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- brycewang-stanford/Auto-Empirical-Research-Skills
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- 2026年6月3日 07:26
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安装方式
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
检查来源文件
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
菜单
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill figure-table-audit命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
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.
正在显示 SKILL.md
基于 SOC 职业分类
| name | figure-table-audit |
| description | Audit figures, tables, captions, cross-references, and statistical notes. |
| argument-hint | [path to manuscript, figures, tables, SI, or compiled PDF; include target journal if known] |
This is an original Open Science Skills workflow for manuscript QA. It remixes general figure/table and citation-compliance ideas from Cheng-I Wu's Academic Research Skills for Claude Code (CC BY-NC 4.0), but is rewritten for open-science social-science manuscripts. It is not a visual hallucination engine: when a claim requires reading plotted values from an image, prefer source data or mark the issue as needing author verification.
This is the end-stage auditor. For figure design and production guidance during drafting, use the figures skill; for table design, use the tables skill. Run figure-table-audit once the figure and table set is stable and you are preparing for submission.
Identify:
If only a PDF is available, state that cross-reference and value checks are lower confidence.
Build an inventory with:
Check:
For each figure/table used to support a substantive claim:
Do not infer exact values by eyeballing a plot unless the figure encodes labeled values. If source data are unavailable, write VISUAL READ ONLY - AUTHOR VERIFY.
Captions and table notes should let a reader understand the evidence without hunting:
For conjoint, list-experiment, topic-modeling, LLM-classification, and OCR studies, invoke or recommend the relevant sibling skill when table/figure interpretation depends on method-specific standards.
Flag:
Check whether:
Produce a Figure and Table Audit Report:
# Figure and Table Audit Report
Scope:
Inputs checked:
Build/source status:
Summary: <N blocking, N recommended, N minor, N author-verification>
## Inventory
| ID | Path/location | Caption/title | First callout | Source/script |
## Blocking Issues
| Location | Figure/table | Issue | Evidence | Fix |
## Recommended Fixes
| Location | Figure/table | Issue | Fix |
## Minor / Production Issues
| Figure/table | Issue | Fix |
## Author Verification Needed
| Figure/table | Why verification is needed |
## Readiness Checklist
| Dimension | PASS/FAIL/PARTIAL/NA | Notes |
Severity: