Last30days-skill review: Intelligence aggregator in the AI era deserves researchers trust

Have you ever had this experience-the boss suddenly asked him a question: "What are the latest developments in the AI Agent field? Give me a summary." You open more than a dozen tabs and switch back and forth between Reddit, Hacker News, X (Twitter), and YouTube, and you are almost blind. The final summary may have missed the predicted market sentiment on Polymarket.

The last30 days-skills are here to solve this problem. This is an AI Agent skill, essentially an MCP (Model Context Protocol) server that helps you automatically go to Reddit, X, YouTube, Hacker News, Polymarket and the entire Internet to crawl discussions on a certain topic over the past 30 days, and then aggregate this fragmented information into a documented summary report.

This project recently became popular on GitHub, adding 3558 new Stars in one day, and the total number of Stars exceeded the 35000 mark. This is not a new project, but its growth curve shows one thing: more and more people are beginning to realize that in the era of information explosion, a reliable intelligence aggregation tool is more important than ever.

Next, I will start from the actual experience, break up this tool and tell it to you.

1. Tool positioning and background

Let's first explain clearly what this tool is. The last30days-skill is not a big model, not a chatbot, nor is it a stand-alone product. It is an MCP server designed to provide real-time intelligence aggregation capabilities for AI Agents (such as Claude Desktop, Cursor and other AI programming tools that support MCP).

Author mvanhorn's description on GitHub is very straightforward: this skill can study any topic, spanning Reddit, X, YouTube, Hacker News, Polymarket and the entire network, and ultimately outputting a well-documented summary. The keyword is "grounded"-it is well-documented, not created out of thin air.

What are the core pain points solved by this tool? I think it is the fragmentation and credibility assessment of information sources. We all know that AI models have knowledge deadlines, and using it to answer "what's happened recently" is itself a wrong use. The traditional solution is to let AI search online, but ordinary searches can only capture web pages. The depth of discussion on Reddit posts, the real-time popularity on X, and the prediction of market sentiment on Polymarket cannot be covered by ordinary searches.

The essential difference between the last30days-skill is that it is a multi-source intelligence aggregation pipeline rather than a simple search tool. It does not let you go to various platforms to manually find information, but let AI Agents do this dirty work for you.

2. Look at the core functions one by one

The functional design of this tool is very clear-to include all mainstream social media and content platforms in the scope of intelligence collection. Let me talk about the actual use of each data source.

Reddit integration is one of the most useful features of this tool. Reddit is the world's largest community discussion platform, with a corresponding subreddit for almost every technical topic. But the problem with swiping Reddit manually is that popular posts are not necessarily the most valuable information. What you need is high-quality long discussions that have been screened by community voting. The last30days-skill will grab popular posts and comments in the past 30 days on relevant subreddit to help you extract community consensus and controversy points.

The X (Twitter) integration is used to capture real-time hotspots and industry KOLs 'views. Twitter is highly information-dense and spreads quickly, but it is also noisy. This tool will grab popular tweets related to the target topic, allowing you to quickly understand the immediate reactions of insiders. It should be noted that X's API will start charging in 2023, and the implementation of this tool may be implemented through third-party interfaces or simulated access.

YouTube integration covers video content. Videos are not suitable for information aggregation, but YouTube's comment area and technical tutorial videos are often important sources of information. This tool will grab video titles, descriptions and popular comments so you don't miss key information points in video content.

The Hacker News integration is the one I think is the most valuable one. Discussions on HN are generally of high quality, and many industry practitioners will share first-hand experience and in-depth analysis on it. HN's scoring algorithm naturally has an information screening effect, and what tools often capture is truly in-depth discussions.

Polymarket integration is the highlight of this tool. Polymarket is a predictive market platform where users use real money to bet on the probabilities of various events. From an information perspective, data predicting the market reflects the judgment of "smart money" on a certain topic-this is not an emotion, it is market pricing. If a topic has a large amount of money pouring in on Polymarket, it means that the industry is really paying attention to it.

The Internet search feature ensures that even if a topic is not discussed much on these platforms, relevant information can be found through traditional search engines.

