This AI Agent Monitors X, Reddit, and YouTube So You Don't Have To

Tech monitoring is a problem every digital professional knows intimately. Following the latest AI developments, new tools, market trends, community reactions: it easily takes two to three hours daily when done seriously. What if an AI agent could do it for you, automatically scanning X (Twitter), Reddit, YouTube, Hacker News, Polymarket, and the web, then synthesizing a structured report with verified sources?

That's exactly what last30days-skill delivers. It's an open-source skill for Claude Code created by Matt Van Horn (@mvanhorn), co-founder of June (acquired by Weber Grills) and early Lyft team member. In just a few weeks, the GitHub repo exploded to over 16,700 stars and 1,300 forks, becoming one of the most popular projects in the Claude Code ecosystem.

For B2B prospecting teams, digital agencies, and SaaS founders, this tool radically changes how market intelligence is collected. Instead of spending hours scrolling X and Reddit, you get a complete, sourced, structured report in minutes.

The volume of information produced daily in the tech ecosystem has reached a level that makes manual monitoring structurally impossible to maintain. On X alone, thousands of technical tweets are published every hour. Reddit hosts dozens of specialized subreddits where high-quality technical discussions mix with noise. YouTube accumulates hundreds of demo videos, tutorials, and analyses daily. Hacker News generates discussions often richer than the articles they comment on.

Traditional monitoring tools, from Google Alerts to Feedly to curated newsletters, only solve part of the problem. They aggregate content but don't synthesize it. You end up with 200 articles to sort instead of zero, which just shifts the problem. What's missing is intelligence capable of reading, understanding, cross-referencing, and summarizing these sources for you, retaining only what's genuinely relevant to your domain.

The second problem is source fragmentation. Relevant information is no longer concentrated in a few reference publications. It's scattered across a founder's tweet announcing a feature, a Reddit thread where users report a critical bug, a YouTube video from an engineer running benchmarks, and a Hacker News post where the community debates implications. No human can cover all of this in real-time.

This is precisely the gap that last30days-skill fills: an agent that goes where the information lives, verifies it, cross-references it, and delivers an actionable report.

last30days-skill is a Claude Code skill, meaning a set of instructions and scripts that Claude Code (Anthropic's command-line tool) executes autonomously. When you launch a research query, the agent orchestrates a multi-step process:

• Query analysis: the agent decomposes your request into sub-themes and identifies the most relevant sources to query

• Parallel collection: the agent simultaneously queries X/Twitter (advanced search), Reddit (API), YouTube (transcripts), Hacker News, Polymarket (market predictions), and the general web

• Cross-verification: each claim is cross-checked across at least two sources before being included in the report

• Structured synthesis: results are organized into thematic sections with citations, links to original sources, and indicators of community consensus or disagreement

The 'zero-config' aspect is a major strength. Core sources (X, Reddit, Hacker News, web) work without any API key or configuration. YouTube and Polymarket have optional modules. The agent uses Claude Code's native capabilities to browse the web, parse pages, and extract relevant information.

A typical run takes between 3 and 15 minutes depending on topic complexity and the number of sources queried. The agent generates a structured Markdown report that can easily be transformed into a shareable document, client brief, or internal newsletter.

# Installation and basic usage
# 1. Clone the repo
git clone https://github.com/mvanhorn/last30days-skill.git

# 2. Add the skill to Claude Code
claude skill add ./last30days-skill

# 3. Run a research query
claude "Research the latest developments in AI code editors 
       (Cursor, Windsurf, Claude Code) over the last 30 days.
       Focus on new features, pricing changes, and community sentiment."

# Markdown report is generated automatically

To illustrate the quality of results, here's what a typical run on 'AI code editors' produces:

The report starts with a 3-4 paragraph executive summary capturing the main trends identified across all sources. Then, each sub-theme is developed in a dedicated section, with direct citations from the most relevant sources and clickable links to original posts, videos, or discussions.

