Auto Empirical Research Skills

Auto-Empirical Research Skills Router

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.

Workflow

1. Classify the user's empirical-research task by stage, then load the single best-matching skill:
   • Full pipeline or orchestration: start with 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.
   • Causal inference and econometrics: pick by method from the table below, or search catalog/skills.json / docs/TAXONOMY.md.
   • AER or top economics journal work: start with skills/50-brycewang-aer-skills/.
   • Replication, citation, or peer review: use docs/SKILL_CATALOG.md and docs/GOLDEN_WORKFLOWS.md to choose a focused skill.
   • Academic de-AIGC (English or Chinese) or academic rewriting: start with skills/48-de-AIGC-skills/ or nearby writing skills in the catalog.
2. Read only the selected child skill's SKILL.md, then follow its progressive-disclosure instructions for references/, scripts/, assets/, or templates.
3. If no child skill clearly matches, inspect 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.

4. For installation help, use docs/INSTALL.md for Codex-style copy installs and INSTALL.md for Claude Code marketplace/plugin installs.
5. If editing this repository, keep parent and nested repos separate. In particular, inspect git status inside skills/69-Paper-WorkFlow/ (a git submodule) before touching it.

Method → where to start

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.

Full-pipeline trigger

If the user is asking for a complete empirical paper from idea to submission, route to skills/69-Paper-WorkFlow/. The orchestrator loads the right skill at the right stage and stops for human decisions at the two hard gates (Method Gate after Stage 3, Draft Quality Gate after Stage 7).

Trigger phrases (any one is enough to dispatch to the orchestrator):

• /paper-workflow
• "帮我写一篇实证论文"
• "从选题到投稿"
• "end-to-end empirical paper"
• "完整复现"
• "from proposal to submission"

The orchestrator is not the right entry point for a single-task ask (e.g. "fit a DiD", "recode this variable", "write a referee report") — those are listed in the Method → where to start table above.

Coverage Notes

• skills/69-Paper-WorkFlow/ is a git submodule. If its folder is empty, the copy or clone skipped submodules (git submodule update --init fixes a clone); fall back to the skills/00* flagship pipeline skills, which are vendored directly.
• The vendored ARIS collection (skills/42-wanshuiyin-ARIS/) also ships its skill set as OpenAI Codex CLI runtime ports (skills-codex* subtrees). Those stay on disk but are excluded from catalog/skills.json (see scripts/skill_discovery.py) — route Claude agents to the primary skills/ tree only.

Install Notes

• Whole-repo imports are supported by this root SKILL.md as a lightweight compatibility entry point.
• Individual skill installs are still preferred when a runtime expects one folder per skill. Copy the folder that directly contains the target SKILL.md.
• Do not copy the repository root into a runtime and expect every child skill to become individually registered unless that runtime explicitly supports recursive skill discovery.
• Name collisions: the catalog contains 47 bare names shared across collections (e.g. data-analysis, lit-review, proofread). When a runtime registers skills by flat name, install one collection at a time, or disambiguate with the globally-unique qualified_name field in catalog/skills.json (<collection>::<name>, e.g. 12-pedrohcgs-claude-code-my-workflow::data-analysis), or the full skills/<collection>/.../SKILL.md path.

Key Files

• catalog/skills.json: machine-readable list of vendored skills.
• catalog/skills-enriched.json: same list plus tags, quality_score, license, and commercial_use for filtering.
• docs/SKILL_CATALOG.md: human-readable skill index.
• docs/TAXONOMY.md: task and method taxonomy.
• docs/GOLDEN_WORKFLOWS.md: ready-to-use empirical-research prompts.
• docs/INSTALL.md: runtime installation guidance for single-skill and whole-repo use.
• docs/CONTENT_ZH.md and README-zh-CN.md: Chinese-language collection index and entry point. Prefer these when the user is working in Chinese — several collections (de-AIGC, SSCI/CSSCI polishing, Chinese academic writing) are documented there in more detail than in the English docs.