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last30days-skill's 5 Hidden Uses 🔥

What if your next sales call started with you already knowing everything your prospect has posted, built, and shipped in the last 30 days — not from LinkedIn, but from Reddit, X, GitHub, and Polymarket?

That's exactly what /last30days does. And with 29,082 GitHub stars in a single day, this agent skill has become the research engine that senior engineers, founders, and PMs are using to replace their 90-minute pre-meeting scroll sessions with a single command.

The concept is simple: instead of one search engine, it bridges Reddit, X, YouTube, HN, Polymarket, GitHub, TikTok, Bluesky, and more — scored by upvotes, likes, and real-money odds. Then an AI synthesis agent produces a brief grounded in what actual people are saying, not what editors decided to publish.

Most people use it for competitor research and meeting prep. But the five hidden uses below reveal capabilities most users never discover on day one.

Hidden Use #1: Shareable HTML Briefs That Look Like a Report, Not a Prompt Dump

Most users run /last30days and copy-paste the markdown into Slack. That's the obvious use. But run it with --emit=html and the skill saves a fully self-contained, dark-mode, print-friendly HTML file — inline CSS, system-font fallbacks, no external dependencies, no JavaScript, works offline.

/last30days OpenClaw --emit=html
# or in plain language:
/last30days OpenClaw, give me a shareable HTML brief
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The file lands in ~/Documents/Last30Days/{topic}-brief.html and the chat response ends with the file path. You can open it, drag it into a Notion page, attach it to a Linear ticket, or email it to a colleague who doesn't have AI access.

The result: A polished, branded research brief that looks like it came from a $200/mo tool — no markdown rendering required, no formatting cleanup, no "can you read this?" moments in Slack.

Data sources: GitHub 29,082 Stars (mvanhorn/last30days-skill), v3 engine changelog, README install matrix.

Hidden Use #2: Intelligent Search Resolves People to Communities Before It Fires a Single Query

The v3 engine has a "pre-research brain" — it resolves your topic to the right people, communities, hashtags, and subreddits before the search begins. Type "OpenClaw" and it figures out @steipete (Peter Steinberger, the creator), r/openclaw, r/ClaudeCode, and the right YouTube channels — all automatically.

This means you don't need to know which subreddits to target. The engine does it for you.

/last30days Peter Steinberger
# Resolves to: @steipete on X, steipete on GitHub, r/openclaw, r/ClaudeCode
# Shows: 22 PRs merged across 3 repos at 85% merge rate
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For a topic that is a person, the engine switches to GitHub person-mode — author-scoped queries instead of keyword search. It shows what they're shipping, where it's landing, and what the community thinks.

/last30days steipete --github-user=steipete
# Shows: own projects with README summaries, star counts, top feature requests
# Release notes for what shipped this month
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The result: Research that would normally take 45 minutes of manual subreddit discovery — done in 90 seconds with entity-aware precision.

Data sources: v3 engine changelog ("Intelligent search" section), GitHub API for steipete repos.

Hidden Use #3: Cross-Source Cluster Merging Eliminates Duplicate Noise

When the same story breaks on Reddit, X, and YouTube simultaneously, v2 showed three separate entries. v3 merges them into one cluster using entity-based overlap detection — even when the titles use completely different words.

A music festival announced on Reddit, discussed on X with ticket price speculation on TikTok = one cluster, not three.

/last30days "Kanye West"
# Merges: Reddit threads (23 threads, 86K upvotes)
#       + X posts (27 posts)
#       + YouTube videos (17 videos)
#       + Polymarket: "Will Kanye tweet again?" 86% Yes
# = one coherent narrative, not three parallel feeds
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The result: A clean synthesis that reads like one journalist covered the story — not three researchers who never talked to each other.

Data sources: README "Cross-source cluster merging" section, Kanye West example in README.

Hidden Use #4: Single-Pass Comparisons Complete in 3 Minutes Instead of 12+

The old engine ran comparison queries serially. "CLI vs MCP" meant three serial passes, 12+ minutes total. v3 runs one pass with entity-aware subqueries for both sides simultaneously — same depth, 3 minutes.

/last30days "CLI vs MCP"
# v3: one pass with entity-aware subqueries for both sides
# v2: three serial passes (12+ minutes)
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The --competitors flag auto-discovers the top 2 peers via WebSearch and fans out 3 full pipelines in parallel, saving a *-raw.md file per entity and merging them into a 3-way comparison.

/last30days OpenAI --competitors
# Auto-resolves: Anthropic, xAI as top 2 peers
# Runs 3 full pipelines in parallel
# Saves: openai-raw.md, anthropic-raw.md, xai-raw.md
# Merges into: 3-way comparison
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The result: A structured 3-way competitive analysis that would normally require three separate research sessions — done in a single command with live GitHub star counts pulled from the API, not stale blog posts.

Data sources: README "Single-pass comparisons" and "Auto-discovered competitor comparisons" sections.

Hidden Use #5: Best Takes — The Cleverest One-Liners Get Surfaced, Not Buried

v3 has a second judge that scores every result for humor, wit, and virality alongside relevance. The result: every brief ends with a "Best Takes" section — cleverest one-liners, most viral quotes, reactions that make you want to share the research.

"Tommy Lloyd's 'My Michael Jordan is Steve Kerr' scores low on relevance to 'Arizona Basketball' but off the charts on fun." — now surfaced in the brief, not buried in a comment thread.

/last30days "Arizona Basketball"
# Best Takes: "My Michael Jordan is Steve Kerr" (viral, funny, relevant to coaching philosophy)
# Surface level: coaching decisions, not just game results
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The result: Research that captures not just what happened, but how people are joking about it — the cultural layer that makes your brief feel alive rather than archival.

Data sources: README "Best Takes" section, v3 changelog "Fun judge v2" entry.

Summary

/last30days started as a way to keep up in AI. It became the research engine that replaces 90-minute pre-meeting scrolls with a single command. Here are the five techniques that most users miss on day one:

  1. Shareable HTML briefs (--emit=html) — polished reports, not markdown dumps
  2. Intelligent pre-research — entity resolution before the first API call fires
  3. Cross-source cluster merging — one story, one entry, no duplicates
  4. Single-pass comparisons — 3-way competitive analysis in 3 minutes
  5. Best Takes — humor and wit surfaced alongside facts

The install takes 30 seconds. Reddit, HN, Polymarket, and GitHub work immediately with zero config:

# Claude Code (recommended)
/plugin marketplace add mvanhorn/last30days-skill

# Or via npx (Codex, Cursor, Copilot, Gemini CLI, 50+ hosts)
/plugin install last30days
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Have a use case of your own? Share it in the comments — the best hidden uses end up in the next changelog.

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