Research Any Topic in Minutes with the Last 30 Days Skill

The last30days-skill does one thing well: it gives your AI agent a structured research workflow that pulls signal from multiple sources in parallel. Instead of manually tabbing between Reddit, Hacker News, and YouTube, you describe what you want to learn and the agent synthesizes findings across platforms.

This tutorial shows how I used it to research the current state of AI agent frameworks in April 2026.

What Makes This Skill Different

Most AI research workflows just do a web search and summarize the top results. This skill is designed for deeper research:

• It queries multiple platforms (Reddit, X/Twitter, YouTube, Hacker News, Polymarket)
• It cross-references findings to find consensus and disagreement
• It produces structured output with source attribution
• It flags where sources conflict, which is where the most interesting insights live

Installation

claude skill add --from-github mvanhorn/last30days-skill

Verify it loaded:

claude
# Inside session:
# What skills do you have loaded?

Real Workflow: Research AI Agent Frameworks

Step 1: Define Your Research Question

Be specific. "Tell me about AI agents" is too broad. A good research question has a scope and a purpose:

Research the current state of AI agent frameworks as of April 2026.
I want to know:
1. Which frameworks are gaining vs losing traction
2. What developers on Reddit and HN actually think about them (not marketing copy)
3. Any emerging patterns or best practices that are becoming consensus
Focus on frameworks used for coding assistants and development tools.

Step 2: Let the Agent Work

The skill directs the agent to query multiple sources in sequence. This takes 2-5 minutes depending on how many platforms have relevant content.

The agent will:

1. Search Reddit for recent discussions and complaints
2. Check Hacker News for technical threads and opinions
3. Look at YouTube for tutorial trends and framework comparisons
4. Search X/Twitter for hot takes and announcements
5. Cross-reference all findings

Step 3: Review the Structured Output

The skill produces output organized like this:

## Consensus Findings
- [What most sources agree on]

## Areas of Disagreement
- [Where sources conflict, with specific quotes]

## Emerging Trends
- [Patterns that appear in the last 30 days but not before]

## Sources
- [Attributed links to every claim]

The key value is the "Areas of Disagreement" section. This is where you learn things that a single-source search would miss.

Step 4: Dig Deeper on What Surprised You

Follow up on unexpected findings:

You mentioned that developers on Reddit are moving away from Framework X.
Find 3 specific threads that discuss this and summarize the key complaints.

This targeted follow-up is where the research goes from "good summary" to "actionable insight."

Practical Tips

• Time-box your research. Set a 10-minute timer. If the agent has not surfaced anything surprising by then, refine your question.
• Ask for contradictions. The default output includes them, but you can push further: "Where do the most credible sources disagree with each other?"
• Verify surprising claims. If the agent finds something unexpected, verify it yourself before acting on it. AI research is a starting point, not a final answer.
• Use for competitive analysis. "What are people saying about [competitor] in the last 30 days?" is a powerful prompt for understanding market positioning.

When This Skill Shines

• Evaluating a new technology before adopting it
• Understanding community sentiment around a tool or framework
• Finding niche discussions that search engines do not surface
• Preparing for a technical interview or presentation on a topic

When to Skip It

• You need academic-grade citations (this uses social media and forums)
• Your topic is obscure with less than 10 relevant discussions in the past month
• You need real-time data (the skill looks at the last 30 days, not the last hour)

The Honest Assessment

This skill is not magic. It aggregates and synthesizes, which saves you 30-60 minutes of manual browsing. But the quality of output depends entirely on the quality of your research question. Spend 2 minutes crafting a specific question and you will get dramatically better results than a vague one.

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