last30days: The day I stopped writing stale AI prompts
I used to think my prompts were the problem. Whenever I asked an AI agent to help me with a new framework, it would confidently generate code that was deprecated exactly fourteen months ago. I would sigh, paste the migration guide into the chat, and feel like I was babysitting a brilliant but amnesiac intern.
The problem isn’t the AI. The problem is the training cutoff. We are asking immortal brains to comment on today’s news using yesterday’s newspapers.
Then I found last30days-skill. Its proposition is so blunt it’s almost funny: what if your agent just looked at Twitter and Reddit before it opened its mouth?
The temporal hearing aid
Think of last30days as a temporal hearing aid for your LLM. Instead of answering from a frozen snapshot of the internet, it intercepts your query, scours eight different sources (including X, Reddit, YouTube transcripts, and Hacker News), synthesizes the vibes of the last month, and then gives you an answer.
It doesn’t just keywords-search. It reads the room.
# How you run it inside Claude Code or Open Claw
/last30days "OpenAI's latest o3 model" --days=7Tells the agent to go read human opinions on Reddit and X from the past week before helping you.
Why it actually works
Most RAG (Retrieval-Augmented Generation) pipelines are boring. They search Wikipedia or some corporate knowledge base. last30days searches where actual developers complain.
Here is what happens under the hood when you fire it up:
| Step | What happens | Why it matters |
|---|---|---|
| 1. Source Discovery | Agent searches web for the topic’s official X handle and subreddits. | It finds the actual source, not just random mentions. |
| 2. Deep Fetch | Scrapes X without filters, hits Reddit APIs, downloads YouTube transcripts. | Captures context beyond 280 characters. |
| 3. Enrichment | Fetches real upvotes and engagement metrics. | Prioritizes what people actually care about, not SEO spam. |
| 4. Synthesis | Feeds it all back to your prompt. | You get a grounded, reality-checked response. |
I’ve seen it used heavily with Open Claw, an always-on agent environment. You just add "Competitor X" to a watchlist, set a cron job, and every Monday morning, your bot hands you a customized briefing of what people are complaining about regarding your competitor. It stores this in a local SQLite database. It’s like having a free junior analyst who never sleeps.
The honest tradeoffs
But there is always a catch. You are wiring your agent directly to the firehose of social media.
First, your search queries leave your machine. If you are researching a super-secret internally unannounced project, using a tool that pings third-party APIs (like Brave Search, OpenRouter, or X) is a great way to leak it.
Second, the setup can be finicky. To get the best X results without API limits, you have to provide your own --x-handle authentication tokens (AUTH_TOKEN and CT0 cookies). It vendors a subset of an X GraphQL client, which is clever, but if X changes its undocumented API tomorrow, that integration will break until a patch is merged.
The honest summary
last30days-skill didn’t make LLMs smarter. It just gave them a window to look outside. The gap between “the AI is hallucinating again” and “the AI actually knows what dropped on Hacker News this morning” is exactly one command.
If you are tired of copy-pasting release notes into Claude just to get a working code snippet, you already know why this repo has ten thousand stars. It grounds the intelligence.
mvanhorn/last30days-skill · MIT · 10.7k