Auto-Empirical Research Skills (AERS)

Repository of reusable agent skills and research-workflow guidance for social-science empirical work, including literature review, data preparation, identification strategies, estimation and robustness checks.

Workflow level n/a Source Repository-documented Outcome Not empirically evaluated here

Evidence & verification

Source level

Repository-documented
Grounded in a maintainer repository/README. Availability, stars and forks do not establish research benefit or correctness.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Partial README documentation review; capabilities attributed to maintainers, not demonstrated by this editorial review.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Offers reusable procedural guidance for empirical social-science analysis; researchers still determine identification assumptions, robustness and appropriate reporting.

Getting started

Untested editorial guidance: use the README stage map to select one estimation or robustness skill, inspect its SKILL.md and dependencies, and test it on public data while checking assumptions and outputs. Do not use detector-evasion features to conceal AI assistance.

Maintenance

README retrieved on 2026-10-17; future maintenance not verified.

License

Consult repository and each dependency/data licence; no independent legal audit.

Workflow & human oversight

Workflow level left unspecified for this resource-level listing. Researchers choose scope, inspect inputs and methods, and verify generated outputs before reuse.

Workflow level is not an evidence score; higher is not better evidence.

Approach

The reviewed README maps research stages to skill collections and describes Claude Code plugin installation or copying an individual SKILL.md-bearing folder. It includes Python, R and Stata empirical-analysis routes and a coordinating workflow; this review covers the collection, not each linked dependency.

Limitations

Partial README review, not a skill-by-skill, benchmark or security audit. Queue marketing counts differ from the current README and are not repeated as verified inventory. Claimed institutional affiliations, automated paper quality and time savings are unverified. Some advertised components aim to evade AI detection or remove provenance; they are not recommended. Preserve AI-use disclosure, source attribution and research integrity.

Reproducibility

Primary README source check only; no installation, execution, security test or performance reproduction.

Adoption requirements

A compatible agent environment and task-specific statistical software/data access; inspect each selected skill, upstream licence, executable content and tool permissions before installation. Use project-local scope and protect sensitive research data.

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