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 nlmegit 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/nlme)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/nlme"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/nlme/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/leolin990405/r-analytics-skill/nlme"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/nlme.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00024 | $0.01120 |
| Opus 5 | $0.00012 | $0.00560 |
| Sonnet 5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00002 | $0.00112 |
Grade A, and why
nlme 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 5d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nlme
Linear and nonlinear mixed-effects models.
Linear Mixed Models
library(nlme)
# Random intercept
model <- lme(y ~ x, random = ~ 1 | group, data = df)
# Random intercept and slope
model <- lme(y ~ x, random = ~ 1 + x | group, data = df)
# Nested random effects
model <- lme(y ~ x, random = ~ 1 | group1/group2, data = df)
Model Specification
# Using formula
model <- lme(
fixed = y ~ x1 + x2,
random = ~ 1 | group,
data = df
)
# Using pdMat classes
model <- lme(
y ~ x,
random = pdDiag(~ 1 + x | group), # Diagonal covariance
data = df
)
# Compound symmetry
model <- lme(
y ~ x,
random = pdCompSymm(~ 1 | group),
data = df
)
Correlation Structures
# AR(1) correlation
model <- lme(y ~ x, random = ~ 1 | group,
correlation = corAR1(form = ~ time | group),
data = df
)
# Compound symmetry
model <- lme(y ~ x, random = ~ 1 | group,
correlation = corCompSymm(form = ~ 1 | group),
data = df
)
# Exponential spatial correlation
model <- lme(y ~ x, random = ~ 1 | group,
correlation = corExp(form = ~ lat + lon | group),
data = df
)
# General correlation
model <- lme(y ~ x, random = ~ 1 | group,
correlation = corSymm(form = ~ 1 | group),
data = df
)
Variance Functions
# Heteroscedasticity by group
model <- lme(y ~ x, random = ~ 1 | group,
weights = varIdent(form = ~ 1 | group),
data = df
)
# Variance proportional to fitted values
model <- lme(y ~ x, random = ~ 1 | group,
weights = varPower(),
data = df
)
# Exponential variance
model <- lme(y ~ x, random = ~ 1 | group,
weights = varExp(form = ~ x),
data = df
)
# Combined variance function
model <- lme(y ~ x, random = ~ 1 | group,
weights = varComb(varIdent(form = ~ 1 | group), varPower()),
data = df
)
Model Summary
# Summary
summary(model)
# Fixed effects
fixef(model)
fixed.effects(model)
# Random effects
ranef(model)
random.effects(model)
# Variance components
VarCorr(model)
# Confidence intervals
intervals(model)
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.
- 5d ago First seen · 211 lines · 24 tokens per session scan A f84b9789e85a
nlme is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 1,120 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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