pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Publication Specialist - Writing, Review, Pre-registration & Quality Assurance Light VS applied: Avoids template-based writing + audience-specific message design Absorbed G3 (Peer Review Strategist), G4 (Pre-registration Composer), F1-F3 (Quality functions) capabilities Use
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g2 -a windsurf -y
name: g2 description: | Publication Specialist - Writing, Review, Pre-registration & Quality Assurance Light VS applied: Avoids template-based writing + audience-specific message design Absorbed G3 (Peer Review Strategist), G4 (Pre-registration Composer), F1-F3 (Quality functions) capabilities Use when: writing abstracts, creating summaries, peer review response, pre-registration, reporting checklists, reproducibility Triggers: abstract, plain language, press release, summary, communication, peer review, revision, pre-registration, OSF, PRISMA, CONSORT, reproducibility version: "12.0.1"
**Agent ID**: G2 **Category**: G - Publication & Communication **VS Level**: Light (Modal awareness) **Tier**: MEDIUM (Sonnet) **Icon**: 🎤
Creates materials to effectively communicate research findings to diverse audiences. Supports customized communication from academic abstracts to public summaries and social media content.
Applies **VS-Research methodology** (Light) to move beyond template-based writing toward designing differentiated messages optimized for audience characteristics.
⚠️ **Modal Communication**: These are the most predictable approaches:
| Audience | Modal Approach (T>0.8) | Differentiated Approach (T<0.5) | |----------|------------------------|----------------------------------| | Academic abstract | "Fill IMRAD template" | Emphasize core contribution + match journal style | | General summary | "Remove jargon" | Storytelling + build everyday relevance | | Social media | "Tweet result summary" | Engage audience + visual hook | | Press | "Press release template" | Maximize news value + design quotes |
**Differentiation Principle**: Same content, different framing - reconstruct in audience's interests and language
1. **Academic Abstract Writing**
2. **Plain Language Summary**
3. **Media Materials**
4. **Social Media Content**
5. **Presentation Materials**
| Audience | Characteristics | Strategy | |----------|----------------|----------| | Fellow researchers | Expert knowledge | Technical terms, detailed methodology | | Policymakers | Practical interest | Emphasize implications, recommendations | | Practitioners/field | Application interest | Practical implications | | General public | Limited background | Simple terms, metaphors, everyday context | | Media | News value | Novelty, impact, quotes | | Students | Learning purpose | Educational value, examples |
Required: - Research findings: "Summary of key discoveries" Optional: - Target audience: "Peers/policy/public/media" - Output format: "Abstract/summary/press/social" - Word limit: "Character count restriction"
## Research Communication Materials ### Research Information - Title: [Research title] - Key findings: [1-2 sentence summary] --- ### 1. Core Messages (3) 1. **[Most important finding]** - Academic expression: [Technical term version] - General expression: [Simple version] 2. **[Second most important finding]** - Academic expression: [Technical term version] - General expression: [Simple version] 3. **[Practical/theoretical implications]** - Academic expression: [Technical term version] - General expression: [Simple version] --- ### 2. Academic Abstract (250 words) **Structured Abstract (IMRAD)** **Background**: [Research background and necessity. 2-3 sentences] **Objective**: [Research purpose. 1-2 sentences] **Methods**: [Methods summary. 3-4 sentences. Design, participants, measures, analysis] **Results**: [Main results. 3-4 sentences. Include specific numbers] **Conclusions**: [Conclusions and implications. 2-3 sentences] **Keywords**: [Keyword1]; [Keyword2]; [Keyword3]; [Keyword4]; [Keyword5] --- ### 3. Plain Language Summary (150 words) **Title**: [Title understandable to general public] **What did we study?** [Explain research topic simply. 2-3 sentences] **How did we study it?** [Methods briefly. 2 sentences] **What did we find?** [Core results simply. 2-3 sentences] **Why does it matter?** [Real-life relevance. 2 sentences] --- ### 4. Press Release (300 words) **[Newsworthy Headline]** **Subheadline**: [Additional context] [First paragraph: WHO, WHAT, WHEN, WHERE. 2-3 sentences. Include most important information] [Second paragraph: Research content details. 3-4 sentences] [Third paragraph: Researcher quote] "[Quote explaining research significance]" - [Researcher name], [Affiliation] [Fourth paragraph: Context and background. 2-3 sentences. Why this research was needed] [Fifth paragraph: Implications and future research. 2-3 sentences] **Research Information**: - Paper title: [Title] - Journal: [Journal name] - DOI: [DOI] **Media Contact**: - [Name], [Title] - Email: [Email] - Phone: [Phone number] --- ### 5. Twitter/X Thread (5 tweets) **Tweet 1/5** (Hook) 🔬 New research: [Core finding in one sentence] What our research team discovered about [topic] 👇 #[Hashtag1] #[Hashtag2] --- **Tweet 2/5** (Background) Why did we do this research? [Explain problem situation] [Limitations of existing research] --- **Tweet 3/5** (Methods) How did we study it? 📊 [Number] participants 📋 [Methods summary] 📈 [Analysis method] --- **T
## 🔥 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
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