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From r-econ-data
Common ways to validate or benchmark data analysis estimates using external sources. Use to confirm or test the plausibility of results from an economic analysis.
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/r-econ-data:validate-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
For example, say you wanted to calculate the real median hourly wage for a demographic group not available in SWADL, like foreign-born workers in California. To verify your basic approach, you might first try to replicate something in SWADL approximating the target group, like the real median wages of all workers in California:
For example, say you wanted to calculate the real median hourly wage for a demographic group not available in SWADL, like foreign-born workers in California. To verify your basic approach, you might first try to replicate something in SWADL approximating the target group, like the real median wages of all workers in California:
library(tidyverse)
library(epidatatools)
library(epiextractr)
library(realtalk)
library(swadlr)
library(assertr)
swadl_ca_median = get_swadl(
"hourly_wage_median",
measure = "real_wage_median_2025",
geography = "CA"
) |>
mutate(year = year(date)) |>
select(year, swadl_real_median = value)
cpi_data = c_cpi_u_annual
cpi_base = cpi_data |>
filter(year == 2025) |>
pull(c_cpi_u)
org_median_wage = load_org(
2010:2025,
year,
statefips,
wage,
orgwgt,
citistat
) |>
filter(wage > 0, statefips == 6) |>
summarize(
org_nominal_median = averaged_median(wage, w = orgwgt),
.by = year
) |>
left_join(cpi_data, by = "year") |>
mutate(org_real_median = org_nominal_median * cpi_base / c_cpi_u)
org_median_wage |>
left_join(swadl_ca_median, by = "year") |>
verify(swadl_real_median - org_real_median < 1e-8)
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
npx claudepluginhub economic/epi-skills --plugin r-econ-data