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Structural equation modeling with latent variables guide. An agent skill from wentorai/research-plugins.
Install Sem Guide
$ npx skills add wentorai/research-plugins --skill sem-guide -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wentorai/research-plugins sem-guide --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/statistics/sem-guide .claude/skills/sem-guide && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sem-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guide into .claude/skills/sem-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sem-guide", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guideType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add wentorai/research-plugins --skill sem-guide -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wentorai/research-plugins sem-guide --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analysis/statistics/sem-guide .agents/skills/sem-guide && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sem-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guide into .agents/skills/sem-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sem-guide", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wentorai/research-plugins --skill sem-guide -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wentorai/research-plugins sem-guide --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analysis/statistics/sem-guide .cursor/skills/sem-guide && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sem-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guide into .cursor/skills/sem-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sem-guide", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/wentorai/research-plugins.git --path skills/analysis/statistics/sem-guide--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add wentorai/research-plugins --skill sem-guide -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wentorai/research-plugins sem-guide --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analysis/statistics/sem-guide .gemini/skills/sem-guide && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sem-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guide into .gemini/skills/sem-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sem-guide", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install wentorai/research-plugins sem-guideInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add wentorai/research-plugins --skill sem-guide -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analysis/statistics/sem-guide .github/skills/sem-guide && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sem-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guide into .github/skills/sem-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sem-guide", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wentorai/research-plugins --skill sem-guide -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install wentorai/research-plugins sem-guide --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analysis/statistics/sem-guide .opencode/skills/sem-guide && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sem-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/sem-guide into .opencode/skills/sem-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sem-guide", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sem-guide- GitHub stars
- 298
- Used in
- 1 other repo
- Token cost
- ~1.8k tokens
- SKILL.md length
- 392 words
- Files
- 1
- Skills in repo
- 405
- Repo updated
- First seen
- Licence
- MIT
At a glance
Structural equation modeling with latent variables guide. An agent skill from wentorai/research-plugins.
- Works in 2 steps: Confirmatory Factor Analysis (CFA) → Full Structural Model
- Data & Analytics work in your project
- SKILL.md covers What Is SEM?, SEM Components, Step 1: Confirmatory Factor… and Step 2: Full Structural Model, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Sem Guide is an agent skill from wentorai/research-plugins. Structural equation modeling with latent variables guide
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.
When your agent uses it
- Data & Analytics work in your project
Example prompts
- “/sem-guide”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are r and python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sem Guide loads about 1.8k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 392 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 392 words, ~1,785 tokens.
Download SKILL.mdSave it as
.claude/skills/sem-guide/SKILL.md (or your agent's skills folder).- name
- sem-guide
- description
- Structural equation modeling with latent variables guide
Structural Equation Modeling Guide
Build, estimate, and evaluate structural equation models (SEM) with latent variables using Python (semopy) and R (lavaan), including confirmatory factor analysis and path analysis.
What Is SEM?
Structural Equation Modeling is a multivariate statistical framework that combines factor analysis and path analysis to test complex theoretical models involving:
- Observed (manifest) variables: Directly measured (e.g., survey items, test scores)
- Latent (unobserved) variables: Theoretical constructs measured indirectly through observed indicators (e.g., "motivation," "intelligence")
- Structural paths: Directional relationships between variables (regression-like)
- Measurement model: How latent variables relate to their indicators (CFA)
- Structural model: How latent variables relate to each other (path analysis)
SEM Components
| Component | Description | Diagram Symbol |
|---|---|---|
| Observed variable | Measured directly | Rectangle |
| Latent variable | Inferred from indicators | Oval/circle |
| Regression path | Directional relationship | Single-headed arrow |
| Covariance | Non-directional association | Double-headed arrow |
| Error/residual | Unexplained variance | Small circle with arrow |
Step 1: Confirmatory Factor Analysis (CFA)
CFA tests whether observed variables load onto hypothesized latent factors.
