current discussion research

The problem

LLMs often rely on training data that is months or years old, making them unreliable for pulse-checking current community sentiment or emerging technical trends. Generic web searches frequently fail to distinguish between primary evidence and social noise, leading to shallow or outdated synthesis.

What it does

• Researches public discussions across Reddit, X, YouTube, Hacker News, Polymarket, and GitHub within a strict 30-day window.
• Ranks findings using a normalized scoring model based on relevance, engagement, recency, and authority.
• Separates social signals from primary factual evidence to prevent engagement metrics from being mistaken for truth.
• Generates a shareable, accessible dark-mode HTML brief with embedded evidence links and platform coverage maps.

Why this beats prompting it yourself

A standard prompt usually results in a single-pass search that misses niche practitioner signals on platforms like Hacker News or GitHub. This skill implements a rigorous multi-platform handling logic, platform-normalized scoring, and a structured evidence standard that ensures every claim is traceable to a specific, dated source.

Use cases

• Synthesizing developer sentiment toward a new framework release on Reddit and HN.
• Tracking emerging market signals for a specific industry across prediction markets and social threads.
• Monitoring product feedback and bug reports across YouTube comments and GitHub issues.
• Creating shareable research briefs for stakeholders that require high transparency on source authority.

Known limitations

Relies entirely on the host agent's existing search and browse capabilities. Cannot access private, gated, or authenticated data without user-provided tools.