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public-relations-manager公共关系经理

Agent Skill

public-relations-manager 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:public-relations-manager(公共关系经理)
来源仓库:https://github.com/zhenstaff/public-relations-manager
安装命令:
openclaw skills install public-relations-manager
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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简介

由人工智能驱动的公关专业人员助手可以匹配媒体、生成新闻稿和宣传材料,并有效地规划为期数周的公共关系活动。

SKILL.md

name
public-relations-manager
display_name
Public Relations Manager
version
1.0.0
author
ZhenStaff
category
productivity
tags
license
MIT
homepage
https://github.com/ZhenRobotics/openclaw-public-relations-manager
repository
https://github.com/ZhenRobotics/openclaw-public-relations-manager

Public Relations Manager 📰

AI-Powered Public Relations Assistant for OpenClaw

Version: 1.0.0 | Status: Production Ready ✅


📋 Description

Public Relations Manager is a comprehensive AI-powered public relations assistant that helps PR professionals, marketers, and founders manage their media relations, create compelling content, and plan strategic campaigns.

Perfect for:

  • 🎯 PR Professionals - Manage media databases and outreach efficiently
  • 🚀 Startups - Announce funding rounds and product launches professionally
  • 📢 Marketing Teams - Coordinate content calendars and multi-channel campaigns

✨ Core Features

1. 🎯 Media Matching Engine

Intelligently match your story with the right journalists and media outlets using a 4-dimensional AI scoring algorithm:

  • Category Match (40%): Direct + related category alignment
  • Influence Level (30%): Top-tier vs mid-tier vs niche outlets
  • Relationship (15%): Response rate + interaction history
  • Relevance (15%): Audience fit + outlet characteristics

Recommendation Levels:

  • 🟢 Highly Recommended (80-100): Perfect fit, pitch immediately
  • 🟡 Recommended (65-79): Strong match, good opportunity
  • 🟠 Maybe (50-64): Possible fit, consider context
  • 🔴 Not Suitable (<50): Poor match, skip

2. 📰 Press Release Generation

Generate professional press releases for:

  • 📱 Product Launches: Feature announcements, new releases
  • 💰 Funding Rounds: Series A/B/C, seed rounds
  • 🤝 Partnerships: Strategic alliances, integrations

Each template includes:

  • Professional headline generation
  • Structured body paragraphs
  • Quote formatting
  • Company boilerplate
  • Contact information module

3. ✉️ Media Pitch Creation

Create personalized media pitches with:

  • Custom subject lines tailored to journalist interests
  • Journalist-specific personalization based on their beat
  • Exclusive offer support for unique story opportunities
  • Follow-up templates for continued engagement

4. 📅 Campaign Planning

Plan comprehensive PR campaigns with:

  • Multi-week content calendars (4-8 weeks)
  • Content scheduling by date and type
  • Deadline tracking for upcoming and overdue items
  • AI-powered content suggestions based on campaign goals

📦 Installation

From PyPI (Recommended)

pip install openclaw-public-relations-manager

From ClawHub

clawhub install public-relations-manager

From GitHub Source

git clone https://github.com/ZhenRobotics/openclaw-public-relations-manager
cd openclaw-public-relations-manager
pip install -e .

🚀 Quick Start

from pr_manager import PRManager, MediaCategory

# Initialize PR Manager
pr = PRManager()

# Load sample media database (5 outlets + 5 journalists)
from pr_manager.data import load_sample_media_database
outlets, journalists = load_sample_media_database()
for outlet in outlets:
    pr.add_media_outlet(outlet)
for journalist in journalists:
    pr.add_journalist(journalist)

# Match a story to relevant media
result = pr.match_story_to_media(
    story_title="Company Raises $10M Series A",
    story_categories=[MediaCategory.TECH, MediaCategory.STARTUP],
    min_score=60.0
)

print(f"Found {len(result.matches)} relevant media contacts")
for match in result.get_top_matches(5):
    print(f"{match.target_name}: {match.overall_score}/100")

📝 Usage Examples

Example 1: Generate Press Release

# Generate a product launch press release
press_release = pr.generate_product_launch_pr(
    company_name="YourCompany",
    product_name="YourProduct",
    key_benefit="streamline team collaboration",
    problem_solved="the challenge of remote coordination",
    key_features=[
        "Real-time collaboration",
        "AI-powered automation",
        "Enterprise security"
    ],
    availability="available immediately",
    pricing="Starting at $99/month",
    quotes=[
        {
            "speaker": "CEO Name",
            "title": "CEO",
            "company": "YourCompany",
            "text": "This is a game-changer for our industry."
        }
    ],
    boilerplate="About YourCompany: Leading software provider...",
    contact_info={"Name": "PR Team", "Email": "pr@company.com"},
    location="San Francisco, CA"
)

# Save as markdown
with open("press_release.md", "w") as f:
    f.write(press_release.to_markdown())

Example 2: Quick Match and Pitch Workflow

# Match story to journalists and generate pitches in one step
results = pr.quick_match_and_pitch(
    story_title="AI Startup Disrupts Industry",
    story_categories=[MediaCategory.TECH, MediaCategory.STARTUP],
    story_hook="Company launches breakthrough AI technology",
    story_details="The platform automates complex workflows...",
    why_relevant="This aligns with your AI coverage.",
    top_n=5
)

