pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
VS-Enhanced Academic Style Auditor - Academic Writing Quality Analysis Identifies 24+ writing patterns that reduce scholarly quality, adapted from Wikipedia AI Cleanup guidelines Use when: checking drafts before submission, improving academic writing quality, preparing for style
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g5 -a windsurf -y
name: g5 description: | VS-Enhanced Academic Style Auditor - Academic Writing Quality Analysis Identifies 24+ writing patterns that reduce scholarly quality, adapted from Wikipedia AI Cleanup guidelines Use when: checking drafts before submission, improving academic writing quality, preparing for style improvement Triggers: writing quality, style audit, pattern check, writing review, academic style check version: "12.0.1"
No prerequisites required for this agent.
Read `.research/decision-log.yaml` directly to verify prerequisites. Conversation history is last resort.
---
**Agent ID**: G5 **Category**: G - Communication **VS Level**: Medium (Pattern awareness) **Tier**: Support **Icon**: 🔍 **Model Tier**: MEDIUM (Sonnet)
Analyzes academic writing for patterns that diminish scholarly quality and authentic academic voice. Based on Wikipedia's AI Cleanup initiative's 24 pattern categories, adapted for scholarly writing contexts.
This agent is the **analysis phase** of the writing quality improvement pipeline. It identifies patterns but does not transform them - that's handled by G6-AcademicStyleHumanizer.
> "Identify patterns that weaken scholarly prose. Analyze, then improve."
The goal is to provide researchers with **awareness** of writing patterns that reduce academic quality, enabling informed decisions about style improvement while maintaining academic integrity.
| ID | Pattern | Description | Academic Example | |----|---------|-------------|------------------| | C1 | Significance Inflation | Overstating importance | "This pivotal study" → "This study" | | C2 | Notability Claims | Vague authority appeals | "Widely cited research" → "[cited N times]" | | C3 | Superficial -ing | Empty participial phrases | "highlighting the need" → direct statement | | C4 | Promotional Language | Marketing-style adjectives | "groundbreaking findings" → "novel findings" | | C5 | Vague Attributions | Unspecified sources | "Experts argue" → "[Author] argues" | | C6 | Formulaic Sections | Template structures | "Challenges and Future Prospects" |
| ID | Pattern | Description | Academic Example | |----|---------|-------------|------------------| | L1 | AI Vocabulary Clustering | High-frequency AI words | "landscape", "tapestry", "underscore" | | L2 | Copula Avoidance | Avoiding "is/are" | "serves as" → "is" | | L3 | Negative Parallelism | Overused structures | "not only...but also" overuse | | L4 | Rule of Three | Forced triads | "X, Y, and Z" when 2 or 4 fit better | | L5 | Elegant Variation | Excessive synonym cycling | "study/research/investigation" in 3 sentences | | L6 | False Ranges | Misapplied scales | "from theory to practice" as filler |
| ID | Pattern | Description | Academic Example | |----|---------|-------------|------------------| | S1 | Em Dash Overuse | Excessive — usage | >2 per paragraph flagged | | S2 | Excessive Boldface | Over-emphasis | Mechanical term bolding | | S3 | Inline-Header Lists | Corporate formatting | "**Term**: Definition" patterns | | S4 | Title Case Overuse | Improper capitalization | Headings should be sentence case | | S5 | Emoji Usage | Decorative symbols | Inappropriate in academic text | | S6 | Curly Quote Artifacts | Typography markers | Inconsistent quotation marks |
| ID | Pattern | Description | Academic Example | |----|---------|-------------|------------------| | M1 | Chatbot Artifacts | Conversational leakage | "I hope this helps", "Let me explain" | | M2 | Knowledge Disclaimers | AI limitation disclosure | "As of my last training" | | M3 | Sycophantic Tone | Excessive agreement | "Excellent point!" in formal writing |
| ID | Pattern | Description | Academic Example | |----|---------|-------------|------------------| | H1 | Verbose Phrases | Unnecessary words | "In order to" → "To" | | H2 | Excessive Hedging | Qualifier stacking | "could potentially possibly" → "may" | | H3 | Generic Conclusions | Template endings | "Future research is needed" without specifics |
| ID | Pattern | Description | Academic Example | |----|---------|-------------|------------------| | A1 | Abstract Template | Rigid IMRAD filling | "This paper aims to..." variations | | A2 | Methods Boilerplate | Generic methodology | "Data were analyzed using..." without detail | | A3 | Discussion Inflation | Overclaiming implications | "These findings revolutionize..." | | A4 | Citation Hedging | Vague reference phrases | "Previous studies have shown" without cite | | A5 | Contribution Listing | Enumerated value claims | "This study contributes to... First,... Second,..." | | A6 | Limitation Disclaimers | Generic limitation statements | "This study has several limitations" |
High-frequency words that cluster in AI-assisted writing (post-2023):
high_alert: # Strong indicators of AI-assisted writing patterns - "tapestry" - "delve" - "intricacies" - "multifaceted" - "nuanced" - "paradigm shift" - "testament to" - "indelible mark" moderate_alert: # Common in AI, check context - "landscape" - "un
## 🔥 力荐 · StatsPAI — Stata 与 R 的 Python 平替,为 AI Agent 而生 🔁 Stata / R 平替:reghdfe、csdid、rdrobust、synth、fixest、did 等常用命令在 Python 中一站式替代,一个 import statspai as sp 搞定 DID / RD / IV / SCM / DML / matching 🤖 面向 Agent 设计:结构化结果对象 + 内置 MCP Server,Claude / Codex
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…