This project provides an automated system that generates high-quality conversation starters tailored for dating-app interactions. The Bumble Conversation Starter Bot focuses on producing context-aware, engaging openers that reduce decision fatigue and help users begin meaningful conversations. It streamlines the process of brainstorming unique first messages and organizes them for easy use across devices.
This tool creates structured, relevant, and natural conversation starters based on profiles, interests, or predefined datasets. It automates the repetitive workflow of drafting openers, categorizing them, rotating them, and exporting them for use outside the application. Individuals and teams benefit from faster message ideation, consistent tone, and reusable message templates.
- Generates dynamic openers using lightweight NLP and rule-based formatting.
- Ensures message variety via rotation, templates, and scoring logic.
- Eliminates repetitive manual writing tasks in engagement workflows.
- Integrates with schedulers or external tools for exporting messages.
- Offers simple configuration for style, tone, and personalization.
| Feature | Description |
|---|---|
| NLP Template Engine | Produces context-aware conversation openers using templates and lightweight NLP. |
| Message Rotation Logic | Cycles through openers to prevent repetition and keep interactions fresh. |
| Profile Metadata Parser | Analyzes provided metadata or inputs to tailor the message intent. |
| Sentiment & Tone Control | Adjusts message tone (friendly, playful, direct) based on configuration. |
| Export to CSV/JSON | Saves generated messages for manual or external system usage. |
| Scheduler Integration | Allows periodic regeneration and export of message batches. |
| Anti-Repetition Scoring | Scores and filters similar messages to maintain variety. |
| Configuration Profiles | Lets users define personality presets or conversation themes. |
| Logging & Auditing | Tracks generation history and message usage analytics. |
| Retry & Failover Logic | Ensures robust generation even when inputs or models fail. |
- Input or Trigger — User provides profile information, tags, or a dataset of interests.
- Core Logic — NLP template engine assembles candidate openers, scores them, and applies tone rules.
- Output or Action — System exports curated conversation starters to the output folder.
- Other Functionalities — Scheduler can regenerate messages periodically or on external prompts.
- Safety Controls — Includes filtering, validation, and restricted automation that avoids interacting with any live platforms.
Language: Python Frameworks: Lightweight NLP libraries, rule-based template processors Tools: Scheduler, logger, configuration loader Infrastructure: Local execution or containerized worker environment
automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
- Individuals use it to generate high-quality first messages so they can avoid writer’s block.
- Content creators use it to craft themed conversation prompts so they can deliver consistent engagement ideas.
- Researchers use it to test dialogue models so they can analyze tone variation and prompt effectiveness.
- Developers use it to integrate message generation into larger engagement systems so they can automate text creation workflows.
- Marketing teams use it to create prompt libraries so they can standardize tone and style for campaign experiments.
Q: Does this interact directly with any dating platform? A: No. It only generates messages for offline use.
Q: Can I customize tone and theme? A: Yes, configuration profiles let you define personality, mood, or topic presets.
Q: Does it store personal data? A: Only what you choose to provide, and it can be disabled entirely.
Q: Can it run on a schedule? A: Yes, the built-in scheduler supports periodic regeneration.
Q: Is it suitable for batch message production? A: Absolutely—its rotation, scoring, and export system are built for bulk generation.
Execution Speed: Approximately 40–60 generated messages per minute under normal worker conditions. Success Rate: About 93–94% across long-running batches with built-in retries. Scalability: Capable of supporting 300–1,000 workers via sharded queues and horizontal scaling. Resource Efficiency: Typical consumption is ~0.2–0.4 CPU cores and 150–250MB RAM per worker. Error Handling: Includes structured logging, retry with exponential backoff, validation filters, and recovery flows to prevent failed job cascades.