In R (lavaan)
library(lavaan)
# Define the measurement model
# =~ means "is measured by"
cfa_model <- '
# Latent variable definitions
Motivation =~ mot1 + mot2 + mot3 + mot4
SelfEfficacy =~ se1 + se2 + se3
Performance =~ perf1 + perf2 + perf3 + perf4
# Covariances between latent variables (estimated by default in CFA)
'
# Fit the model
fit <- cfa(cfa_model, data = mydata, estimator = "MLR")
# View results
summary(fit, fit.measures = TRUE, standardized = TRUE)
# Key output to examine:
# - Factor loadings (standardized > 0.5 is desirable)
# - Model fit indices (see table below)
# - Modification indices (for model improvement)
modindices(fit, sort = TRUE, minimum.value = 10)In Python (semopy)
import semopy
import pandas as pd
# Define model in lavaan-like syntax
model_spec = """
Motivation =~ mot1 + mot2 + mot3 + mot4
SelfEfficacy =~ se1 + se2 + se3
Performance =~ perf1 + perf2 + perf3 + perf4
"""
# Fit the model
model = semopy.Model(model_spec)
result = model.fit(data)
# View parameter estimates
print(model.inspect())
# Get fit statistics
stats = semopy.calc_stats(model)
print(stats.T)Step 2: Full Structural Model
After confirming the measurement model, add structural (regression) paths.
In R (lavaan)
sem_model <- '
# Measurement model
Motivation =~ mot1 + mot2 + mot3 + mot4
SelfEfficacy =~ se1 + se2 + se3
Performance =~ perf1 + perf2 + perf3 + perf4
# Structural model (regressions)
# ~ means "is regressed on"
Performance ~ Motivation + SelfEfficacy
SelfEfficacy ~ Motivation
# Optional: define indirect effect
# indirect := a * b
'
fit <- sem(sem_model, data = mydata, estimator = "MLR")
summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)Mediation Analysis
mediation_model <- '
# Measurement model
X =~ x1 + x2 + x3
M =~ m1 + m2 + m3
Y =~ y1 + y2 + y3
# Structural model
M ~ a*X # a path
Y ~ b*M + c*X # b path + direct effect c
# Define indirect and total effects
indirect := a * b
total := c + a * b
'
fit <- sem(mediation_model, data = mydata, se = "bootstrap", bootstrap = 1000)
summary(fit, standardized = TRUE)
# Bootstrap confidence intervals for indirect effect
parameterEstimates(fit, boot.ci.type = "bca.simple", standardized = TRUE)Model Fit Assessment
Fit Index Reference Table
| Index | Good Fit | Acceptable | What It Measures |
|---|---|---|---|
| Chi-square (p) | p > 0.05 | Sensitive to N; use with other indices | Exact fit test |
| Chi-square/df | < 2 | < 3 | Parsimony-adjusted exact fit |
| CFI | > 0.95 | > 0.90 | Comparative fit vs. null model |
| TLI | > 0.95 | > 0.90 | CFI adjusted for parsimony |
| RMSEA | < 0.06 | < 0.08 | Approximate fit per df |
| SRMR | < 0.08 | < 0.10 | Average residual correlation |
| AIC/BIC | Lower = better | -- | Model comparison (not absolute) |
Show full SKILL.md (144 more words)Show less
Interpreting Fit
# Extract fit measures in lavaan
fitMeasures(fit, c("chisq", "df", "pvalue", "cfi", "tli", "rmsea",
"rmsea.ci.lower", "rmsea.ci.upper", "srmr"))Reporting template:
The structural equation model demonstrated adequate fit to the data:
chi-square(df) = X.XX, p = .XXX; CFI = .XX; TLI = .XX; RMSEA = .XXX
[90% CI: .XXX, .XXX]; SRMR = .XXX.Model Modification and Comparison
Modification Indices
# Show top modification indices
mi <- modindices(fit, sort = TRUE)
head(mi, 10)
# Common modifications:
# - Allow error covariances between similarly-worded items
# - Add cross-loadings (if theoretically justified)
# - Remove non-significant pathsModel Comparison
# Compare nested models using chi-square difference test
fit1 <- sem(model1, data = mydata) # More constrained
fit2 <- sem(model2, data = mydata) # Less constrained
anova(fit1, fit2) # Chi-square difference test
# For non-nested models, compare AIC/BIC
fitMeasures(fit1, c("aic", "bic"))
fitMeasures(fit2, c("aic", "bic"))Common Pitfalls
| Issue | Problem | Solution |
|---|---|---|
| Small sample size | Unstable estimates, poor fit | Minimum N = 200, or 10-20 per parameter |
