What is last30days-skill?

last30days-skill is a rapidly emerging open-source AI research skill on GitHub, created by developer mvanhorn. The project can be found at GitHub Repository.

In essence, this is a “skill module” that runs on Claude Code or a compatible AI Agent framework. You can ask it about a topic (e.g., “Best Prompts for Claude Code” or “Cursor vs Windsurf”), and it will automatically scrape discussions from over 10 platforms, including Reddit, X, Hacker News, YouTube, TikTok, Bluesky, and Polymarket, from the past 30 days, synthesizing a comprehensive intelligence report complete with sources, ratings, and usable prompts.

The core logic of the project is that real community discussions are closer to the “truth” than official documentation. While official documentation tells you what a tool can do, posts on Reddit and HN reveal where the pitfalls are and what the truly effective practices are.

The project is licensed under the MIT open-source license and is completely free, but you need to configure your own APIs. Initially covering only Reddit and X, it now supports over 10 sources and has integrated predictive markets (Polymarket), evolving rapidly.

Community Popularity and Project Scale

The growth rate of this project has surprised me. Here are some numbers:

The speed from 1.5k stars to over 9k is quite impressive for open-source AI tools. Community feedback has been largely positive—most discussions in GitHub Issues are about feature requests rather than bug complaints, indicating a solid foundation.

However, as for revenue, this is a purely open-source project, and the author’s monetization is non-existent. The costs incurred from using it mainly come from the APIs you configure.

Core Features

Multi-Platform Parallel Search

It performs simultaneous searches across Reddit, X (Twitter), Hacker News, Polymarket, YouTube, TikTok, Instagram, Bluesky, and more than 10 platforms in one query. This is true parallel scraping, not sequential searching.

Two-Stage Intelligent Search Architecture

The first stage is “broad discovery”—using wide-ranging keywords to cast a wide net; the second stage is “intelligent supplementary search”—identifying subtopics worth digging deeper into based on the first stage’s results and adding precise queries. This design ensures a more comprehensive coverage of results.

Composite Scoring Model

All scraped results go through a scoring pipeline:

• Text relevance (35%)
• Interaction heat (25%)
• Source authority (20%)
• Cross-platform convergence (10%)—same topic appearing on multiple platforms gets extra weight
• Timeliness decay (10%)—newer content has higher weight

Predictive Market Integration

This feature impressed me. It integrates with Polymarket’s Gamma API to pull in “real money betting prediction data.” When most people are betting real money on the success of an AI tool, this signal is much more credible than general post discussions.

Comparative Analysis Mode

Supports X vs Y style queries, such as /last30days cursor vs windsurf, generating a side-by-side comparison report that includes the pros and cons, community sentiment, and discussion trends. This is very useful for competitive research.

Dashboard and Automatic Monitoring

You can set up topic monitoring lists for periodic automatic re-research, suitable for users needing to continuously track a specific area.

Automatic Archiving

Each run’s results are automatically saved to a local document library for easy historical reference.

Target Audience

Honestly, this tool has a clear “technical user” attribute. If you don’t understand command lines or can’t configure API keys, getting started can be quite painful. However, if you are a developer or content creator who frequently needs to conduct research, it is definitely worth spending an hour to set it up.

Application Scenarios

1. AI Tool Research (Most Common)

Want to know how the community is rating “Cursor” recently or how “DeepSeek” is viewed on Reddit? One command can do it:

/last30days DeepSeek R2 for coding

In 5 minutes, you’ll receive a comprehensive report with source links, key discussion points, and actual usage tips.

2. Technical Selection Comparison

/last30days claude code vs cursor vs github copilot

This will pull recent discussions of the three tools across platforms, automatically generating a comparison report to present directly in technical selection meetings.

3. Content Creation Topic Selection

As an AI blogger, I now use it to “find topics.” I check what the AI community has been discussing over the last 30 days, and which topics have high cross-platform convergence, then I write about those. This is much more efficient than mindlessly scrolling through information feeds.

4. Best Practices for Prompts Collection

/last30days best system prompts for claude code

It will help you find community-validated prompt patterns from Reddit, HN, and X, organized into a directly reusable format.

