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 LeoLin990405/r-analytics-skill --skill ndtvgit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWrote 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/leolin990405/r-analytics-skill/ndtv)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/ndtv"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/ndtv.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.00573 |
| Opus 5 | $0.00010 | $0.00287 |
| Sonnet 5 | $0.00004 | $0.00115 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
ndtv 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 3d 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.
What it actually says
ndtv
Network dynamic temporal visualization.
Compute Animation
library(ndtv)
# Compute layout for animation
compute.animation(dnet,
animation.mode = "kamadakawai",
slice.par = list(
start = 0,
end = 10,
interval = 1,
aggregate.dur = 1
))
Render Animation
# HTML/D3 animation
render.d3movie(dnet, filename = "network.html")
# GIF animation
render.animation(dnet, filename = "network.gif")
# Video
render.animation(dnet, filename = "network.mp4",
ani.options = list(interval = 0.1))
Customization
render.d3movie(dnet,
# Vertex appearance
vertex.col = "blue",
vertex.cex = 1.5,
vertex.sides = 50, # Circle
# Edge appearance
edge.col = "gray",
edge.lwd = 1,
# Labels
displaylabels = TRUE,
label.cex = 0.8,
label.col = "black",
# Output
filename = "network.html",
output.mode = "htmlWidget"
)
Dynamic Attributes
# Color by attribute
render.d3movie(dnet,
vertex.col = function(slice) {
get.vertex.attribute(slice, "group")
})
# Size by degree
render.d3movie(dnet,
vertex.cex = function(slice) {
degree(slice) / 5
})
Timeline
# Add timeline
timeline(dnet,
slice.par = list(start = 0, end = 10, interval = 1))
# Filmstrip view
filmstrip(dnet, displaylabels = TRUE)
Layout Modes
# Available modes
compute.animation(dnet, animation.mode = "kamadakawai")
compute.animation(dnet, animation.mode = "MDSJ")
compute.animation(dnet, animation.mode = "Graphviz")
compute.animation(dnet, animation.mode = "useAttribute")
Proximity Timeline
# Proximity timeline plot
proximity.timeline(dnet,
default.dist = 10,
mode = "sammon")
Export Frames
# Export individual frames
for (t in 0:10) {
net_t <- network.extract(dnet, at = t)
png(paste0("frame_", t, ".png"))
plot(net_t)
dev.off()
}
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.
- 3d ago First seen · 121 lines · 21 tokens per session scan A b7c8a69cbf9c
ndtv is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 573 once invoked, about $0.0001 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 2026-09-03.
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