Jcr Data Analysis
franklee16/academic-research-skills
A skill your agent uses when running and reporting the evidence for a Journal of Consumer Research (JCR) manuscript — process evidence (mediation/moderation) for behavioral experiments, or…
A skill your agent uses when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation…
Install Hrm Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills hrm-data-analysis --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/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Human-Resource-Management-Skills/skills/hrm-data-analysis .claude/skills/hrm-data-analysis && 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 "hrm-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysis into .claude/skills/hrm-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hrm-data-analysis", 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/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysisType 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 brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills hrm-data-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Human-Resource-Management-Skills/skills/hrm-data-analysis .agents/skills/hrm-data-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hrm-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysis into .agents/skills/hrm-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hrm-data-analysis", 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 brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills hrm-data-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Human-Resource-Management-Skills/skills/hrm-data-analysis .cursor/skills/hrm-data-analysis && 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 "hrm-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysis into .cursor/skills/hrm-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hrm-data-analysis", 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/brycewang-stanford/Awesome-Journal-Skills.git --path Human-Resource-Management-Skills/skills/hrm-data-analysis--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 brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills hrm-data-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Human-Resource-Management-Skills/skills/hrm-data-analysis .gemini/skills/hrm-data-analysis && 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 "hrm-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysis into .gemini/skills/hrm-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hrm-data-analysis", 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 brycewang-stanford/Awesome-Journal-Skills hrm-data-analysisInstalls 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 brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Human-Resource-Management-Skills/skills/hrm-data-analysis .github/skills/hrm-data-analysis && 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 "hrm-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysis into .github/skills/hrm-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hrm-data-analysis", 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 brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills hrm-data-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Human-Resource-Management-Skills/skills/hrm-data-analysis .opencode/skills/hrm-data-analysis && 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 "hrm-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Human-Resource-Management-Skills/skills/hrm-data-analysis into .opencode/skills/hrm-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hrm-data-analysis", 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
hrm-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 685 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation…
- Estimation and analysis are the bottleneck for a Human Resource Management (Wiley HRM) manuscript — fitting HLM/SEM
- SKILL.md covers When to trigger, Match the estimator to the…, Multilevel and SEM discipline… and Robustness and transparency…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Testing mediation and cross-level moderation
What it does
Hrm Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation, defending aggregation, and qualitative coding rigor. Runs and validates the analysis; it does not design the study (hrm-methods).
Its SKILL.md is about 1.7k 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, covering Data analysis, Dispute resolution and Recruiting and HR. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Estimation and analysis are the bottleneck for a Human Resource Management (Wiley HRM) manuscript — fitting HLM/SEM
- Testing mediation and cross-level moderation
- Defending aggregation
- Qualitative coding rigor
Example prompts
- “/hrm-data-analysis”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. 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.
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
Hrm Data Analysis loads about 1.7k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 685 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 brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 685 words, ~1,703 tokens.
Download SKILL.mdSave it as
.claude/skills/hrm-data-analysis/SKILL.md (or your agent's skills folder).- name
- hrm-data-analysis
- description
- Use when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation, defending aggregation, and qualitative coding rigor. Runs and validates the analysis; it does not design the study (hrm-methods).
Data Analysis (hrm-data-analysis)
When to trigger
- You have nested data (employees in units/firms) and need the right multilevel model
- A mediation/moderation hypothesis needs a defensible test (not just a significant indirect effect)
- A measurement model (CFA) must establish discriminant validity before structural tests
- A reviewer challenges the aggregation, the estimator, or asks for robustness
- Qualitative data need a transparent, auditable coding and trustworthiness account
Match the estimator to the data structure
| Data / claim | Estimator | What referees will check |
|---|---|---|
| Individuals nested in units; cross-level effects | HLM / mixed models (random intercepts/slopes) | Variance decomposition; ICC justifying multilevel; correct level for each predictor |
| Latent constructs + structural paths | SEM (with measurement model first) | CFA fit (CFI/TLI ≥ ~.95, RMSEA ≤ ~.06, SRMR ≤ ~.08); discriminant validity (AVE > shared variance) |
| Mediation (the HR black box) | Bootstrap indirect effect CIs; multilevel mediation if cross-level | Theorized mechanism, not inference from significance alone; 1-1-1 vs. 2-1-1 structure stated |
| Moderation / interaction | Product terms; simple slopes; interaction plot | Centering; region of significance; power; theory for the slope change |
| HR system → firm performance (panel) | Fixed-effects / DiD / IV | Endogeneity strategy; clustered SEs; pre-trends if DiD |
| Meta-analysis | Random-effects (e.g., Hunter–Schmidt / HVZ) | Coding reliability; heterogeneity (I², Q); publication-bias checks; moderator analysis |
Multilevel and SEM discipline (HRM's bread and butter)
- Justify going multilevel. Report ICC(1)/ICC(2); if essentially zero between-unit variance, a multilevel model is not warranted — say so.
- Group-mean center lower-level predictors when testing within-unit effects; grand-mean center for cross-level; state which and why (the choice changes the meaning of the coefficient).
- Measurement before structure. Run the CFA and establish discriminant validity before interpreting structural paths; a saturated SEM with a poor measurement model is not evidence.