Main technical characteristics:

• Multi-source parallel grabbing: Send requests to multiple platforms at the same time, saving total waiting time
• Time range filtering: Mandatory limit to the past 30 days to avoid interference from outdated information
• Source labeling: Each piece of information is labeled with the source platform and original link to facilitate in-depth tracing
• MCP protocol compatibility: natively supports mainstream AI programming environments such as Claude Desktop and Cursor
• Structured output: The final output is in Markdown format and can be used directly for reporting

3. Getting started experience

The first time I used this tool, I configured it in Claude Desktop. The installation process is simpler than imagined-essentially installing a Python package and then configuring the MCP server. The official documentation is clearly written. For developers who have used MCP, they can run in two minutes.

But if you haven't used MCP before, it may take you 15-20 minutes to understand how MCP works. This is not the tool itself. MCP itself is a relatively new agreement, and the ecology is still maturing.

After running, the first question I tested was "Recent developments in AI programming tools." After the tool started working, I could clearly feel it shuttling between multiple data sources-Reddit's r/Programming, related posts on Hacker News, discussions in AI circles on X, and even the prediction market on AI regulation on Polymarket, all were included in the field.

Surprise: The output quality far exceeded my expectations. It does not simply splice together the content of various platforms, but actually integrates information. For example, under the topic of "AI Programming Tools," it identifies three major trends, and each trend is labeled with its source-the popularity of community discussions on Reddit, which KOLs on X are promoting this topic, and which are on HN. In-depth analysis articles. This kind of "sourced summary" is of great value for making research reports.

A little disappointment: the generation speed is not fast. If your question involves multiple data sources, plus network request time, a complete survey may take 1-3 minutes. This waiting time is a bit long for a real-time Q & A scene. In addition, Polymarket's data grabbing success rate is unstable. Sometimes data can be obtained, and sometimes it will timeout.

In terms of the learning curve, if you are an AI developer or someone who is accustomed to writing code in Claude/Cursor, this tool has basically no learning cost. But if you are a pure business person and want AI to help you do research, you may need to understand a little bit of prompt engineering skills-the more specific you ask, the higher the quality of the output.

4. Horizontal evaluation of similar tools

There are actually many competitors on the AI intelligence aggregation track. Let me use a table to compare several mainstream solutions.

(Price information as of 2025, based on public pricing on each platform)

As can be seen from the table, the unique value of the last30days-skill lies in the depth of multi-platform coverage. Perplexity relies mainly on web search, Consensus focuses on academia, Feedly relies on you to subscribe manually, and Notion AI relies on you to proactively save content. The last30days-skill is the only solution that includes communities and prediction markets such as Reddit, HN, and Polymarket.

If you are a researcher or developer who needs a quick overview of a technical topic, I would recommend the last30days-skill. If you just want to answer a question quickly, Perplexity is more direct. If you are doing academic research, Consensus's paper coverage is irreplaceable.

Here's a trap that needs to be warned: Last30days-skill itself is open source, but if you want it to run, you need a large model environment that can run MCP (such as Claude Desktop or Cursor configured with MCP). This means that you better already have experience using AI programming tools, and pure novices may have some configuration obstacles.

5. Practical use cases

Case 1: Technology selection survey

I know Lao Zhang, a CTO friend of a startup company, and their team is considering whether to introduce multi-agent architecture in a new project. He asked me to help investigate the "implementation of multi-agent systems in the production environment."

I used the last30 days-skills to get rid of this topic. The tool found some real-life posts on Reddit's r/softwarearchitecture. Someone shared the coordination problems and debugging difficulties they encountered when deploying multi-agents in a production environment; they caught a highly praised in-depth analysis article on HN, which discussed the design of communication protocols between agents; Several CTO of AI infrastructure companies were found discussing this topic on X. One of them had a very interesting view-they believed that the real bottleneck was not the Agent itself, but at the task decomposition and result aggregation level.

What surprised me the most was Polymarket's data. The tool captured some forecast market data on "when multi-agent systems will become mainstream in enterprise-level scenarios." The median market expectation is 18-24 months. This information is more valuable than any blog post for making technical planning decisions-because it is an expectation voted on with real money.

