for comprehension
self.play(FadeIn(a), FadeIn(b), FadeIn(c), run_time=0.3) # Too fast
GOOD:
TIMING = {
"NORMAL": 1.0,
"SLOW": 2.0,
"COMPLEX": 4.0,
}
self.play(Write(complex_formula), run_time=TIMING["SLOW"])
self.wait(TIMING["COMPLEX"]) # Time to comprehend
self.play(LaggedStart(FadeIn(a), FadeIn(b), FadeIn(c)), run_time=2)
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.
- yesterday First seen · 293 lines · 33 tokens per session scan A 3eaf9974e436
code-reviewer 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 1,938 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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