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 letitbk/claude-academic-setup --skill survey-analysisgit 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/survey-analysis)<a href="https://agentmods.dev/skills/letitbk/claude-academic-setup/survey-analysis"><img src="https://agentmods.dev/badge/skills/letitbk/claude-academic-setup/survey-analysis/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/letitbk/claude-academic-setup/survey-analysis"><img src="https://agentmods.dev/badge/skills/letitbk/claude-academic-setup/survey-analysis.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 | $0.00052 | $0.02188 |
| Opus 5 | $0.00026 | $0.01094 |
| Sonnet 5 | $0.00010 | $0.00438 |
| Haiku 4.5 | $0.00005 | $0.00219 |
Grade A, and why
survey-analysis 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 9d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Survey Analysis
Design-based survey inference in R using the survey package. Covers setup, descriptives, regression, and raking. For IPW/propensity score weighting, use /compute-survey-weights.
Survey Design Setup
Simple weights only
library(survey)
des <- svydesign(~1, weights = ~wt, data = df)
Strata + weights
des <- svydesign(~1, strata = ~strat, weights = ~wt, data = df)
Full complex design (PSU + strata + weights)
des <- svydesign(~psu, strata = ~strat, weights = ~wt, nest = TRUE, data = df)
Always set this option
# Handle singleton strata (one PSU per stratum)
options(survey.lonely.psu = "adjust")
Common Survey Datasets
| Survey | Design |
|---|---|
| TESS | svydesign(~1, weights = ~weight, data = df) |
| GSS | svydesign(~vpsu, strata = ~vstrat, weights = ~wtssall, nest = TRUE, data = df) |
| NHIS | svydesign(~psu_p, strata = ~strat_p, weights = ~wtfa_sa, nest = TRUE, data = df) |
| BRFSS | svydesign(~1, strata = ~_ststr, weights = ~_llcpwt, data = df) |
| ACS/PUMS | svrepdesign(weights = ~PWGTP, repweights = "PWGTP[0-9]+", type = "JK1", scale = 4/80, data = df) |
For custom surveys: start with simple weights. Add strata/PSU only if the codebook documents them.
Weighted Descriptives
Means and proportions
# Weighted mean + SE
svymean(~continuous_var, design = des, na.rm = TRUE)
# Weighted proportion (factor variable)
svymean(~factor(categorical_var), design = des, na.rm = TRUE)
# Confidence intervals
confint(svymean(~continuous_var, design = des, na.rm = TRUE))
Totals and quantiles
# Weighted total
svytotal(~continuous_var, design = des, na.rm = TRUE)
# Weighted quantiles
svyquantile(~continuous_var, design = des, quantiles = c(0.25, 0.5, 0.75), na.rm = TRUE)
Subgroup estimates
# Mean by group (like Stata: svy: mean var, over(group))
svyby(~outcome, ~group, design = des, FUN = svymean, na.rm = TRUE)
# Proportion by group
svyby(~factor(binary_var), ~group, design = des, FUN = svymean, na.rm = TRUE)
# Confidence intervals for subgroup estimates
confint(svyby(~outcome, ~group, design = des, FUN = svymean, na.rm = TRUE))
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.
- 9d ago First seen · 251 lines · 52 tokens per session scan A ef0944300eaa
survey-analysis is a skill published in the GitHub repository letitbk/claude-academic-setup (44 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 2,188 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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