The tool's strength lies in its ability to cross-reference perspectives. On a topic like Cursor vs Windsurf, the agent will:

• Collect official announcements from both teams on X

• Analyze user experience feedback on r/programming, r/LocalLLaMA, r/cursor

• Extract key points from benchmark videos on YouTube

• Identify the most upvoted technical discussions on Hacker News

• Synthesize everything into a balanced report that doesn't just list but analyzes

Every claim in the report is accompanied by a link to the original source. This isn't text generation: it's AI-assisted documentary research with complete traceability. One user shared an example of a legal monitoring report on new AI regulations that impressed the community with its depth and accuracy.

Before launching a prospecting campaign on Emelia, the quality of targeting and personalization makes all the difference. last30days-skill lets you automatically research the latest news in a sector, technologies adopted by target companies, and pain points expressed by the community. A sales rep launching a campaign targeting AI startup CTOs can ask the agent: 'What are the main technical problems mentioned by AI startup CTOs on X and Reddit over the last 30 days?' and get a report identifying real pain points to address in their emails.

For teams producing content (like we do at Emelia, Bridgers, and Maylee), identifying trending topics before they go mainstream is a major competitive advantage. last30days-skill can scan the most engaging discussions on Hacker News and Reddit to identify tools and technologies gaining traction, well before they appear in mainstream tech newsletters. This is exactly how we identify our article topics.

Before an important meeting with a prospect or partner, running last30days-skill on their company and sector lets you arrive prepared with fresh information: their latest announcements, community reception, problems reported by their users, and competitive positioning. This is automated sales intelligence.

SaaS founders need to simultaneously track their market, competitors, tech trends, and user feedback. last30days-skill can be configured to generate an automatic weekly report covering all these angles, transforming hours of scrolling into a few minutes of targeted reading.

last30days-skill's strength lies in its configuration flexibility. By default, the agent queries core sources with zero configuration required. But for more specific use cases, several customization options are available:

The ability to add custom sources is particularly interesting for B2B use cases. An agency like Bridgers can configure the agent to monitor client websites, specialized industry forums, and niche social networks not covered by default sources.

The agent also supports multilingual queries, which is relevant for multilingual platforms. A search on e-commerce trends in Germany will automatically use German terms to query German-language sources, then synthesize results in English or French based on your preference.

last30days-skill's main advantage over these alternatives is the combination of multi-source coverage and automatic synthesis. Perplexity excels at one-off questions but doesn't do systematic monitoring. Google Alerts only covers Google-indexed web and synthesizes nothing. Feedly aggregates but doesn't intelligently summarize. Grok only covers X.

The main drawback is Claude Code token cost: each run consumes Claude tokens, which can amount to a few dollars per in-depth report. For daily use, the monthly token budget should be planned. However, compared to the hourly cost of a qualified human analyst, the value proposition strongly favors the agent.

The enthusiasm around last30days-skill is remarkable. The project hit 16,700 GitHub stars and topped the daily trending repos multiple times. Reactions from tech professionals are telling:

• Logan Green (Lyft co-founder) simply commented 'So good!' on the announcement tweet

• Nabeel (Spark Capital VC) reacted 'Oh. I just needed something like this'

• Gentry Underwood (Dropbox/Mailbox co-founder) called the tool 'rad'

• Users shared example reports on topics as varied as AI legal research, code tool comparisons, and crypto market analysis

What distinguishes last30days-skill from dozens of other Claude Code skills is result quality. Generated reports aren't simple link compilations: they're argued syntheses with nuances, identified points of disagreement, and an editorial structure that makes them immediately actionable. Several users reported using the reports directly as bases for newsletters, client briefs, or internal presentations.

The project has also inspired derivative tools. x-research-skill, for instance, focuses specifically on deep X/Twitter research. Other developers have forked the project for specific use cases: regulatory monitoring, patent tracking, product sentiment analysis.

