mvanhorn / last30days-skill

2026-01-23 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

The /last30days skill, now in v2.9.5, is a research tool designed for AI professionals to stay current with rapidly evolving trends. It scans various social media platforms and web sources, including Reddit, X, Bluesky, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the broader web, for content from the last 30 days. The skill identifies community-vetted information, such as upvoted discussions, shared content, and prediction market bets, to generate grounded narratives with citations. Recent updates include the addition of Bluesky/AT Protocol as a social source, a "Comparative mode" for side-by-side analysis of two topics, and per-project `.env` configuration. It also features auto-saving of briefings to `~/Documents/Last30Days/`, enhanced Reddit discovery via ScrapeCreators with top comment elevation, Instagram Reels integration, and advanced multi-signal quality-ranked relevance scoring incorporating Polymarket and Hacker News data.

Key takeaway

For AI Product Managers evaluating new tools or techniques, /last30days offers a critical advantage by synthesizing real-time community sentiment and best practices. You can quickly identify dominant prompting strategies, emerging workflows, and product use cases that official documentation might miss. Use its comparative mode to weigh options like "X vs Y" and inform your product roadmap with data-driven insights, avoiding reliance on outdated information.

Key insights

The /last30days skill provides community-validated, real-time intelligence across diverse platforms for AI professionals.

Principles

• Community engagement signals reveal true relevance.
• Multi-source data fusion enhances research accuracy.
• Structured prompts improve AI model adherence.

Method

The skill employs a two-phase search architecture: broad discovery across 10+ sources, followed by smart supplemental searches using extracted entities, all scored by relevance, recency, and engagement.

In practice

• Use JSON prompts for Nano Banana Pro image generation.
• Employ bracket tags like [Chorus] for Suno song structure.
• Implement a Claude-Codex "review loop" for code development.

Topics

• AI Content Analysis
• Social Media Intelligence
• Prompt Engineering
• Prediction Markets
• Claude Code Skills

Code references

• mvanhorn/last30days-skill
• openclaw/openclaw
• mvanhorn/last30days-skill
• clawdbot/clawdbot
• steipete/summarize

Best for: AI Product Manager, Prompt Engineer, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.