5. Competitor Sentiment Monitoring

For startup teams, running /last30days [competitor name] regularly can quickly reveal the latest complaints and praises from users about competitors, guiding product optimization direction.

Differences from Similar Tools

Currently, there are many tools that provide similar “real-time information aggregation + AI summarization,” but last30days has several unique aspects:

Core Differences:

• vs Perplexity: Perplexity is better for quick Q&A, while last30days excels in community intelligence gathering; Perplexity lacks deep coverage of social media like Reddit/TikTok and does not integrate Polymarket.
• vs Manual Search: This is the biggest competitor. Manually checking Reddit/HN/X takes 2-3 hours; using last30days takes 5-10 minutes.

Usage Tips

Tip 1: Configure SKILL.md for lifelong benefits

In the .claude/skills/last30days/ directory, there is a SKILL.md where you can preset your common research preferences (technical/community/business). Configure it once for more precise results in future queries.

Tip 2: Use --deep mode for important research

The default mode balances speed and depth. If you are conducting important technical selection or competitive analysis, add the --deep parameter to run an additional round of supplementary searches, significantly improving result quality, though it will increase the time from 5 minutes to 10-15 minutes.

Tip 3: Cross-platform convergence is the strongest signal

The output report includes a “cross-platform convergence” metric. When the same viewpoint appears on Reddit, HN, and X simultaneously, the confidence level is highest. Focus on these high-convergence conclusions rather than single hot posts from one platform.

Tip 4: Use the dashboard feature for continuous monitoring

If you have several topics you want to keep an eye on (e.g., “our competitors’ dynamics”), set them in the monitoring list and run them regularly with CI/CD to automatically generate daily intelligence reports without manual operation.

Tip 5: Use --emit=json to integrate with your knowledge base

Output in JSON format, then write a simple script to push the results to Notion, Feishu, Obsidian, etc., building your own AI community intelligence archive that becomes more valuable as you accumulate data.

Value for Enterprises and Individuals

For Enterprise Users:

For Individual Users:

A developer shared that using last30days for technical selection compressed two days of competitive research into half a day, with more comprehensive conclusions due to coverage of platforms that previously lacked time for manual checks. This efficiency boost is tangible.

Cost of Use

The project itself is completely free, but you will need to pay for the following services:

Overall Cost Estimation:

• Light Users (1-3 queries daily): approx. $30-50/month
• Moderate Users (5-10 queries daily): approx. $80-150/month
• Heavy/Enterprise Users: assess specific API usage

Comparison with Commercial Tools: Similar commercial intelligence tools (like Brandwatch, Mention, etc.) can easily exceed $500+/month, making last30days a cost-effective option. Of course, there are differences in functional depth, mainly depending on your needs.

Official Website and Installation Links

Minimum Environment Requirements:

• Node.js 18+
• Python 3.10+
• Claude Code or compatible AI Agent framework

Overall Evaluation

last30days-skill addresses a real pain point: in the information explosion of the AI era, how to efficiently extract valuable signals from noisy community discussions. It is not a silver bullet; configuration has a threshold, and search results are not 100% accurate—but it automates the task of “multi-platform manual research,” and being free and open-source makes it well worth your time to set it up once. For me, the most valuable feature is the integration of the Polymarket predictive market—those betting real money are often closer to the truth than keyboard warriors.

Rating (Out of 5 Stars)

Usability:
(3/5) Command-line operation, API configuration has a threshold
Functionality:
(5/5) Coverage of 10+ platforms + intelligent synthesis, top in the industry
Stability:
(4/5) Depends on external APIs, occasional fluctuations
Scalability:
(5/5) Open-source MIT, fully customizable, active community
Cost-Effectiveness:
(5/5) Open-source free, API costs far lower than commercial tools

Overall Score: 4.4/5 Stars

One-Sentence Summary

If you spend over 2 hours a week manually scrolling through Reddit/HN/X for AI community intelligence, last30days-skill is the tool you need—compressing a 2-hour information chore into a 10-minute command.