Agent skill · Data & Analytics

demog-data-analysis

Use when executing and reporting the analysis for a Demography (PAA / Duke University Press) manuscript so it survives expert, double-blind review — correct rate construction, honest uncertainty, and demographic methods done right (life tables, decomposition, event history, age-period-cohort). Guides analysis norms; it does not fabricate results.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill demog-data-analysis --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: Demography-Skills/skills/demog-data-analysis/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Data Analysis (demog-data-analysis) Demography reviewers are expert demographers and the journal expects **reproducible code** behind the results (see `demog-data-and-reproducibility`). Analyze as if a methodologist will re-derive your rates and re-run your decomposition — because they may. This skill covers execution and reporting norms; method choice lives in `demog-research-design`. ## When to trigger - Constructing rates and life tables; building the results section - Running a decomposition, event-history, APC, or projection analysis - A reviewer asked for robustness, sensitivity, or alternative specifications - Making the analysis reproducible before deposit ## Analysis norms Demography expects 1. **Get the denominators right.** Exposure (person-years), the correct base population, and age/period alignment are where demographic analyses live or die. Document how rates were built. 2. **Report uncertainty honestly.** Confidence/credible intervals for rates, life-expectancy contributions, and derived quantities — not just point estimates or stars. Bootstrap or delta-method intervals for decomposition components and life-table functions. 3. **Decomposition with clear components

What's inside
Steps it walks through
  1. When to trigger
  2. Analysis norms Demography expects
  3. Demographic computation specifics
  4. Reproducibility while you work (not at the end)
  5. Execution bridge (StatsPAI / Stata MCP)
  6. Anti-patterns
  7. Evidence pass for Demography
  8. Output format
  9. Supplementary resources
More from Awesome-Journal-Skills
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About this skill
What does the demog-data-analysis skill do?

Use when executing and reporting the analysis for a Demography (PAA / Duke University Press) manuscript so it survives expert, double-blind review — correct rate construction, honest uncertainty, and demographic methods done right (life tables, decomposition, event history, age-period-cohort). Guides analysis norms; it does not fabricate results.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill demog-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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