Despite its qualities, last30days-skill has limitations to know before integrating it into your workflow:

• Result quality depends heavily on prompt quality. A vague request produces a vague report. Best results come from specific queries that specify the topic, desired analysis angles, and priority sources

• Access to some sources is limited by paywalls or API restrictions. X/Twitter notably can block frequent requests. YouTube results depend on transcript availability

• Claude token costs can become significant for daily in-depth research. A complex run covering 10+ sources can consume $2-5 equivalent in tokens

• The agent has no memory between runs: each search is independent. It cannot detect changes from a previous monitoring run (unless you explicitly specify this in the prompt)

• Like any LLM-based system, it can occasionally hallucinate details or misattribute a citation. Verification of critical information remains necessary

• The tool requires Claude Code, which is a paid Anthropic product. It doesn't work with other AI assistants

These limitations are, however, largely offset by the time savings. A professional spending 2 hours daily on monitoring can reduce this to 15-20 minutes of reading the agent-generated report, while covering a much broader spectrum of sources.

To maximize the tool's value, the community has identified several best practices that consistently produce better reports:

# Optimized prompt for B2B SaaS monitoring
claude "Research the B2B SaaS prospecting and cold email market 
       over the last 30 days. Focus on:
       1. New tools launched or major updates (AI-powered)
       2. Pricing changes from major players
       3. Community sentiment on Reddit and X about deliverability
       4. Notable case studies or growth hacks shared publicly
       
       Prioritize: X (@lemlist, @instantly_ai), Reddit (r/coldemail, 
       r/sales), Hacker News. Output structured report with exec summary."

# Competitive intelligence prompt
claude "Research everything public about [Competitor] over the last 
       30 days: product launches, hiring, funding, community feedback, 
       pricing changes, user-reported technical issues."

The ideal workflow is to create prompt templates adapted to your recurring needs and run them at regular intervals. Some users have automated the process via cron jobs that launch a daily run and send the report via email or Slack. For teams managing multiple product lines or client accounts, maintaining a library of specialized prompts ensures consistent and comprehensive coverage without reinventing the wheel each time.

The agent also works well in combination with other Claude Code skills. You can chain last30days-skill with a writing skill to automatically transform monitoring reports into newsletter drafts, or with a data analysis skill to extract structured data from the research results. This composability is one of the key advantages of the Claude Code skill ecosystem and makes last30days-skill far more powerful than a standalone monitoring tool.

Another consideration when comparing alternatives is the depth of analysis. Perplexity and Google deliver breadth but often lack depth on any single topic. last30days-skill, because it spends 3-15 minutes per research run compared to Perplexity's approximate 5 seconds, can dive deeper into each sub-topic, follow discussion threads, and capture nuances that faster tools miss entirely. For professionals who need to make decisions based on their research rather than just stay informed, this depth matters significantly more than speed.

The open-source nature of last30days-skill also means you can audit exactly what the agent does, modify its behavior, and add sources that matter for your specific industry. Try doing that with a closed-source SaaS monitoring tool. For security-conscious organizations that want full control over their research pipeline and need to ensure no sensitive queries leak to third-party services, this transparency is a decisive advantage.

For digital agencies managing multiple clients, the ROI calculation is straightforward. A single last30days-skill run that takes 10 minutes of compute time and costs a few dollars in Claude tokens can replace an hour of senior analyst time that would cost 50 to 100 dollars. Multiply that by daily monitoring across five client accounts, and the savings become substantial within the first week of adoption. The professionals who learn to leverage these autonomous research tools effectively now will have a significant competitive advantage as the technology matures and becomes mainstream.

last30days-skill isn't just a practical tool. It's a strong signal of what AI agents will transform in digital professionals' daily work. Tech monitoring is one of the first use cases where an autonomous agent can genuinely replace hours of human work with comparable or superior results.

The logical next step, already anticipated by the community, is adding memory between runs. An agent that remembers what it reported last week and only flags changes and new developments would be a complete game-changer. Court Starr, entrepreneur and early adopter, captured the general sentiment by tweeting: 'We're going to need the /last30mins soon.'

For B2B prospecting platforms like Emelia, integrating similar monitoring agents directly into the tool would open considerable possibilities: automatic prospect data enrichment with fresh information, real-time identification of sales triggers (funding rounds, hires, product launches), and email sequence personalization based on each target company's latest news.

last30days-skill is available open source under MIT license on GitHub. The project is actively maintained, with recent commits and a rapidly growing contributor community. If you use Claude Code, it's probably the first skill to install.