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Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
Passed 19 of 21 test cases with the skill, and 5 of 21 without it.
and 12 more in the full report
15 fixed5 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am reviewing Chinese-language documentation for SSCI/CSSCI polishing tools in this repository. Which document contains the dedicated Chinese collection guide?
| Checked | Without skill | With skill |
|---|---|---|
| Directs to docs/CONTENT_ZH.md or README-zh-CN.md | Missed | Passed |
The instructions your model reads when the skill loads.
Use this root skill when the full AERS repository has been installed as a single skill folder. Treat it as a router and catalog, not as a request to load every vendored SKILL.md.
The catalog holds 1,096 skills across 76 vendored collections. Never read them all — route to one, then load only that skill's SKILL.md.
skills/69-Paper-WorkFlow/ or the skills/00* flagship analysis skills — skills/00-Full-empirical-analysis-skill_StatsPAI/ (StatsPAI), skills/00.1-Full-empirical-analysis-skill_Python/ (Python), skills/00.2-Full-empirical-analysis-skill_Stata/ (Stata), skills/00.3-Full-empirical-analysis-skill_R/ (R). Note the StatsPAI flagship has no dot in its prefix, so a skills/00.* glob misses it.catalog/skills.json / docs/TAXONOMY.md.skills/50-brycewang-aer-skills/.docs/SKILL_CATALOG.md and docs/GOLDEN_WORKFLOWS.md to choose a focused skill.skills/48-de-AIGC-skills/ or nearby writing skills in the catalog.SKILL.md, then follow its progressive-disclosure instructions for references/, scripts/, assets/, or templates.catalog/skills.json first (has path, name, description, line_count, and a globally-unique qualified_name), then docs/SKILL_CATALOG.md. For richer filtering (topic tags, quality_score, license, commercial_use), use catalog/skills-enriched.json. Avoid broad recursive reads of skills/.
Both catalog JSON files are large (roughly 1 MB / 20k lines each) — query them instead of reading them whole. Example:
python3 -c "import json; [print(s['qualified_name'], '->', s['path']) for s in json.load(open('catalog/skills.json'))['skills'] if 'synthetic control' in (s['name'] + ' ' + s['description']).lower()]"
A plain grep -in "synthetic control" catalog/skills.json works too when a rough match is enough.
docs/INSTALL.md for Codex-style copy installs and INSTALL.md for Claude Code marketplace/plugin installs.git status inside skills/69-Paper-WorkFlow/ (a git submodule) before touching it.Match the user's identification strategy or task to a starting collection, then confirm against catalog/skills.json.
This table is a shortcut to the most common starting points, not a complete index — it names fewer than half of the vendored collections, and the rest are reachable only through catalog/skills.json. A task missing from this table is not a task without a skill: fall through to step 3 and search the catalog before concluding nothing matches.
| Task / method | Start here |
|---|---|
| Full paper pipeline (orchestrator) | skills/69-Paper-WorkFlow/ |
| Agent-native causal analysis (one call runs DiD / RD / IV / SCM / DML with automatic robustness gates) | skills/00-Full-empirical-analysis-skill_StatsPAI/ |
| DiD / staggered DiD / event study | skills/50-brycewang-aer-skills/, skills/10-Jill0099-causal-inference-mixtape/, skills/13-scunning1975-MixtapeTools/ |
| Instrumental variables (IV) | skills/50-brycewang-aer-skills/, skills/40-py-econometrics-pyfixest/ |
| Regression discontinuity (RDD) | skills/50-brycewang-aer-skills/, skills/10-Jill0099-causal-inference-mixtape/ |
| Synthetic control (SCM) | skills/50-brycewang-aer-skills/, skills/13-scunning1975-MixtapeTools/ |
test cases passed, vs 5 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.