Borrowing it
Nothing to install: this file belongs to dylanroscover/Embody. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dylanroscover/Embody/main/.claude/skills/pop-networks/SKILL.mdgit clone --depth 1 https://github.com/dylanroscover/EmbodyWrote 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/dylanroscover/embody/pop-networks)<a href="https://agentmods.dev/skills/dylanroscover/embody/pop-networks"><img src="https://agentmods.dev/badge/skills/dylanroscover/embody/pop-networks/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/dylanroscover/embody/pop-networks"><img src="https://agentmods.dev/badge/skills/dylanroscover/embody/pop-networks.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 | $0.00042 | $0.03399 |
| Opus 5 | $0.00021 | $0.01699 |
| Sonnet 5 | $0.00008 | $0.00680 |
| Haiku 4.5 | $0.00004 | $0.00340 |
Grade A, and why
pop-networks 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
POP Networks
Build GPU-resident point and geometry systems with TouchDesigner POPs. POPs are the 2025+ point-operator family: geometry attributes live on the GPU, many operations run in parallel, and the right chain can replace CPU-heavy SOP or particle workflows.
Use this skill with /create-operator, /visual-aesthetics, performance.md, network-layout.md, and td-python.md. POP correctness is not just a clean network: it is a rendered frame, readable layout, bounded performance, and no operator errors.
POPs vs SOPs
Use POPs when the work is point-heavy, particle-heavy, or naturally parallel:
- Many points, particles, trails, instancing templates, or live geometry streams.
- GPU-side displacement, filtering, attributes, neighborhood queries, texture lookups, or simulation loops.
- Geometry that needs to stay GPU-resident for rendering, instancing, GLSL, or TOP/MAT interop.
- SOP chains whose main cost is moving or filtering lots of points every frame.
Keep SOPs when they are the honest tool:
- Booleans, UV unwrap, small static meshes, precise CPU geometry tools, or legacy SOP-only workflows.
- Tiny one-off static shapes where a SOP is clearer and has no measurable cost.
- Any workflow that needs a mature CPU operator with no POP equivalent.
Prefer POPs for dynamic scale, not as a religion. Start from the user's goal, expected point count, and render path, then choose the family that keeps the system understandable and fast.
Core Vocabulary
Generators create points or geometry:
gridPOP,boxPOP,spherePOP,torusPOP,tubePOP,circlePOP,linePOP,rectanglePOP.randomPOP,sprinklePOP,pointgeneratorPOP,patternPOP,curvePOP.particlePOPcreates and manages built-in particles over time.sourcePOPis common in feedback-style particle systems.
Filters and topology ops reshape or route data:
transformPOP,mathPOP,mathmixPOP,mathcombinePOP,normalizePOP,limitPOP,rerangePOP,quantizePOP,trigPOP.noisePOPfor built-in noise displacement or attribute generation.attributePOP,attributecombinePOP,attributeconvertPOP,normalPOP.mergePOP,switchPOP,selectPOP,deletePOP,groupPOP,sortPOP,copyPOP,blendPOP,nullPOP.primitivePOP,convertPOP,extrudePOP,subdividePOP,facetPOP,polygonizePOP.
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 · 210 lines · 42 tokens per session scan A 96ee8c411797
pop-networks is a skill published in the GitHub repository dylanroscover/Embody (170 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 3,399 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 2026-08-30.
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