## ----include=FALSE------------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE, comment = "#>",
  eval = identical(tolower(Sys.getenv("LLMRAGENT_RUN_VIGNETTES", "false")), "true")
)

## ----setup--------------------------------------------------------------------
# library(LLMRagent)
# cfg <- LLMR::llm_config("groq", "openai/gpt-oss-20b", temperature = 0.7)

## ----robustness---------------------------------------------------------------
# rate_risk <- function(cond, rep, perturb) {
#   a <- agent("Rater", perturb$config)
#   answer <- a$reply(perturb$prompt(
#     "On a scale of 1 to 10, how risky is skydiving? Reply with one integer."
#   ))
#   as.numeric(gsub("[^0-9].*$", "", trimws(answer)))
# }
# 
# battery <- agent_robustness(
#   rate_risk,
#   vary = list(temperature = c("0", "1")),
#   reps = 3,
#   measure = function(x) x,
#   config = cfg
# )
# 
# battery$by_axis
# battery$overall

## ----personas-----------------------------------------------------------------
# base <- persona_frame(
#   "A first-time voter in a competitive district.",
#   source = "synthetic",
#   scope = list(country = "US")
# )
# 
# set <- persona_variants(
#   base,
#   vary = list(age = c("22", "52"),
#               employment = c("salaried", "hourly"))
# )
# 
# audit <- persona_audit(set)
# audit
# diagnostics(audit)

## ----claims-------------------------------------------------------------------
# coder <- agent("Coder", cfg)
# coder$chat("Is this abstract about climate? Answer yes or no.\n...")
# run <- mark_claim_type(as_agent_run(coder), "coding")
# 
# report(run)

## ----lint---------------------------------------------------------------------
# llm_claim_lint(
#   "We find that 60% of Americans support the policy.",
#   run = run,
#   action = "scope"
# )