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last30days-skill

What it is

last30days-skill is a sophisticated AI agent skill for Claude Code, OpenClaw, and Gemini CLI. It acts as a specialized search and research engine that prioritizes real-time social signals (Reddit upvotes, X likes, YouTube transcripts, Polymarket odds) over traditional SEO-optimized web results.

What problem it solves

Traditional search engines often surface stale editorial content or SEO-spam. In the fast-moving AI ecosystem, critical information first appears in community discussions. /last30days bridges a dozen disconnected platforms, allowing an AI agent to search, score, and synthesize current trends, tool comparisons, and "unfiltered" community feedback from the last 30 days.

Where it fits in the stack

Category: AI Assistants & Knowledge / Claude Code Skills

Typical use cases

  • Deep Tool Comparison: Asking /last30days OpenClaw vs Hermes to see real-world performance reports and GitHub velocity instead of marketing pages.
  • Pre-Meeting Briefings: Quickly summarizing a person or company's activities over the last month (e.g., /last30days Peter Steinberger).
  • Trend Analysis: Understanding the latest best practices in prompt engineering or agentic workflows (e.g., /last30days Nano Banana Pro prompting).
  • Repository Onboarding: Summarizing the last 30 days of Git history and issues to get an agent up to speed on a new codebase.

Strengths

  • Social Scoring: Ranks information based on actual engagement (upvotes, engagement rates) rather than keyword density.
  • Parallel Search: Executes entity-aware subqueries across multiple platforms (Reddit, X, HN, GitHub, TikTok, etc.) simultaneously.
  • Intelligent Pre-Research: The v3 engine resolves relevant handles, subreddits, and hashtags before searching, ensuring high-signal discovery.
  • Shareable Artifacts: Can emit self-contained, dark-mode HTML briefs for easy distribution in Slack or Notion.

Limitations

  • Token Usage: Parallel synthesis of multiple sources can consume significant input tokens if not carefully managed.
  • Rate Limits: Subject to the rate limits of the underlying search providers and social platforms.
  • Recency Bias: Explicitly ignores older, potentially more established documentation in favor of the "last 30 days" of activity.

When to use it

  • Emerging Tech Research: When researching tools or libraries that were released or updated very recently.
  • Vibe Checks: Understanding the community sentiment or "vibe" around a specific AI model or framework.
  • Crisis Monitoring: Tracking real-time outages, bugs, or major breaking changes reported by the community.

When not to use it

  • Deep Historical Research: If you need information from more than a month ago, traditional search is required.
  • Static Documentation: For stable libraries with unchanging APIs, official docs are more reliable than social chatter.
  • Critical Production Code: Social signals should not replace rigorous testing or official security advisories.

Getting started

Installation (Claude Code)

# Add the skill via the Claude Code plugin marketplace
/plugin marketplace add mvanhorn/last30days-skill

Basic Usage

# Research a topic
/last30days "Claude Code MCP servers"

# Generate a shareable HTML brief
/last30days "OpenRouter vs DeepSeek" --emit=html

Installation (OpenClaw)

clawhub install last30days-official

Technical details

  • Engine Architecture: Built with Python 3.12+ and utilizes yt-dlp for YouTube transcript retrieval and a vendored Node.js client for X search.
  • Entity Resolution: Bidirectional mapping between persons, companies, and their respective social/GitHub footprints.
  • Synthesis Engine: Uses an "AI Agent Judge" to filter noise and cluster identical stories across multiple sources.
  • Privacy: Operates locally on the user's machine; research data and API keys are not sent to external tracking servers.

Sources / references

Contribution Metadata

  • Last reviewed: 2026-05-17
  • Confidence: high