Transform your AI assistant from reactive to proactive by continuously monitoring specific topics and alerting you only when important developments occur.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install proactive-research
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install proactive-research using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Proactive Research?
Proactive Research is a specialized tool designed for Openclaw Skills that enables autonomous monitoring of the web for subjects you care about. Instead of requiring manual searches, this skill acts as a background agent that uses AI-powered importance scoring to filter through digital noise. It ensures you only receive immediate notifications for critical events while compiling minor updates into a convenient weekly digest.
By leveraging advanced search routing and memory integration, this skill bridges the gap between raw data and actionable intelligence. It analyzes findings within the context of your previous conversations and personal interests, providing not just links, but meaningful summaries that explain exactly why a development matters to you. This makes it an essential addition for users looking to expand their ecosystem of Openclaw Skills.
Proactive Research Use Cases
- Tracking breaking news and product releases in the AI and technology sectors.
- Monitoring security vulnerabilities and critical patches for DevOps and cybersecurity teams.
- Following financial market developments, such as price fluctuations in specific assets.
- Conducting competitive analysis to stay updated on rival product launches and funding rounds.
- Automating academic research by tracking new papers and publications on platforms like ArXiv.
How Proactive Research Works
- The user defines research topics with specific search queries, keywords, and monitoring frequencies like hourly or daily.
- The skill executes automated web searches using integrated providers such as web-search-plus.
- An AI importance scorer evaluates every search result based on freshness, relevance, and the user's personal context.
- High-importance findings trigger immediate alerts through configured notification channels like Telegram or Discord.
- Medium-importance findings are captured and stored for a structured weekly digest.
- The system updates its internal state to prevent duplicate alerts and uses local learning data to refine its scoring accuracy over time.
Proactive Research Setup
To get started with this component of your Openclaw Skills library, initialize your configuration by copying the example file:
cp config.example.json config.json
Add your first monitoring topic using the provided management script:
python3 scripts/manage_topics.py add "AI Model Releases" \
--keywords "GPT,Claude,Llama,release" \
--frequency daily \
--importance high
Finally, automate the monitoring lifecycle by setting up cron jobs on your system:
python3 scripts/setup_cron.py --auto
Proactive Research Data Schema & Taxonomy
Proactive Research organizes its operations through a structured set of JSON state files and markdown context files:
| Component | Description |
|---|---|
config.json |
Primary configuration for topics, alert channels, and global settings. |
.research_state.json |
Tracks last check timestamps and alerted URLs to handle deduplication. |
.findings/ |
Directory containing JSON-formatted records of items reserved for the weekly digest. |
memory_hints.md |
User-defined markdown file that provides context for more personalized AI summaries. |
.learning_data.json |
Stores local feedback to adjust importance scoring based on user interaction. |
Proactive Research Advanced Features
- Multi-channel notification support including Telegram, Discord webhooks, and SMTP email.
- AI Importance Scoring that prioritizes alerts based on major news signals or specific price thresholds.
- Learning Mode which automatically adjusts scoring weights and importance thresholds based on your behavior.
- Memory Integration that reads your conversation history to provide personalized summaries explaining the relevance of an alert.
- Advanced noise reduction using Regex patterns, source boosting, and negative keyword filtering.
- Intelligent search routing when paired with other Openclaw Skills like web-search-plus.
SKILL.md
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