Research Dissemination Automation: Scripts That Generate Launch Packages Automatically

Build Python scripts that generate complete publication packages automatically. Connect Claude to Google Drive for direct file access. Create custom skills—Press Release Specialist, Accessibility Translator, Social Media Strategist.

Research Dissemination Automation: Scripts That Generate Launch Packages Automatically

Publication day arrives. You run a single command. Minutes later, your complete launch package is ready:

This is automated dissemination.

The Automation Opportunity

Manual Dissemination: The Problem

Even with AI assistance, manual dissemination requires:

For a complete launch package, this takes 60-90 minutes of manual orchestration.

Automated Dissemination: The Solution

A single script:

Total time: Minutes of execution + review.

Building the Launch Package Generator

Prerequisites

Installation

pip install anthropic

The Core Script

import anthropic
import os
from datetime import datetime
from pathlib import Path

client = anthropic.Anthropic()

class ResearchDisseminationEngine:
    def __init__(self, project_folder):
        self.project_folder = Path(project_folder)
        self.context = self._load_context()
        self.guardrails = self._load_guardrails()
        self.output_folder = self.project_folder / "dissemination" / datetime.now().strftime("%Y%m%d")
        self.output_folder.mkdir(parents=True, exist_ok=True)

    def _load_context(self):
        context_file = self.project_folder / "context" / "research-brain.md"
        return context_file.read_text() if context_file.exists() else ""

    def _load_guardrails(self):
        guardrails_file = self.project_folder / "context" / "nuance-guardrails.md"
        return guardrails_file.read_text() if guardrails_file.exists() else ""

    def _generate(self, prompt, max_tokens=2048):
        full_prompt = f"""Research Context:
{self.context}

Accuracy Guardrails:
{self.guardrails}

Task:
{prompt}

Important: Stay within the accuracy guardrails. Do not overstate findings."""

        message = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=max_tokens,
            messages=[{"role": "user", "content": full_prompt}]
        )
        return message.content[0].text

    def generate_twitter_thread(self):
        prompt = """Create a Twitter thread (10 tweets) about this research:
        - Tweet 1: Hook + promise
        - Tweets 2-3: The problem addressed
        - Tweets 4-6: Key findings (one per tweet)
        - Tweets 7-8: Why this matters
        - Tweet 9: Important caveats
        - Tweet 10: Call to action + [LINK]

        Use accessible language. No jargon. Include numbering (1/10, etc.)."""

        result = self._generate(prompt)
        output_path = self.output_folder / "twitter-thread.md"
        output_path.write_text(result)
        return result

    def generate_linkedin_article(self):
        prompt = """Create a LinkedIn article (900 words) about this research:
        - Opening hook (why professionals should care)
        - The research question and why it matters
        - What we found (accessible, appropriately hedged)
        - What this means for practice/policy
        - What questions remain
        - Call to action

        Tone: Thoughtful, authoritative, accessible. Include 2-3 subheadings."""

        result = self._generate(prompt, max_tokens=3000)
        output_path = self.output_folder / "linkedin-article.md"
        output_path.write_text(result)
        return result

    def generate_press_release(self):
        prompt = """Create a press release about this research:
        - Headline that would make a journalist click
        - Subhead with secondary hook
        - Lede paragraph: who, what, why it matters
        - Quote from researcher (write in first person)
        - Background context
        - Methods summary (brief, accessible)
        - Notes to Editors section
        - Contact: [CONTACT INFO]

        400-600 words. Accessible to general assignment reporters."""

        result = self._generate(prompt)
        output_path = self.output_folder / "press-release.md"
        output_path.write_text(result)
        return result

    def generate_blog_post(self):
        prompt = """Create a blog post (2000 words) about this research:
        - Engaging introduction
        - The context: what problem or gap?
        - What we did: accessible methodology
        - What we found: findings with implications
        - What this means: practical takeaways
        - What we don't know: limitations
        - Conclusion: the bottom line

        Subheadings every 300-400 words. Short paragraphs."""

        result = self._generate(prompt, max_tokens=4000)
        output_path = self.output_folder / "blog-post.md"
        output_path.write_text(result)
        return result

    def generate_newsletter_blurb(self):
        prompt = """Create a 150-word newsletter blurb:
        - Why this research matters (one sentence)
        - What we found (2-3 sentences)
        - What to do with this (one sentence)
        - [LINK] placeholder

        Compelling enough to click through, valuable even if they don't."""

        result = self._generate(prompt, max_tokens=500)
        output_path = self.output_folder / "newsletter-blurb.md"
        output_path.write_text(result)
        return result

    def generate_all(self):
        print("Generating launch package...")

        print("  - Twitter thread...")
        self.generate_twitter_thread()

        print("  - LinkedIn article...")
        self.generate_linkedin_article()

        print("  - Press release...")
        self.generate_press_release()

        print("  - Blog post...")
        self.generate_blog_post()

        print("  - Newsletter blurb...")
        self.generate_newsletter_blurb()

        print(f"\nLaunch package generated in: {self.output_folder}")
        return self.output_folder


if __name__ == "__main__":
    import sys
    project_folder = sys.argv[1] if len(sys.argv) > 1 else "."
    engine = ResearchDisseminationEngine(project_folder)
    engine.generate_all()

Running the Script

python generate_launch_package.py /path/to/research-project

Output appears in research-project/dissemination/YYYYMMDD/.

