Unlocking Multi-Source Intelligence: A Deep Dive into mvanhorn/last30days-skill

Overcoming the Real-Time Context Gap for AI Agents

Retrieval-Augmented Generation (RAG) has revolutionized how large language models (LLMs) access external knowledge. However, standard search engines often fall short when agents need to analyze highly dynamic, sentiment-driven, and multi-source conversational trends. Traditional search queries surface static SEO-optimized landing pages rather than real-time developer sentiment on Hacker News, public discourse on X (formerly Twitter), community discussions on Reddit, video breakdowns on YouTube, or financial sentiment on Polymarket.

Enter last30days-skill, an open-source Python library developed by mvanhorn. This project delivers a specialized AI agent skill designed to orchestrate deep research across the most influential social platforms, prediction markets, and the broader web over a strict 30-day window. It then synthesizes these disparate signals into a highly grounded, citation-backed summary, mitigating the hallucination risks common in multi-source analysis.

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Key Architecture & Features

What sets last30days-skill apart from simple scraping scripts is its intentional design as an extensible AI agent skill. It acts as an orchestrator, querying multiple platform APIs, normalizing raw payloads, and compiling them for structured prompt consumption.

• Multi-Platform API Connectors: Built-in integrations for Reddit, X, YouTube, Hacker News, and Polymarket, allowing the agent to capture a 360-degree view of any trending topic.
• Temporal Grounding (30-Day Constraint): Algorithms filter search queries and responses strictly to the preceding month, eliminating stale data and ensuring summaries focus purely on current trends.
• Prediction Market Signals: By integrating with Polymarket, the tool captures quantitative, real-money sentiment data, providing unique predictive context that traditional text-based search engines miss.
• Structured Synthesizer: Leverages advanced LLMs to process raw platform feeds, resolve conflicting statements, deduplicate information, and output a markdown report containing inline citations.
• Agentic Framework Compatibility: Designed with a modular, functional interface, making it easy to register as a custom tool in LangChain, CrewAI, Autogen, or LlamaIndex pipelines.

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Getting Started: Installation and Implementation

To integrate last30days-skill into your Python environment, follow this step-by-step setup guide.

Prerequisites

Ensure you have Python 3.10+ installed. You will also need access to API keys for your preferred LLM provider (e.g., OpenAI) and the search platforms (e.g., Tavily or Serper for general web search, along with necessary platform API keys).

Installation

# Clone the repository
git clone https://github.com/mvanhorn/last30days-skill.git
cd last30days-skill

# Install the package and dependencies
pip install -r requirements.txt

Practical Implementation Code Snippet

The following code demonstrates how to initialize the researcher tool, configure platform targets, and run a comprehensive search on a trending technical topic.

import os
from last30days_skill.researcher import Last30DaysSkill
from last30days_skill.config import SkillConfig

# Configure environment variables (normally loaded from a .env file)
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-search-api-key"

# Define the research parameters
config = SkillConfig(
    platforms=["reddit", "hackernews", "polymarket", "youtube", "web"],
    max_results_per_platform=10,
    output_format="markdown"
)

# Initialize the tool
research_tool = Last30DaysSkill(config=config)

# Execute the synthesis agent
topic = "LLM agents on production environments and reliability"
print(f"Initiating 30-day deep research on: \"{topic}\"...\n")

research_payload = research_tool.research(query=topic)

# Display the grounded summary and citations
print("### RESEARCH SUMMARY ###")
print(research_payload.summary)

print("\n### SOURCES & CITATIONS ###")
for source in research_payload.sources:
    print(f"- [{source.platform.upper()}] {source.title} ({source.url})")

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Real-World Use Cases & Target Audience

last30days-skill is an essential component for engineers and organizations building autonomous analytical workflows:

1. DevRel & Product Teams: Track developer reactions to a new framework release on Hacker News and Reddit. Understand friction points, common bugs, and integration patterns within days of launch.
2. Investment & Market Analysts: Use Polymarket API endpoints combined with X sentiment trends to analyze shift variations in geopolitical events, crypto-asset movements, or corporate announcements.
3. Autonomous News Agents: Power automated newsletters or social content curation bots with high-fidelity, synthesized reports that avoid repeating outdated information.
4. AI Research Engineers: Inject accurate, real-world context into custom RAG architectures, reducing overall system latency by utilizing pre-filtered platform queries instead of massive vector DB lookups.

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Why It Matters: The Evolution of Search for AI

As LLMs transition from passive chatbots to active, goal-oriented agents, raw data retrieval must become more specialized. Universal search tools return too much noise. By isolating the most informative, crowd-sourced digital channels—such as Hacker News for technical feedback and Polymarket for probability tracking—and scoping them strictly to the last 30 days, last30days-skill provides a template for the future of agentic research tools.

This project bridges the gap between structured databases and chaotic, real-time social channels, enabling developers to build smarter, more grounded, and highly contextualized AI agents.