Automate Your AI News Monitoring: How last30days-skill Watches X, Reddit and HN for You

If you work in tech, you already know: the volume of relevant information published every day across X, Reddit, Hacker News, YouTube, and niche forums is impossible to track manually. You either spend an hour each morning scrolling through feeds, or you miss important developments and find out about them a week late in someone else's newsletter.

Created by Matt Van Horn (@mvanhorn), co-founder of June (acquired by Weber Grills) and early Lyft team member, the project has exploded to over 16,700 GitHub stars.

https://x.com/mvanhorn/status/2015551849710190697

Newsletters and aggregators help, but they are curated by someone else's priorities, published on someone else's schedule, and usually lack the depth you need to make decisions. What you actually want is a research assistant that monitors the sources you care about, filters for the topics relevant to your work, and delivers a cited report you can act on.

That is exactly what last30days-skill does.

last30days-skill is an open-source plugin (or "skill") for Claude Code and OpenClaw that runs a recency-bounded research workflow. You give it a topic. It gathers everything discussed about that topic in the last 30 days across 10+ platforms and produces a grounded, citation-heavy report, plus ready-to-use prompts tailored to your intent.

Created by Matt Van Horn, the project has rapidly gained traction: 2,600+ GitHub stars, 300 forks, and a spot as the top trending repository on GitHub at its peak. The current version (2.9.5) runs as a Claude Code skill invoked with the /last30days command.

The skill follows a structured workflow that goes well beyond a simple web search:

https://x.com/axiaisacat/status/2037349200069886330

When you invoke /last30days with a topic, the skill first analyzes your query to determine the topic, the target tool or platform you care about, and the type of research you need (recommendations, news, comparison, or prompt generation). This intent classification shapes the entire research process.

The skill systematically queries across its source list:

• Social platforms: X/Twitter, Reddit, Bluesky, Truth Social

• Developer communities: Hacker News, GitHub

• Video platforms: YouTube, TikTok, Instagram

• Prediction markets: Polymarket

• General web: Search engines and news sites

This is not keyword matching. The skill uses Claude's reasoning capabilities to identify relevant discussions, extract key claims, and note consensus versus disagreement across sources.

The collected data is synthesized into a structured report with inline citations linking back to specific posts, threads, and articles. Every claim is traceable to its source. This is the critical differentiator from typical AI-generated summaries that confidently state facts without provenance.

Beyond the report, the skill generates copy-paste-ready prompts tailored to your stated intent and target tool. If you are researching a framework to decide whether to adopt it, you get prompts designed for deeper investigation. If you are preparing a presentation, you get prompts that help generate supporting content.

Run /last30days "local LLM inference GPUs" each morning. Instead of scrolling through Reddit's r/LocalLLaMA, Hacker News front page, and your X feed separately, you get a consolidated report of what the community has been discussing, testing, and recommending over the past month. Benchmarks, gotchas, driver issues, and consensus picks, all cited.

Track what people are saying about a competitor's product across social media and forums. The 30-day window captures launch reactions, bug reports, feature requests, and sentiment shifts that would take hours to manually compile.

Before committing to a framework, library, or tool, run a last30days query to see real-world usage reports. Developer forums and social media contain unfiltered feedback that official documentation and marketing pages never will.

If you write technical content, newsletters, or documentation, the skill provides a curated snapshot of current discourse around your topic. You get the talking points, the debates, and the emerging consensus, all with sources you can verify and cite.

Before a strategy meeting, stakeholder call, or investor update, run a query on your industry or product category. The resulting report gives you current data points and community sentiment that go beyond what you would find in a quick Google search.

The objection is obvious: can you not just use a search engine or ask ChatGPT? The difference is specificity and structure.

# Install and run
git clone https://github.com/mvanhorn/last30days-skill
claude skill add ./last30days-skill

# Research a topic
claude "Research the latest developments in AI email clients 
       over the last 30 days. Focus on new features, 
       community sentiment, and competitive moves."
# Generates a structured Markdown report with sources

For email productivity tools like Maylee, last30days-skill opens up powerful automation possibilities. Imagine automatically generating a weekly briefing of competitor moves, user feedback trends, and emerging technologies in the email client space. This kind of continuous market intelligence used to require a dedicated analyst. Now it runs as a scheduled Claude Code task.

https://x.com/MillieMarconnni/status/2036363493478375797

Full documentation includes prompt templates for common research use cases and configuration guides for optional sources like YouTube and Polymarket.

