Introduction

Most people writing empirical papers have experienced this state: once data cleaning is finished, the next step is model selection. Descriptive statistics, correlation coefficients, F-tests, Hausman tests, deciding between fixed or random effects—every step requires clear thought before execution. After running the analysis, results must be organized into an Excel report. If a single step in model selection is wrong, the entire chain of results behind it must be redone.

The SkillHub Skill introduced below, “Econometrics and Stata Applications - One-click Workflows for Full Panel Empirical Process,” solidifies these types of judgments into a standard operating procedure (SOP): input the data, follow the workflow, and receive the report.

What is this

One-sentence positioning: It turns three workflows—panel data empirical analysis, causal inference, and regression modeling—into a one-click triggerable process, covering everything from data cleaning to result interpretation and Excel report output.

Producer: Chao Xuan Technology (潮悬科技).
Source Type: Enterprise.
Category: Data Analysis.
Current Version: 1.0.3.
Skill Coordinates: @org-soincd06/econometrics.

Trigger Method: According to official documentation, it triggers when keywords such as “Full empirical workflow,” “Panel empirical,” “Panel data empirical,” “Full causal workflow,” “Full regression workflow,” “One-click report,” “Graduation thesis empirical,” or “All-in-one service” appear in the conversation.

Core Functions

The official description breaks it down into three workflows, all verified capabilities:

  1. Full Panel Data Empirical Workflow: Descriptive statistics, correlation analysis, F-tests, and Hausman tests, finally settling on either a Fixed Effects or Random Effects model.
  2. Full Causal Inference Workflow: Three routes—DID (Difference-in-Differences), PSM (Propensity Score Matching), and IV (Instrumental Variables). The Skill intelligently routes between them, selecting based on data and context rather than requiring the user to choose manually.
  3. Full Regression Modeling Workflow: A process-oriented approach to general regression modeling.

Package Contents: Data cleaning, result interpretation, improvement suggestions, and Excel reports. The official description uses “Get it all done with a package deal” to describe the payment method, indicating this is a bundled capability for a one-time fee, not a free tool.

Installation and Activation

The Skill coordinates are @org-soincd06/econometrics.

Note: At the time of writing, the SkillHub page (https://www.skillhub.cn/skills/org-soincd06/econometrics ) and API address (https://api.skillhub.cn/org-soincd06/econometrics ) both failed to retrieve data. Therefore, I cannot verify the official installation commands, configuration fields, or differences between Cursor / Codex CLI / Claude Code. No speculative commands are provided here; please refer to the actual display on the page.

Verified Configuration Information:

  • The tag indicates requires_api_key=true, meaning an API Key must be configured before activation.
  • The source type is enterprise, indicating an enterprise publishing channel, not a community or individual source.

Page Data: Rating 100,000; Downloads 1,227; Installations 0. The combination of an extremely high rating and zero installations is worth noting. It suggests that there may not have been actual deployment validation yet, or the statistics for installations are not synchronized with downloads.

Typical Usage

Due to the failure to retrieve the official page, no reproducible official example commands were obtained. The usage method is described below based on the workflow sequence outlined in the official introduction. Specific parameters should be based on the output after the Skill is actually triggered.

  1. Prepare Data: Provide the panel data to the Skill. Keep field names and data structures consistent with the paper as much as possible to reduce extra work during cleaning.
  2. Trigger Full Empirical Workflow: Use one of the trigger words mentioned above in the conversation. The Skill will proceed in the order of “Description → Correlation → F-test → Hausman test → Fixed/Random Effects.”
  3. Select Causal Inference Route: When identifying causal effects is required, the Skill intelligently routes between DID, PSM, and IV. The applicability conditions for these three differ: DID relies on the parallel trend between the treatment and control groups; PSM relies on comparability on observable variables; IV requires finding an instrumental variable that affects the outcome only through the treatment variable. The routing result should be used as a basis for judgment, not taken directly as a conclusion.
  4. View Output: The final output includes result interpretation, improvement suggestions, and an Excel report, which can be used directly in the paper’s appendix or main text.

Suitable Scenarios and Considerations

Suitable for: Graduation theses, course empirical projects, and scenarios requiring a quick run-through of panel models with a deliverable report, especially for those not yet proficient in model selection judgment.

Points to note:

  • Intelligent routing does not guarantee correctness. The applicability of DID/PSM/IV depends on whether identification assumptions hold; one must still review based on literature and the data itself.
  • Result interpretation and improvement suggestions are generated by the model. Symbol direction, significance levels, and economic meaning must be manually verified; do not copy them directly into the paper.
  • An API Key is required for activation; confirm the key configuration method before use.
  • Installations are 0, meaning there is currently no public user feedback, and stability and output quality have no third-party verification.
  • Current version is 1.0.3. Future updates may adjust the workflow order or report format.

Conclusion

The value of this Skill lies in solidifying the processes that are prone to error and repetition in empirical research, saving time on repeatedly checking test steps and manually organizing reports. It serves as a process skeleton for the empirical stage of a paper, but the identification assumptions behind model selection still require personal judgment.

Official Address: https://api.skillhub.cn/org-soincd06/econometrics
SkillHub Page: https://www.skillhub.cn/skills/org-soincd06/econometrics