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 MoizIbnYousaf/marketing-cli --skill manimgl-best-practicesgit clone --depth 1 https://github.com/MoizIbnYousaf/marketing-cliWrote 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/moizibnyousaf/marketing-cli/manimgl-best-practices)<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/manimgl-best-practices"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/manimgl-best-practices/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/moizibnyousaf/marketing-cli/manimgl-best-practices"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/manimgl-best-practices.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00167 | $0.02011 |
| Opus 5 | $0.00084 | $0.01006 |
| Sonnet 5 | $0.00033 | $0.00402 |
| Haiku 4.5 | $0.00017 | $0.00201 |
Grade A, and why
manimgl-best-practices 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 10d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
How to use
Read individual rule files for detailed explanations and code examples:
Core Concepts
- rules/scenes.md - InteractiveScene, Scene types, and construct method
- rules/mobjects.md - Mobject types, VMobject, Groups, and positioning
- rules/animations.md - Animation classes, playing animations, and timing
Creation & Transformation
- rules/creation-animations.md - ShowCreation, Write, FadeIn, DrawBorderThenFill
- rules/transform-animations.md - Transform, ReplacementTransform, TransformMatchingTex
- rules/animation-groups.md - LaggedStart, Succession, AnimationGroup
Text & Math
- rules/tex.md - Tex class, raw strings R"...", and LaTeX rendering
- rules/text.md - Text mobjects, fonts, and styling
- rules/t2c.md - tex_to_color_map (t2c) for coloring math expressions
Styling & Appearance
- rules/colors.md - Color constants, gradients, RGB, hex, GLSL coloring
- rules/styling.md - Fill, stroke, opacity, backstroke, gloss, shadow
3D & Camera
- rules/3d.md - 3D objects, surfaces, Sphere, Torus, parametric surfaces, lighting
- rules/camera.md - frame.reorient(), Euler angles, fix_in_frame(), camera animations
Interactive Development
- rules/interactive.md - Interactive mode with
-seflag, checkpoint_paste() - rules/frame.md - self.frame, camera control, reorient, and zooming
- rules/embedding.md - self.embed() for IPython debugging, touch() mode
Configuration & CLI
- rules/cli.md - manimgl command, flags (-w, -o, -se, -l, -h), rendering options
- rules/config.md - custom_config.yml, directories, camera settings, quality presets
Working Examples
Complete, tested example files demonstrating common patterns:
What ships with it
60 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.
- examples/attention_arcs_animation.py 8.1 KB runs code
- examples/attention_pattern_dots.py 4.3 KB runs code
- examples/attention_scenes.py 34 KB runs code
- examples/attention_softmax_masking.py 3.7 KB runs code
- examples/autoregressive_flow.py 7.0 KB runs code
- examples/basic_multihead.py 5.0 KB runs code
- examples/bloch_sphere_3d.py 6.9 KB runs code
- examples/block_collision_basic.py 5.4 KB runs code
- examples/blocks_3d.py 7.2 KB runs code
- examples/collision_phase_space.py 10 KB runs code
- examples/complex_s_plane.py 9.8 KB runs code
- examples/cost_function.py 3.9 KB runs code
- examples/cube_projection_3d.py 6.3 KB runs code
- examples/damped_solutions_splane.py 9.2 KB runs code
- examples/dot_product_visualization.py 5.5 KB runs code
- examples/double_slit_interference.py 11 KB runs code
- examples/eigenvalue_equations.py 7.2 KB runs code
- examples/eigenvector_flow_field.py 6.5 KB runs code
- examples/eigenvector_matrix_transformation.py 6.2 KB runs code
- examples/elastic_collision_vectors.py 10 KB runs code
- examples/embedding_matrix.py 5.5 KB runs code
- examples/equation_transforms.py 10.0 KB runs code
- examples/exponential_derivative.py 8.2 KB runs code
- examples/fibonacci_eigenvalues.py 5.9 KB runs code
- examples/gradient_descent_basic.py 6.8 KB runs code
- examples/hexagon_cube_correspondence.py 2.8 KB runs code
- examples/integration_visualization.py 6.9 KB runs code
- examples/laplace_integral.py 9.3 KB runs code
- examples/light_polarization.py 11 KB runs code
- examples/linear_regression.py 3.8 KB runs code
- examples/llm_prediction_pipeline.py 9.3 KB runs code
- examples/lorenz_attractor.py 5.3 KB runs code
- examples/lozenge_tiling.py 3.2 KB runs code
- examples/max_random_process.py 5.8 KB runs code
- examples/mlp_forward_pass.py 8.7 KB runs code
- examples/mlp_network_icon.py 3.2 KB runs code
- examples/mlp_neuron_activation.py 7.7 KB runs code
- examples/mlp_neurons_flow.py 10 KB runs code
- examples/mlp_relu_visualization.py 5.3 KB runs code
- examples/mlp_vector_space.py 8.6 KB runs code
- examples/mlp_weight_matrix.py 8.5 KB runs code
- examples/multi_head_attention.py 8.3 KB runs code
- examples/network_block_flow.py 8.4 KB runs code
- examples/neural_network_basic.py 4.3 KB runs code
- examples/parallax_starfield.py 6.3 KB runs code
- examples/probability_distribution.py 12 KB runs code
- examples/probability_output.py 12 KB runs code
- examples/quantum_gates.py 8.7 KB runs code
- examples/qubit_state_vector.py 6.0 KB runs code
- examples/query_key_dot_products.py 4.2 KB runs code
- examples/query_key_space_mapping.py 6.3 KB runs code
- examples/radial_wave_visualization.py 7.6 KB runs code
- examples/rotating_exponentials.py 9.3 KB runs code
- examples/semantic_similarity.py 5.8 KB runs code
- examples/simple_test.py 1.8 KB runs code
- examples/softmax_visualization.py 4.8 KB runs code
- examples/solve_damped_ode.py 9.1 KB runs code
- examples/spring_mass_system.py 9.6 KB runs code
- examples/sqrt_random_process.py 4.8 KB runs code
- examples/superposition_effect.py 9.0 KB runs code
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.
- 10d ago First seen · 216 lines · 167 tokens per session scan A 31e2114d7676
manimgl-best-practices is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 24d ago), licensed MIT. It adds 167 tokens to every session and 2,011 once invoked, about $0.0008 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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