- Mediation is a theory claim. Report the indirect effect with bias-corrected bootstrap CIs, but the mechanism must have been theorized a priori; do not back-fill the mechanism from a significant indirect path.
- Aggregation evidence travels with the analysis. r_wg, ICC(1), ICC(2) belong in the results, tied to the composition model from
hrm-methods.
Robustness and transparency HRM expects
- Report alternative specifications (controls in/out, alternative operationalizations of the HR system) and show the focal effect is stable.
- Address endogeneity for adoption/performance claims (FE, DiD, IV) and report clustered standard errors at the assignment level.
- Provide effect sizes in practitioner-meaningful terms (e.g., a 1-SD increase in HPWS is associated with X% higher productivity) — HRM rewards results an HR leader can act on.
- For qualitative work, give a transparent audit trail: data structure (first-order codes → second-order themes → aggregate dimensions), coding reliability or consensus process, and trustworthiness (member checks, triangulation).
- Follow Wiley's data-availability policy: include a data-availability statement and prepare materials for sharing where permitted (检索于 2026-06；以官网为准).
Show full SKILL.md (255 more words)Show less
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. HRM is empirical HR — multilevel survey data, field experiments, and panels; multilevel inference and many-outcome corrections matter most.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Checklist
- Estimator matches the data structure (nesting modeled; latent constructs in SEM)
- ICC reported and the multilevel choice justified
- CFA fit + discriminant validity established before structural interpretation
- Centering choice stated and matched to the effect being tested
- Indirect effects via bootstrap CIs; mechanism theorized a priori
- Endogeneity addressed; SEs clustered at the right level
- Effect sizes translated into practitioner-meaningful magnitudes
- Qualitative: transparent data structure + trustworthiness account
- Data-availability statement prepared per Wiley policy
Anti-patterns
- OLS on nested data: ignoring clustering and inflating significance
- Structure before measurement: interpreting SEM paths with a failing CFA
- Mediation by significance: claiming a mechanism from a significant indirect effect never theorized
- Centering silence: not stating group- vs. grand-mean centering in multilevel models
- p-value-only results: no effect sizes, no practitioner translation
- Aggregation without evidence: a unit-level construct with no r_wg/ICC
- Opaque qualitative coding: themes with no audit trail or reliability account
Output format
【Journal】Human Resource Management (Wiley "HRM")
【Skill】hrm-data-analysis
【Data structure】nested / latent-SEM / panel / meta / qualitative
【Estimator】HLM / SEM / FE-DiD-IV / bootstrap mediation / RE meta
【Measurement】CFA fit + discriminant validity status
【Multilevel】ICC reported; centering choice
【Mediation/moderation】indirect-effect CIs; interaction plot; a-priori mechanism?
【Robustness】alt specs / endogeneity / clustered SEs
【Practitioner magnitude】effect translated to an actionable number
【Data policy】availability statement prepared? 检索于 2026-06；以官网为准
【Next skill】hrm-contribution-framing© brycewang-stanford, 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 Human-Resource-Management-Skills/skills/hrm-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Hrm Data Analysis 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 |
|---|---|---|---|---|---|---|
| Hrm Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Jcr Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~1.2k | Automated safety check: Pass | None | |
| Scenario Experiment Benchmark MiningDrchronx/ai-agent-research-starter-kit | 135 | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Exploratory Data Analysisspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 83k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Exploratory Data AnalysisOleafly/Oleafly | 206 | 3 repos | ~3.4k | Automated safety check: Notes | MIT |
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Questions about Hrm Data Analysis
What does Hrm Data Analysis do?
A skill your agent uses when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation…. Hrm Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation, defending aggregation, and qualitative coding rigor.
When should I use Hrm Data Analysis?
Hrm Data Analysis fits situations like: estimation and analysis are the bottleneck for a Human Resource Management (Wiley HRM) manuscript — fitting HLM/SEM; testing mediation and cross-level moderation; defending aggregation; qualitative coding rigor.
How do I install Hrm Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a claude-code`. Or copy the skill folder (Human-Resource-Management-Skills/skills/hrm-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/hrm-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Hrm Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a codex`. Or copy the skill folder (Human-Resource-Management-Skills/skills/hrm-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/hrm-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Hrm Data Analysis 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 brycewang-stanford/Awesome-Journal-Skills --skill hrm-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hrm-data-analysis, .gemini/skills/hrm-data-analysis, .github/skills/hrm-data-analysis and .opencode/skills/hrm-data-analysis in your project.
What does Hrm Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Hrm Data Analysis is instructions for the agent only.
Does Hrm Data Analysis 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 Hrm Data Analysis 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 Hrm Data Analysis use?
Hrm Data Analysis 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 Hrm Data Analysis use?
About 1.7k tokens (SKILL.md is roughly 6.8k 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 Hrm Data Analysis?
Skills that share tags, products or a category with Hrm Data Analysis: Jcr Data Analysis (franklee16/academic-research-skills, 223 stars), Scenario Experiment Benchmark Mining (Drchronx/ai-agent-research-starter-kit, 135 stars), Exploratory Data Analysis (spacering-net/codeg, 3.8k stars) and Excel and CSV Data Analysis (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 Hrm Data Analysis?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.