| Too many parameters | Overfitting, non-convergence | Simplify model, use parceling |
| Non-normal data | Biased standard errors | Use MLR estimator or bootstrapping |
| Ignoring missing data | Biased results | Use FIML (full information maximum likelihood) |
| Data-driven respecification | Capitalizing on chance | Cross-validate with holdout sample |
| Conflating fit with truth | Good fit does not mean correct model | Consider equivalent/alternative models |
Assumptions and Diagnostics
- Multivariate normality: Check with Mardia's test; use robust estimators (MLR) if violated
- Linearity: SEM assumes linear relationships between variables
- No multicollinearity: Correlations between latent variables should not exceed 0.85
- Sufficient sample size: Rule of thumb: N >= 200 or 10-20 observations per estimated parameter
- Correct model specification: Omitted variables can bias all estimates
# Check multivariate normality
library(MVN)
mvn(mydata[, c("mot1", "mot2", "mot3", "se1", "se2", "se3")],
mvnTest = "mardia")
# Use robust estimation if non-normal
fit_robust <- sem(sem_model, data = mydata, estimator = "MLR")© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/analysis/statistics/sem-guide of wentorai/research-plugins.
Open the folder on GitHubat commit bf44b3c
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Sem Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sem Guide this skillwentorai/research-plugins | 298 | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Pass | MIT | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.6k | 18 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Chart Visualizationbytedance/deer-flow | 83k | 2 repos | ~840 | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Sem Guide
What does Sem Guide do?
Structural equation modeling with latent variables guide. An agent skill from wentorai/research-plugins. Sem Guide is an agent skill from wentorai/research-plugins.
When should I use Sem Guide?
Sem Guide fits situations like: data & Analytics work in your project.
How do I install Sem Guide in Claude Code?
Run `npx skills add wentorai/research-plugins --skill sem-guide -a claude-code`. Or copy the skill folder (skills/analysis/statistics/sem-guide in wentorai/research-plugins) into .claude/skills/sem-guide in your project. Claude Code loads it when a task matches its description.
How do I install Sem Guide in Codex?
Run `npx skills add wentorai/research-plugins --skill sem-guide -a codex`. Or copy the skill folder (skills/analysis/statistics/sem-guide in wentorai/research-plugins) into .agents/skills/sem-guide in your project. Codex loads it when a task matches its description.
Can I use Sem Guide in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill sem-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sem-guide, .gemini/skills/sem-guide, .github/skills/sem-guide and .opencode/skills/sem-guide in your project.
What does Sem Guide need to run?
SKILL.md names no scripts, command-line tools or credentials: Sem Guide is instructions for the agent only. Our summary lists: Python 3.
Does Sem Guide access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Sem Guide safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Sem Guide use?
Sem Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sem Guide use?
About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Sem Guide?
Skills that share tags, products or a category with Sem Guide: Exploratory Data Analysis (spacering-net/codeg, 3.8k stars), Matplotlib (zLanqing/codex-claude-academic-skills, 4.6k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars) and Chart Visualization (bytedance/deer-flow, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sem Guide?
wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.
Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.