# Review and send to top 5 matched journalists
for journalist, pitch in results:
    print(f"\
To: {journalist.email}")
    print(f"Subject: {pitch.subject_line}")
    print(pitch.to_email())

Example 3: Plan PR Campaign

from datetime import date, timedelta

# Create a 6-week product launch campaign
campaign = pr.create_campaign(
    name="Product Launch Q2",
    goal="product_launch",
    description="Comprehensive launch campaign",
    start_date=date.today() + timedelta(days=14),
    duration_weeks=6,
    target_categories=[MediaCategory.TECH, MediaCategory.BUSINESS],
    key_messages=[
        "Revolutionary AI technology",
        "10x productivity improvement",
        "Enterprise-ready security"
    ],
    budget=75000
)

# Get AI-suggested content for the campaign
planner = pr.calendar_planner
suggestions = planner.suggest_campaign_content(campaign)

for suggestion in suggestions:
    print(f"{suggestion['title']} - {suggestion['content_type'].value}")

📊 Sample Database

The skill includes realistic sample data:

Media Outlets (5):

  • 🚀 TechCrunch - Top-tier tech coverage
  • 📰 The Wall Street Journal - Business/finance authority
  • 💼 VentureBeat - Mid-tier tech news
  • 💰 Forbes - Business and entrepreneurship
  • 🔍 The Information - Tech insider coverage

Journalists (5):

  • Profiles with beat coverage, contact info, response rates
  • Recent article lists
  • Influence level classification

🧪 Testing

Run the comprehensive test suite:

python test_pr_manager.py

All 6 core tests pass with 100% success rate:

  • ✅ Media matching engine
  • ✅ Press release generation
  • ✅ Media pitch creation
  • ✅ Content calendar planning
  • ✅ Quick workflows
  • ✅ Database management

🔧 Parameters & Configuration

MediaCategory Enum

MediaCategory.TECH        # Technology
MediaCategory.STARTUP     # Startups & entrepreneurship
MediaCategory.BUSINESS    # General business
MediaCategory.FINANCE     # Finance & investment
MediaCategory.CONSUMER    # Consumer products
MediaCategory.ENTERPRISE  # Enterprise software
MediaCategory.LIFESTYLE   # Lifestyle & culture
MediaCategory.HEALTH      # Healthcare & wellness

InfluenceLevel Enum

InfluenceLevel.TOP_TIER   # Major outlets (NYT, WSJ, TechCrunch)
InfluenceLevel.MID_TIER   # Industry publications
InfluenceLevel.NICHE      # Specialized blogs/newsletters
InfluenceLevel.EMERGING   # New/growing platforms

🗺️ Roadmap

v1.1 (Planned)

  • 📊 Analytics dashboard for campaign performance
  • 📧 Email integration for automated sending
  • 📈 Media coverage tracking
  • 💬 Sentiment analysis

v1.2 (Planned)

  • 🔌 CRM integrations (HubSpot, Salesforce)
  • 🌍 Multi-language support
  • 📦 Press kit generation
  • 🔔 Media monitoring alerts

v2.0 (Future)

  • 🤖 AI-powered newsjacking suggestions
  • 🔍 Competitive PR intelligence
  • ⏰ Automated follow-up scheduling
  • 💵 ROI measurement tools

📚 Resources

  • GitHub: https://github.com/ZhenRobotics/openclaw-public-relations-manager
  • PyPI: https://pypi.org/project/openclaw-public-relations-manager/
  • Documentation: README.md
  • Contributing: CONTRIBUTING.md
  • Issues: https://github.com/ZhenRobotics/openclaw-public-relations-manager/issues

🤝 Support

  • Email: code@zhenrobot.com
  • GitHub Issues: For bug reports and feature requests
  • GitHub Discussions: For questions and community support

📄 License

MIT License - Open source and free to use


👥 Credits

Author: Justin Wang Co-Author: Claude Sonnet 4.5 Organization: ZhenRobotics


📊 Technical Details

  • Language: Python 3.10+
  • Platforms: Linux, macOS, Windows
  • Dependencies: pydantic>=2.5.0, python-dateutil>=2.8.0
  • Code Size: 4,078 lines across 27 modules
  • Test Coverage: 6 comprehensive tests (100% pass rate)
  • Architecture: Modular design with clear separation of concerns

🎯 Best Practices

For PR Professionals

  1. Build your media database gradually
  2. Track journalist response rates
  3. Personalize every pitch
  4. Follow up strategically

For Startups

  1. Prepare boilerplate and quotes in advance
  2. Time announcements strategically
  3. Build relationships before you need them
  4. Measure and iterate

For Marketing Teams

  1. Plan campaigns 4-6 weeks ahead
  2. Align messaging across channels
  3. Track all media interactions
  4. Maintain consistent brand voice

🎊 Ready to transform your PR workflow? Install now and start managing media relations like a pro!

clawhub install public-relations-manager

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