A search engine returns a list of links. You still have to click through, read, evaluate relevance, cross-reference, and synthesize. An LLM chat gives you a summary, but without citations, without source diversity guarantees, and without the recency constraint that ensures you are getting current information.

last30days-skill combines several properties that no single tool provides:

• Recency guarantee: Only content from the last 30 days. No stale blog posts from 2023.

• Source diversity: 10+ platforms, ensuring you see discussion from developers, influencers, casual users, and prediction markets, not just whatever ranks highest on Google.

• Full citations: Every claim links to a specific source. You can verify anything that seems surprising.

• Intent-aware output: The report structure and prompt suggestions adapt to what you are trying to accomplish.

• Reproducible: Run the same query next week and get an updated report reflecting new developments.

last30days-skill is part of a growing ecosystem of Claude Code skills, modular plugins that extend the AI assistant's capabilities with specialized workflows. This ecosystem is expanding rapidly as Claude Code adoption grows (recent reports attribute 4% of all GitHub commits to Claude Code, and Anthropic's $30 billion Series G at a $380 billion valuation reflects the momentum behind agentic coding tools).

Skills represent a new distribution model for AI capabilities. Instead of building a full application, developers can package a workflow as a skill that runs inside the user's existing AI environment. The user gets new capabilities without leaving their terminal or switching tools.

For teams already using Claude Code for development, adding last30days-skill is a single installation command. No new accounts, no new interfaces, no subscription fees.

The skill is not without constraints:

• Depends on Claude Code: You need an active Claude Code subscription (Pro at $20/month or Max at $100-200/month). The skill itself is free, but it runs on Claude's infrastructure.

• Source access variability: Not all platforms are equally accessible. Some social media APIs restrict access, which can limit the depth of data collection from certain sources.

• Quality scales with model: The quality of synthesis depends on the underlying Claude model. Max-tier subscribers using Opus will get more nuanced analysis than Pro-tier users on Sonnet.

• No real-time monitoring: This is on-demand research, not a continuous monitoring feed. You run it when you need a report, not as a background daemon.

The most effective way to use last30days-skill is as part of a structured information routine. Rather than passively scrolling feeds, you actively query for the information you need:

• Monday morning: Run queries on your key technology areas and competitors to start the week informed.

• Before decisions: Query the specific technology, vendor, or approach you are evaluating.

• Before publishing: Check current discourse on your topic to ensure your content is timely and addresses active debates.

• Weekly team sync: Share the generated report with your team as a current-state briefing.

This kind of structured research workflow pairs well with AI-powered productivity tools. For instance, if your last30days report surfaces an important development, you might want to email your team about it immediately. AI email clients like Maylee can auto-draft that communication in your voice, turning research output into team action in seconds rather than the usual draft-edit-send cycle.

The technical architecture of last30days-skill is worth understanding because it illustrates how Claude Code skills work in practice. The skill is defined in a SKILL.md file (currently version 2.9.5) that contains the full workflow specification: intent classification rules, source prioritization logic, synthesis templates, and prompt generation patterns.

When Claude Code loads the skill, it gains access to this structured workflow without any additional API keys or external services. The skill leverages Claude's existing web browsing and reasoning capabilities, orchestrating them in a specific sequence optimized for recency-bounded research.

The output format is also carefully designed. Reports include structured sections for key findings, source consensus, dissenting views, and emerging trends. Each section uses inline citations formatted for easy verification. The prompt templates that accompany each report are tailored to the user's stated intent and target tool, making it trivial to continue the research in a different context.

Creator Matt Van Horn has maintained an aggressive release cadence, with the project now at 203 releases. This pace suggests active iteration based on user feedback and expanding source coverage.

last30days-skill points toward a broader shift in how knowledge workers consume information. Instead of subscribing to feeds and being passively notified, you dispatch an AI agent to actively research a topic on demand. The agent knows where to look, how to evaluate what it finds, and how to present the results in a format you can act on.

Dave Morin, a prominent tech investor, publicly praised the tool, stating he uses it daily. This kind of endorsement from high-profile users has contributed to the project's visibility and adoption velocity.

The 2,600+ stars and rapid GitHub trending placement suggest this is a workflow many developers have been waiting for. As the skill ecosystem matures and more specialized research skills emerge, the manual ritual of "check Twitter, check Reddit, check HN, check email, synthesize mentally" will increasingly feel like an unnecessary tax on productive time.

For now, last30days-skill is the most practical implementation of this vision: a single command that replaces an hour of scrolling with a cited, structured, intent-aware research report.