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The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill slfgThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
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.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
Showing SKILL.md
Based on SOC occupation classification
| name | slfg |
| description | Full autonomous research workflow using swarm mode for parallel execution |
| argument-hint | [research task, estimation problem, or methodological improvement] |
| disable-model-invocation | true |
Swarm-enabled LFG. Run these steps in order, parallelizing where indicated. Do not stop between steps — complete every step through to the end.
/workflows:brainstorm $ARGUMENTS
Gate: must produce a file in docs/brainstorms/ before proceeding.
/workflows:plan
Gate: must produce a file in docs/plans/ before proceeding.
/workflows:work — Use swarm mode: Break the plan into independent tasks and launch parallel subagents via Task tool to build them concurrently. Each subagent handles one task from the plan. See references/orchestration-patterns.md for parallel dispatch patterns.
Gate: must produce at least one code change (committed or staged) before proceeding. If work fails with no changes, stop and report the failure.
After work completes, launch steps 4 and 5 as parallel swarm agents (both only need completed code to operate):
/workflows:review — spawn as background Task agent/workflows:compound — spawn as background Task agentWait for both to complete before finishing.
When all steps are done, output:
Research workflow complete.
Brainstorm: [brainstorm file path]
Plan: [plan file path]
Work: [summary of implementation]
Review: [summary of findings]
Documentation: [docs/solutions/ path if created]
Start with step 1 now.