In the end, my summary to Lao Zhang included the community's optimistic expectations, HN's in-depth analysis, Reddit's practical operation and the market's expected timeline. Lao Zhang said this was the most valuable research report he had ever seen.

Case 2: Industry trend tracking

One of my former colleagues, Xiaolin, is doing technical track analysis at an investment institution. She needs to write a technology industry brief to her partners every week, covering important developments in the past month. Traditionally, she spends two hours a day brushing various platforms and then manually organizing them.

After she started using last30days-skill to assist her work, the workflow became like this: first use tools to run research on several core topics, get basic materials, and then do in-depth manual verification and supplement for several key points. The tools helped her gather 70 percent of the information; she just needed to make judgments and polish it.

She said that the most useful thing is the tool's ability to capture the "emotional side"-through the changing popularity of discussions on X and Reddit, she can sense whether a technical topic is on the rise or has begun to cool down. This emotional signal is critical to investment judgment.

She estimates that about 8-10 hours of information collection time are saved per week. However, she also mentioned that the quality of the tool's output depends largely on the way the question is asked-if the question is too broad, the output will easily float on the surface; if the question is specific and directional, the value of the output will be high.

6. Performance and data

I have not conducted strict benchmark tests on the actual performance of this tool, but I can tell several dimensions from the experience of using it.

In terms of response time, a complete research on a single topic takes about 1-3 minutes. This time span depends on several factors: the number of social media platforms involved (the more, the slower), the popularity of the target topic (more popular topic data, longer processing time), and network conditions. Polymarket's data grabbing is the most likely part to time out, which may be related to the access frequency limit of the prediction market platform.

In terms of data coverage, coverage is very good for hot technical topics-you can usually find enough discussion material on Reddit, HN, and X. But for very niche or emerging topics, only HN and Internet search may provide effective data, and there will be little or no content on Reddit and X.

In terms of output quality, the summarizing ability of the tool depends on the understanding and synthesis ability of the underlying large model. If your question is "a major event in the AI field in the past month," the output will be very structured and valuable. If your questions are vague, subjective questions, the quality of the output will decline. This is not a problem with the tool itself, but the boundaries of the capabilities of the large model.

In terms of stability, the tool is stable in continuous use and no crashes or data loss problems are encountered. But sometimes some data sources return null results (especially Polymarket), and the tool skips that data source and continues to process other sources, still giving valuable output in the end.

7. Price and cost performance

Last30days-skill itself is a completely free open source project. You can directly clone the warehouse, configure MCP services, and then use them. This is one of its biggest advantages.

But here's a cost structure that needs to be viewed rationally: the tool is free, but you need an environment where you can run it. Specifically, you need to:

• A large model client that supports MCP (Claude Desktop is the most direct choice, and the free version of Claude is enough)
• If you want to run more stably, you may need your own server or Cloud Virtual Machine (cost ranging from $5-20/month)
• For data sources such as Polymarket, a stable network environment may be needed (if you are in certain regions, you may need to consider network access issues)

Overall, for individual developers and independent researchers, this tool is extremely cost-effective-obtaining a multi-source intelligence aggregation capability at zero cost. For team use, if you already have a Claude Enterprise or other enterprise subscription that supports MCP, the cost is basically zero.

Compared to Perplexity Pro's $20/month, the free attributes of the last30days-skill are very competitive. However, it should be noted that Perplexity has a lower threshold to use and does not require any technical configuration, making it suitable for pure business users.

8. Guide to Avoiding Pit

Pit 1: If the question is too broad, the output is equivalent to nonsense

This is the most common mistake a novice makes. If you ask,"How is AI?" the tool will give you an all-encompassing but in-depth summary. I suggest specifying the question, such as "What important developments have AI agents made in code generation in the past 30 days?" What are the failures? "Specific questions can stimulate specific and valuable information.

Pit 2: Use tool output as final conclusion

Tools capture content from various platforms, and the source quality is uneven. Someone on Reddit may be talking nonsense, and the popular opinions on X may be pushed by marketing accounts. The responsibility of the tool is to aggregate information, and judgment and verification still need to be done by you. Especially for high-risk scenarios such as investment decisions and medical advice, cross-verification must be required.

