skills/25-HosungYou-Diverga/skills/g2/SKILL.md
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
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research g2Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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
Academic Abstract Writing
Plain Language Summary
Media Materials
Social Media Content
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]
---
**Tweet 4/5** (Results)
What did we find?
✅ [Result 1]
✅ [Result 2]
✅ [Result 3]
---
**Tweet 5/5** (Implications + CTA)
Why does this matter?
[Practical/theoretical implications]
Full paper 👉 [Link]
Questions? Comment below! 💬
---
### 6. LinkedIn Post
**[Professional tone hook]**
[Research background and motivation. 2-3 sentences]
[Core findings summary. 3-4 sentences]
**Key Implications:**
• [Implication 1]
• [Implication 2]
• [Implication 3]
[Suggestions for practice/field. 2 sentences]
Paper link: [URL]
#Research #[Field] #[Keyword]
---
### 7. Graphical Abstract Concept
**Components**:
┌─────────────────────────────────────────┐ │ [Research title (brief)] │ ├─────────────────────────────────────────┤ │ │ │ [Research question] │ │ ↓ │ │ [Methods icon/diagram] │ │ ↓ │ │ [Core results visualization] │ │ ↓ │ │ [Conclusion/implications] │ │ │ ├─────────────────────────────────────────┤ │ [Author] | [Journal] | [DOI] │ └─────────────────────────────────────────┘
**Recommended visual elements**:
- [Icon suggestion 1]
- [Icon suggestion 2]
- [Graph type suggestion]
---
### 8. Elevator Pitch (30 seconds)
"We studied [topic].
Analyzing [participants/data],
we discovered [core finding].
These results have important implications for [implications]."
You are a science communication expert.
Please create materials to communicate the following research findings to various audiences:
[Research findings]: {results}
[Target audience]: {audience}
Tasks to perform:
1. Extract core messages (3)
- Most important finding
- Practical/theoretical implications
- What readers should remember
2. Audience-specific materials
[Academic Abstract] (250 words)
- Background, objective, methods, results, conclusion structure
[Plain Language Summary] (150 words)
- Without technical jargon
- Emphasize "Why does it matter?"
[Press Release] (300 words)
- Newsworthy headline
- Researcher quote
- Reader relevance
[Twitter/X Thread] (5 tweets)
- Each within 280 characters
- Appropriate emoji use
- Include hashtags
[LinkedIn Post]
- Professional tone
- Emphasize practical implications
3. Visual abstract concept
- Main components
- Recommended layout
After G2 generates any content, the Humanization Pipeline can be invoked:
┌─────────────────────────────────────────────────────────────┐
│ 📝 Content Generated │
├─────────────────────────────────────────────────────────────┤
│ │
│ G2 Output: [Abstract / Summary / Press Release / etc.] │
│ │
│ AI Pattern Analysis: │
│ • Patterns detected: 12 │
│ • AI probability: ~55% │
│ • High-risk: 3 Medium: 6 Low: 3 │
│ │
│ 🟠 CHECKPOINT: CP_HUMANIZATION_REVIEW │
│ │
│ Would you like to humanize before export? │
│ │
│ [A] Humanize (Conservative) │
│ [B] Humanize (Balanced) ⭐ Recommended │
│ [C] Humanize (Aggressive) │
│ [D] View detailed report │
│ [E] Keep original │
│ │
└─────────────────────────────────────────────────────────────┘
"Generate abstract with humanization"
→ G2 generates → G5 analyzes → Checkpoint → G6 transforms
"Create summary (humanize: balanced)"
→ Specifies mode, skips mode selection
"Write press release (skip humanization)"
→ G2 generates → Direct output (no pipeline)
"Generate Twitter thread (humanize: aggressive)"
→ Social media benefits from aggressive mode
| Output Type | Recommended Mode | Rationale | |-------------|------------------|-----------| | Academic Abstract | Conservative | Preserve scholarly precision | | Plain Language Summary | Balanced | Natural but accurate | | Press Release | Balanced | Professional yet accessible | | Twitter/X Thread | Aggressive | Maximum naturalness | | LinkedIn Post | Balanced | Professional tone | | Elevator Pitch | Aggressive | Conversational style |
g2_humanization_workflow:
trigger: "After G2 output generation"
default: "Show checkpoint"
options:
auto_humanize: false # Require user approval
default_mode: "balanced"
skip_if_low_ai: true # Skip if AI probability < 25%
preservation:
- "All research findings"
- "All citations"
- "Key messages"
- "Target audience adaptations"
When generating Word (.docx) documents that contain mathematical equations (e.g., for journal submission),
use the latex2omml package to render LaTeX as native Word equations (OMML).
from docx import Document
from latex2omml import add_display_equation, add_inline_equation
doc = Document()
# Display equation (centered, own line)
p = doc.add_paragraph()
add_display_equation(p, r"\frac{a^2 + b^2}{c}")
# Inline equation (within text)
p = doc.add_paragraph()
p.add_run("The formula ")
add_inline_equation(p, r"E = mc^{2}")
p.add_run(" is well known.")
doc.save("paper.docx")
When converting manuscripts from Markdown/LaTeX to Word:
$$...$$ on its own line): Parse LaTeX, call add_display_equation(paragraph, latex)$...$ within text): Split text at $ delimiters, alternate between p.add_run(text) and add_inline_equation(p, latex)a/b instead of proper fraction)| Construct | Example | OMML Element |
|-----------|---------|-------------|
| Fractions | \frac{a}{b} | Stacked fraction |
| Sub/superscripts | x_{i}^{2} | Sub-superscript |
| Greek letters | \alpha, \Omega | Unicode Greek |
| Text mode | \text{hello} | Plain text run |
| Accents | \hat{x}, \bar{x} | Accent element |
| Sums/products | \sum_{i=1}^{N} | N-ary with limits |
| Functions | \log(x), \sin(x) | Function element |
| Square roots | \sqrt{x}, \sqrt[3]{x} | Radical |
For CHB, IJHCS, C&E submissions, include before References:
required_elements:
highlights: "3-5 items, max 85 chars each"
data_availability: "Data available at [URL]"
credit_statement: "Author: Conceptualization, Methodology, ..."
ai_disclosure: "AI tools used: [description]"
competing_interests: "The author declares no competing interests."
APA 7th formatting: Times New Roman 12pt, double-spaced, 1" margins, hanging indent references.
The latex2omml package is available at packages/latex2omml/. It is a pure Python
recursive-descent parser with no external dependencies beyond lxml and python-docx.
No Pandoc, MS Office XSLT, or commercial libraries required.
Before generating Word equations, use G5-AcademicStyleAuditor to validate LaTeX syntax (Category 7: LaTeX Syntax Patterns X1-X6).
../../research-coordinator/core/vs-engine.md../../research-coordinator/core/t-score-dynamic.md../../research-coordinator/references/creativity-mechanisms.md../../research-coordinator/core/project-state.md../../research-coordinator/core/pipeline-templates.md../../research-coordinator/core/integration-hub.md../../research-coordinator/core/guided-wizard.md../../research-coordinator/core/auto-documentation.mddevelopment
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.