Individual skill
sf-data-analysis
Use when executing and reporting the analysis for a Social Forces (SF) manuscript so it survives expert, double-anonymized review — honest uncertainty, robustness, and triangulation appropriate to quantitative, demographic, network, or computational work. Guides analysis norms; it does not fabricate results.
Social-Forces-Skills/skills/sf-data-analysis/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,405
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 6,392 bytes
Read excerpt
# Data Analysis (sf-data-analysis)
Social Forces built its standing on **methodological rigor**, and its reviewers are sophisticated. The
analysis must report uncertainty honestly, probe robustness that could actually break the result, and
stay reproducible. This skill covers execution and reporting norms; design decisions live in
`sf-research-design`. Keep an eye on the exhibit budget — results must fit within **10 tables and
figure panels** (see `sf-tables-figures`).
## When to trigger
- Running main and supporting analyses; building the results section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Reconciling confirmatory vs. exploratory analyses
- Making the analysis reproducible before drafting the data availability statement