Last30Days is a recency-focused research skill that searches recent public discussion and engagement signals across several platforms, then synthesizes a brief with citations. Its broad connector surface makes source availability and credential review central to safe use.
Key takeaways
- The SKILL.md lists Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub and web research among its sources.
- Some sources work without configuration, while others use optional API keys, tokens or browser cookies.
- Engagement is an attention signal, not proof that a claim is true.
What does Last30Days do?
The skill combines platform-specific search, date filtering, engagement scoring, clustering and synthesis. Its metadata lists many optional environment variables, including service API keys and social-platform credentials. The README says Reddit, Hacker News, Polymarket and GitHub can work immediately, while a setup flow enables more sources.
How does the workflow work?
- Define the topic and an exact recency window.
- Run source health checks and enable only the platforms needed for the question.
- Collect posts, transcripts, repository activity and web sources with dates and links.
- Separate what people are discussing from what authoritative evidence confirms, then synthesize the brief.
The exact result still depends on the model, the files available in the working environment, and the permissions granted to that environment. This page documents the repository instructions; Anavem did not run an end-to-end benchmark of the skill.
What does a useful first task look like?
Research a tightly defined topic over a dated 30-day window using only the sources needed for the question. Require links, publication dates and a separate section for primary-source confirmation. Treat repeated social claims as a discussion cluster until an authoritative source verifies them.
How can you verify the result?
- Open a sample of citations and confirm date, author and claim context.
- Separate discussion volume from verified fact and authoritative confirmation.
- Record unavailable or rate-limited sources instead of silently treating them as empty.
A successful run should produce an output you can inspect independently. If the result cannot be checked against a file, rendered artifact, source reference or explicit acceptance criterion, narrow the task before relying on it.
When is it a good fit?
- Finding current community language, objections and emerging themes.
- Comparing recent discussion across several public platforms.
- Creating a sourced briefing that preserves links and publication dates.
Choose it when those tasks match the documented scope. A popular repository is not evidence that a skill is appropriate for confidential or production data.
What should you review before installing it?
- Every optional token, cookie and API key requested by the chosen connectors.
- Platform terms, scraping limits and whether authentication data is stored locally.
- The distinction between popularity, sentiment, market odds and verified fact.
Read the current SKILL.md and every bundled script before installation. Pin a reviewed commit when repeatability matters, then test with non-sensitive data and the narrowest available permissions.
When is it not the right choice?
- Using social engagement as factual confirmation.
- Collecting private or restricted data without authorization.
- Research where an official primary source is required but not consulted.
Why is it included in this research set?
GitHub’s API reported 63,449 stars and 5,529 forks for mvanhorn/last30days-skill on 2026-10-03. The public Agent Skills trend index placed it in the top ten and recorded 662 stars added over seven days.
GitHub stars and short-term growth are discovery signals, not quality scores. They do not prove security, correctness, maintained compatibility, or individual-skill usage.