arctuition/last30days

last30days

Research a topic the way a sharp human would skim the last month of the internet: pull what real people actually engaged with across Reddit, Hacker News, X/Twitter, YouTube, and GitHub within a recent time window, weight it by engagement and cross-source agreement, and synthesize a grounded brief with inline citations.

This is a lightweight, zero-dependency take on mvanhorn/last30days-skill. It runs entirely on the built-in WebSearch and WebFetch tools — no API keys, no Python engine.

When to use this vs. deep-research: reach for last30days when recency and community signal are the point (trend discovery, product/tool selection, "is X any good lately", sentiment, current events). Reach for deep-research when you need authoritative, fact-checked answers regardless of date.

Prerequisites

• WebSearch and WebFetch are built-in — no setup. If WebSearch isn't available in the session, load it first (ToolSearch → select:WebSearch,WebFetch).
• WebSearch is US-region and works best on indexed web. Reddit, Hacker News, GitHub, and YouTube are reliably searchable. X/Twitter is partially indexable — treat it as best-effort and lean on the others when it's thin.

Workflow

Step 0 — Scope the query

1. Topic. Restate the topic in one line so the user can correct it before you burn searches.
2. Time window. Default to the last 30 days. Honor explicit windows: "this week" → 7d, "past two weeks" → 14d, "last N days/months". Today's date is known from context — compute the cutoff date and carry it into every search as a recency anchor (the current month + year, e.g. "June 2026", and terms like "this week"/"latest").
3. Query type (shapes synthesis):
   • Trend discovery — what's emerging, what shifted.
   • Product / tool selection — what practitioners actually pick and why; quote real usage.
   • Sentiment — how people feel, the split between camps.
   • News / event monitoring — what happened and the reactions.
4. Keyword traps. Flag ambiguous names (a tool that shares a word with something common) and disambiguate in the query before searching.

Step 1 — Resolve entities & plan queries

Before firing searches, name the concrete places the conversation lives:

• Subreddits likely to discuss it (e.g. r/LocalLLaMA, r/programming).
• X/Twitter handles — the project, its founder, loud practitioners.
• GitHub repo/org (releases, PR activity, star momentum are strong recency signals).
• YouTube channels likely to have recent walkthroughs/reviews.

Draft 3–6 targeted subqueries, each pointed at one source family (see references/source-recipes.md for exact WebSearch recipes per platform).

Step 2 — Parallel multi-source search

Fire the planned searches in a single message (parallel tool calls) so they run concurrently. Use allowed_domains to pin each search to a source, and always include the recency anchor:

• Reddit → allowed_domains: ["reddit.com"]
• Hacker News → allowed_domains: ["news.ycombinator.com"] (and the Algolia API via WebFetch, see recipes)
• GitHub → allowed_domains: ["github.com"]
• YouTube → allowed_domains: ["youtube.com"]
• X/Twitter → allowed_domains: ["x.com", "twitter.com"] (best-effort)
• Open web → a plain WebSearch with the recency anchor, no domain pin, to catch blogs/news.

Step 3 — Deepen the high-signal hits

For the strongest 4–8 results, WebFetch the page to pull the actual substance, not just the snippet:

• Reddit thread → the claim + top comments + rough upvote/comment counts.
• HN thread → top comments and the contrarian replies.
• GitHub → latest release notes, recent PR/commit themes, star trajectory.
• YouTube → title, channel, view count, and the gist (description/transcript if reachable).

Step 4 — Score & converge

Rank by signal, not mention-count:

• Engagement weight — upvotes, comments, stars, views, retweets. A 2k-upvote thread outweighs ten driveby mentions.
• Recency — inside the window; surface "as of last week" shifts.
• Cross-source convergence — the same claim appearing on ≥2 platforms is high confidence; flag single-source claims as such.
• Per-voice cap — don't let one loud account or one mega-thread dominate; cap ~3 items per author/source and diversify.
• Separate signal from noise — drop SEO spam, ancient reposts surfacing late, and off-topic keyword collisions.

Step 5 — Synthesize the brief

Output grounded prose, not an evidence dump. Structure:

🗓️ last30days · <topic> · window: <start>–<today>

**TL;DR** — 3–5 bullets of what someone busy needs to know.

**Key themes**
- <Theme>: what's being said, who's saying it, confidence (multi-source vs single), with inline links.
- ...

**Notable takes** — a few real quotes/positions with source links (never invent quotes or titles).

**Emerging / shifting** — what's newer than the rest, what changed this window.

**Contrarian & risks** — the dissent, the caveats, the "don't" warnings.

**Gaps** — what you couldn't verify or where coverage was thin (e.g. X was sparse).

---
Scanned <N> sources across <platforms> · window <N> days · <date>

Rules:

• Every non-obvious claim gets an inline markdown link to its source.
• Prefer convergent, multi-source claims; label single-source ones as unconfirmed.
• No fabricated titles, quotes, counts, or links — if you didn't see it, don't assert it.
• Lead with the synthesis; keep the raw list in the links, not the body.

Notes & limits

• WebSearch is US-indexed; non-English/regional communities and X/Twitter may be under-covered — say so in Gaps rather than papering over it.
• This skill reports the current conversation on a topic; it is not a fact-checker. For "is this claim actually true," hand off to deep-research.