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 skills/letitbk/claude-academic-setup/marginaleffectsnpx skills add letitbk/claude-academic-setup --skill marginaleffectsgit clone --depth 1 https://github.com/letitbk/claude-academic-setupWrote 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/letitbk/claude-academic-setup/marginaleffects)<a href="https://agentmods.dev/skills/letitbk/claude-academic-setup/marginaleffects"><img src="https://agentmods.dev/badge/skills/letitbk/claude-academic-setup/marginaleffects.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 | $0.00060 | $0.01642 |
| Opus 5 | $0.00030 | $0.00821 |
| Sonnet 5 | $0.00012 | $0.00328 |
| Haiku 4.5 | $0.00006 | $0.00164 |
Grade A, and why
marginaleffects 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
marginaleffects
Compute marginal effects, comparisons, and predictions in R. This skill covers computation only — for plotting, use /marginal-effects-plot or /coefficient-plot.
Reference: https://marginaleffects.com/
Three Core Functions
| Function | What it computes | When to use |
|---|---|---|
avg_slopes() / slopes() |
Partial derivatives (instantaneous rate of change) | Continuous variables: "effect of a 1-unit increase" |
avg_comparisons() / comparisons() |
Discrete changes (finite differences) | Factor contrasts OR custom continuous contrasts |
avg_predictions() / predictions() |
Predicted values at observed or specified data | "What's the predicted outcome for group X?" |
The avg_* versions average across observations (AMEs). The non-avg_ versions return observation-level estimates.
Quick Start
library(marginaleffects)
library(data.table)
model <- glm(y ~ x1 + x2 + factor(group), data = df, family = binomial)
# Average marginal effects (continuous vars = slopes, factors = contrasts)
avg_slopes(model, type = "response")
# Pairwise factor contrasts
avg_comparisons(model, variables = list(group = "pairwise"), type = "response")
# Predicted probabilities by group
avg_predictions(model, by = "group", type = "response")
Patterns by Model Type
OLS: lm()
model <- lm(y ~ x1 + x2 + factor(treatment), data = df)
# AMEs — straightforward, no type argument needed
avg_slopes(model)
# Factor contrasts (pairwise)
avg_comparisons(model, variables = list(treatment = "pairwise"))
# Contrast against reference level (default behavior)
avg_comparisons(model, variables = list(treatment = "reference"))
# Robust SEs
avg_slopes(model, vcov = "HC3")
Logit/Probit: glm()
model <- glm(y ~ x1 + factor(treatment), data = df, family = binomial)
# AMEs on probability scale — ALWAYS specify type = "response"
avg_slopes(model, type = "response")
# Factor contrasts on probability scale
avg_comparisons(model, variables = list(treatment = "pairwise"), type = "response")
# Predictions on probability scale
avg_predictions(model, by = "treatment", type = "response")
# Clustered SEs
avg_slopes(model, type = "response", vcov = ~cluster_id)
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 · 196 lines · 60 tokens per session scan A ba8278051a85
marginaleffects is a skill published in the GitHub repository letitbk/claude-academic-setup (43 stars, last pushed 2mo ago), licensed MIT. It adds 60 tokens to every session and 1,642 once invoked, about $0.0003 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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