Last30Days: AI Agent Research Skill for Real-Time Insights

Modular Multi-Source Architecture

The Last30Days skill employs a sophisticated multi-source architecture that integrates data from 12 distinct platforms including Reddit, X (Twitter), YouTube, Hacker News, Polymarket, and web search. The system is built around a core abstraction of a Researcher class that orchestrates data collection, processing, and synthesis.

The design emphasizes modularity, allowing easy addition of new data sources while maintaining a consistent interface for the synthesis engine. A notable trade-off is the prioritization of breadth over depth - the system casts a wide net across platforms rather than providing deep analysis of any single source.

The most significant innovation is the grounded synthesis approach that ensures AI-generated summaries are directly tied to specific sources and timeframes, reducing hallucination while maintaining contextual relevance.

• Recency-weighted aggregation: The system implements a sophisticated scoring mechanism that prioritizes content from the last 30 days, with exponential decay for older content. This ensures summaries reflect current trends rather than historical information.
• Platform-specific normalization: Each data source undergoes custom preprocessing to extract meaningful content while filtering out noise, spam, and irrelevant information. For example, Reddit content is processed to remove moderator bot posts and prioritize high-karma comments.
• Multi-modal content handling: The skill can process text, video transcripts (YouTube), and even market data (Polymarket) in a unified framework, allowing for comprehensive analysis across different content types.
• Source attribution system: Every claim in the final summary is linked back to its original source with a timestamp, enabling users to verify information and understand the provenance of insights.
• Dynamic query expansion: The system automatically generates related search terms based on initial queries to ensure comprehensive coverage of a topic across multiple platforms.

Scalability and Performance Metrics

The system demonstrates good horizontal scalability, with each data source operating as an independent collector that can be scaled separately. However, the synthesis step represents a potential bottleneck as it relies on external LLM API calls. The implementation includes smart caching for recent queries, which reduces response times for repeated searches.

Limitations include occasional API rate limiting from social platforms (particularly X and Reddit), and challenges in processing video content at scale. The system also struggles with highly specialized or emerging topics that haven't yet gained traction on monitored platforms.

Competitive Landscape

Last30Days integrates seamlessly with the OpenClaw ecosystem and works specifically with Claude models, making it particularly valuable for users already invested in the Anthropic ecosystem. The skill has gained significant traction in the AI research community, with notable adoption by journalists, market researchers, and AI developers.

The project maintains active development with regular updates to support new platforms and improve synthesis quality. Its open-source nature has fostered a community of contributors who add new data sources and enhance existing functionality. Integration points include webhook support for automated research workflows and API access for embedding the skill into other applications.