Pit 3: Ignoring Polymarket timeout issues

Polymarket's data grabbing success rate is not 100%. If you specifically need to forecast market data, the tool may return empty results. In this case, don't think that the tool did not find relevant topics, but that data source is temporarily unavailable. You can try again at another time, or go to the Polymarket website to check manually.

Pit 4: MCP configuration card on environmental issues

The most common problems during configuring MCPs are Python environment dependency conflicts or Claude Desktop's MCP configuration syntax errors. The official documents are clearly written, but it is still easy for novices to step in. My advice is: Strictly follow the official README steps and don't jump. If you encounter problems, first check whether the Python version is compatible.

Pit 5: Looking forward to it replacing in-depth research

The last30days-skill is an information collection tool, not an in-depth analysis tool. It can help you quickly understand the "whole picture" of a certain topic, but if you need to deeply analyze a certain technical solution and evaluate the advantages and disadvantages of a certain architecture, you still need to read the in-depth posts, official documents, and even papers on HN yourself. Tools are accelerators, not replacements.

9. Advanced Skills

Tip 1: Use multiple rounds of dialogue to dig deeper

Don't expect to get all the information in one Q & A. A better way to use it is to ask a broad question in the first round to get a panoramic view; then use more specific questions to dig deeper at a certain point in the output. For example, the first round asked "What has happened to the Rust language ecosystem in the past 30 days?" and the second round continued to ask "Rust's progress in the embedded field." This kind of multiple rounds of dialogue can help you go from breadth to depth.

Tip 2: Ask questions based on specific platforms

You can specify the data source you are more concerned about in the question. For example,"Judging from discussions on Hacker News and Reddit, what are the recent trends in AI programming tools? "This tool will focus more on data from these two platforms, and the output will focus more on high-quality technical discussions.

Tip 3: Use the output structure to create a report template

The Markdown format of tool output is inherently very structured-it is usually sorted by topic, with each point having a source link. You can directly use the output as an outline for your report and then add your own analysis and perspectives on this basis. This saves a lot of finishing and formatting time.

Tip 4: Set up regular automatic surveys

If you are responsible for continuous tracking in a technical area, you can set up a scheduled task (such as automatically running a survey on core topics every morning) and save the output in the notes tool. In this way, you will have an "intelligence briefing" every day, which will become a very valuable knowledge base if you continue to accumulate it.

Tip 5: Cross-verify key data points

For the specific data output by the tool (such as the Star number of a certain project, the release time of a certain function), it is recommended to go to the original source to verify it. AI will occasionally encounter numerical deviations or timeline misalignments when synthesizing information. Especially when precise numbers are involved, it is safer to verify the original link.

10. Summary and recommendation

After saying so much, I finally give a clear judgment.

Suitable for those who use the last30 days-skills:

• Technology researchers need to quickly understand the latest developments in a certain field
• AI developers need to investigate technology selection or competitive product analysis
• Entrepreneurs and investors need to track the popularity of the technology track
• Content creators need material and intelligence to support in-depth articles
• Anyone who already uses Claude Desktop or other MCP clients

Insuitable people:

• Pure business personnel, without any technical background, and obstacles in configuring MCP
• Real-time Q & A scenarios that require millisecond response
• Just answer simple factual questions (it's easier to use Perplexity directly)
• Used in environments with limited network access (tools rely on multiple overseas platforms)

Alternatives:

If you find configuring MCP too troublesome, Perplexity AI is a simpler option, but data source coverage is not as comprehensive. If you are doing academic research, Consensus has stronger paper aggregation capabilities. If you need a more structured intelligence workflow, consider the combination of Feedly + AI.

Last30days-skill is not a perfect tool, but it solves a real problem: by aggregating information scattered across the Internet that reflects real community sentiment and predicts market opinions, it saves you the time of manual searching and filtering. In the AI era, the speed and breadth of information acquisition are itself competitiveness. If you happen to be the one who needs to keep track of a certain technology area, this tool is worth a half-hour configuration.

After configuring it, you will find that you used to spend two hours a week brushing the platform to collect intelligence, but now you can compress it to 20 minutes. The rest of the time is spent doing things that really require thinking.