respol-data-analysis
Use when executing and stress-testing the empirical analysis for a Research Policy (RP) manuscript — building bibliometric/patent variables, running estimation or qualitative coding, and assembling robustness that an innovation-studies referee will accept. Executes the analysis; it does not choose the design (respol-methods) or present exhibits (respol-tables-figures).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill respol-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis (respol-data-analysis) ## When to trigger - Patent/bibliometric variables are built but the construction steps are not documented or reproducible - Headline results exist but robustness to alternative measures and specifications is thin - A count outcome (patents, citations) is run with OLS instead of an appropriate count model - Qualitative coding lacks a transparent coding scheme or inter-coder reliability - A referee says results are "not robust," "driven by outliers/one sector," or "the data are a black box" ## The Research Policy analysis bar RP referees know innovation data intimately and distrust opaque pipelines. The two things they probe hardest are **how the variables were built** (especially patent/bibliometric ones) and **whether the finding survives the obvious alternatives**. Counts and skewed distributions are the norm in innovation data, so estimators must respect that; and because most RP indicators are noisy proxies, robustness is not optional decoration — it is how you show the innovation claim, not the measure's artifacts, drives the result. ## Building and modeling innovation data ### Variable construction (document everything) - For patents/cit
- When to trigger
- The Research Policy analysis bar
- Building and modeling innovation data
- Variable construction (document everything)
- Estimation that fits innovation outcomes
- Qualitative analysis
- Robustness that persuades RP
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the respol-data-analysis skill do?
Use when executing and stress-testing the empirical analysis for a Research Policy (RP) manuscript — building bibliometric/patent variables, running estimation or qualitative coding, and assembling robustness that an innovation-studies referee will accept. Executes the analysis; it does not choose the design (respol-methods) or present exhibits (respol-tables-figures).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill respol-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.