Custom Research Skills

The Skill Architecture

Skills are specialized Claude configurations for specific tasks:

class ResearchSkill:
    def __init__(self, name, system_prompt, output_format):
        self.name = name
        self.system_prompt = system_prompt
        self.output_format = output_format

    def execute(self, content, context=""):
        # Implementation
        pass

Press Release Specialist

press_release_skill = ResearchSkill(
    name="Press Release Specialist",
    system_prompt="""You are a science communication specialist who writes
    press releases that journalists actually cover.

    Your releases:
    - Lead with the most newsworthy angle
    - Use accessible language without dumbing down
    - Include quotable statements
    - Provide context journalists need
    - Maintain scientific accuracy

    Always stay within provided accuracy guardrails.""",
    output_format="markdown"
)

Accessibility Translator

accessibility_skill = ResearchSkill(
    name="Accessibility Translator",
    system_prompt="""You translate complex research for public audiences.

    Your translations:
    - Use no jargon (or define immediately)
    - Lead with why it matters to readers
    - Use concrete examples and analogies
    - Maintain accuracy despite simplification
    - Include appropriate uncertainty

    Never sacrifice accuracy for accessibility.""",
    output_format="markdown"
)

Social Media Strategist

social_media_skill = ResearchSkill(
    name="Social Media Strategist",
    system_prompt="""You create research content optimized for social media.

    For Twitter:
    - Compelling hooks
    - Thread structures that maintain engagement
    - Appropriate use of data/visuals
    - Hashtag suggestions

    For LinkedIn:
    - Professional tone
    - Actionable insights for professionals
    - Thought leadership positioning

    Always maintain scientific accuracy.""",
    output_format="markdown"
)

Google Drive Integration (MCP)

Setting Up MCP for Research

Connect Claude to Google Drive for direct manuscript access:

{
  "mcp_servers": {
    "gdrive": {
      "command": "mcp-server-gdrive",
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/credentials.json"
      }
    }
  }
}

Automated Workflow with Drive Integration

# Conceptual workflow with MCP
def generate_from_drive(manuscript_path):
    """
    1. Read manuscript directly from Google Drive
    2. Generate dissemination content
    3. Write outputs back to Drive
    """
    # MCP enables direct file access without manual download/upload
    pass

Scheduled Automation

Publication Day Automation

Schedule your launch package generation:

# cron job: Generate launch package Monday before publication
0 9 * * 1 python /path/to/generate_launch_package.py /path/to/project

Weekly Dissemination Digest

For ongoing research communication:

def weekly_dissemination_tasks():
    """Run weekly dissemination automation"""

    # Check for new publications to disseminate
    new_pubs = check_for_new_publications()

    for pub in new_pubs:
        # Generate standard launch package
        engine = ResearchDisseminationEngine(pub.folder)
        engine.generate_all()

        # Notify researcher for review
        send_notification(pub.author, "Launch package ready for review")

Verification Automation

Automated Guardrail Checking

def verify_against_guardrails(content, guardrails):
    """Check generated content against accuracy guardrails"""

    prompt = f"""Review this content against accuracy guardrails:

    Content:
    {content}

    Guardrails:
    {guardrails}

    Identify:
    1. Any claims that overstate findings
    2. Missing required hedging
    3. Statements outside population boundaries
    4. Potential misinterpretations

    For each issue, provide:
    - The problematic text
    - Why it's problematic
    - Suggested revision"""

    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=2048,
        messages=[{"role": "user", "content": prompt}]
    )
    return message.content[0].text

Consistency Checking

def check_consistency(outputs):
    """Verify consistency across multiple outputs"""

    all_content = "\n\n---\n\n".join([
        f"{name}:\n{content}"
        for name, content in outputs.items()
    ])

    prompt = f"""Review these outputs for consistency:

    {all_content}

    Check:
    1. Are key findings stated consistently?
    2. Any contradictions between versions?
    3. Appropriate complexity for each format?
    4. Core message preserved?

    Flag any inconsistencies."""

    # Execute check
    pass

Time Savings Summary

| Task | Manual Time | Automated Time | |------|-------------|----------------| | Load context files | 5 min | 0 (automatic) | | Generate Twitter thread | 10 min | 30 sec | | Generate LinkedIn article | 15 min | 30 sec | | Generate press release | 15 min | 30 sec | | Generate blog post | 20 min | 45 sec | | Generate newsletter blurb | 5 min | 15 sec | | Save and organize | 10 min | 0 (automatic) | | Total generation | 80 min | ~3 min | | Review and edit | 30 min | 30 min | | Total | 110 min | ~33 min |

Per paper savings: ~77 minutes Per year (5 papers): ~6.5 hours

Plus: consistency, reduced cognitive load, and the ability to generate more content types.

Ready to Build Your Dissemination Engine?

This article is part of a comprehensive guide to AI-powered research dissemination. Learn how to get your research out of the PDF graveyard and into the hands of people who can use it.

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