last30days: Research Any Topic from the Last 30 Days Skill

last30days: Research Any Topic from the Last 30 Days

Research ANY topic across Reddit, X, YouTube, Hacker News, and the web. Surface what people are actually discussing, recommending, and debating right now.

Reference Files

examples.md - Complete output examples for all query types
advanced.md - Depth flags, agent mode, Polymarket, sparse results

Step 1: Parse User Intent

Before doing anything, parse the user's input:

TOPIC — what they want to learn about
QUERY_TYPE — one of:
- PROMPTING — "X prompts", "prompting for X" → wants techniques and copy-paste prompts
- RECOMMENDATIONS — "best X", "top X", "what X should I use" → wants a ranked list
- NEWS — "what's happening with X", "X news" → wants current events
- GENERAL — anything else → wants broad understanding
TARGET_TOOL — where they'll use prompts (if specified). Only set when query contains "for [tool]".

Common patterns:

[topic] for [tool] → tool is specified
best [topic] or top [topic] → RECOMMENDATIONS
Just [topic] → GENERAL

Do NOT ask about target tool before research. Research first, ask after.

Display your parsing before any research:

I'll research {TOPIC} across Reddit, X, and the web to find what's been discussed in the last 30 days.

Parsed intent:
- TOPIC = {TOPIC}
- QUERY_TYPE = {QUERY_TYPE}

Research typically takes 2-8 minutes. Starting now.

Only include TARGET_TOOL in the parsed intent display if one was detected.

Step 2: Research Across All Sources

Search five sources for discussions from the last 30 days (or --days=N if specified). Run searches in parallel where possible. Use --quick or --deep to control search depth (see advanced.md).

2a. Platform-Scoped Web Searches

Run site-scoped web searches to hit each platform:

| Source | Search Strategy | | --------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | Reddit | site:reddit.com {TOPIC} — look for threads with upvote/comment counts. Try subreddit-specific searches if you find relevant subs. | | X/Twitter | site:x.com {TOPIC} — look for posts with engagement | | YouTube | site:youtube.com {TOPIC} — look for videos with view counts | | Hacker News | site:news.ycombinator.com {TOPIC} or site:hn.algolia.com {TOPIC} — stories, Show HN, Ask HN |

2b. Supplemental Web Search

After platform searches, run general web searches to fill gaps. Adapt queries to QUERY_TYPE:

RECOMMENDATIONS → "best {TOPIC} recommendations", "most popular {TOPIC}"
NEWS → "{TOPIC} news 2026", "{TOPIC} announcement update"
PROMPTING → "{TOPIC} prompts examples 2026", "{TOPIC} techniques tips"
GENERAL → "{TOPIC} 2026", "{TOPIC} discussion"

2c. Enrichment

Fetch full page content selectively on high-signal results — X posts with engagement data, HN threads with deep discussion. Reddit and YouTube enrichment often hits rate limits or returns noise; skip unless the snippet alone is insufficient.

If a platform returns 0 results, omit it from synthesis and stats entirely.

See examples.md for complete output examples. See advanced.md for depth flags, agent mode, Polymarket, and sparse results handling.

Step 3: Synthesize

Weight sources by signal quality:

Reddit/X — highest (real engagement: upvotes, likes)
YouTube — high (views, likes, transcript content)
Hacker News — medium-high (technical community, points + comments)
Web — lower (no engagement data)

Cross-platform signals are strongest. When the same story appears on multiple platforms, lead with it.

Ground in Research, Not Pre-existing Knowledge

Read research output carefully. Use exact product/tool names, specific quotes, and what sources actually say. Do NOT substitute your own knowledge for what the research found.

Step 4: Present Results

Synthesis (adapt to QUERY_TYPE)

If RECOMMENDATIONS — show a ranked list:

🏆 Most mentioned:

[Name] — {n}x mentions
Use Case: [what it does]
Sources: @handle1, r/sub, blog.com

[Name] — {n}x mentions
...

Notable mentions: [others with 1-2 mentions]

If PROMPTING/NEWS/GENERAL — show narrative with patterns:

What I learned:

**{Topic 1}** — [1-2 sentences, per @handle or r/sub]

**{Topic 2}** — [1-2 sentences, per @handle or r/sub]

KEY PATTERNS from the research:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per @handle

Citation Rules

Priority (most to least preferred):

@handles from X — "per @handle"
r/subreddits — "per r/subreddit"
YouTube channels — "per [channel] on YouTube"
HN — "per HN"
Web — ONLY when social sources don't cover that fact

Lead with people, not publications. Never paste raw URLs — use publication names.

Stats Block

Summarize what the research actually found. Omit any platform that returned nothing.

---
├─ 🟠 Reddit: {N} threads │ r/{sub1}, r/{sub2}
├─ 🔵 X: {N} posts │ {N} likes │ top: @{handle}
├─ 🔴 YouTube: {N} videos
├─ 🟡 HN: {N} stories │ {N} points
├─ 🌐 Web: {N} pages — Source Name, Source Name
└─ 🗣️ Top voices: @{handle1}, @{handle2} │ r/{sub1}
---

Use approximate counts where exact engagement data wasn't available. Never fabricate precision.

Step 5: Expert Mode

After research, you are an expert on this topic.

DO NOT run new searches for follow-up questions — answer from research
If user asks a question → answer from findings with citations
If user asks for a prompt → write one copy-paste-ready prompt using research insights, appropriate for TARGET_TOOL if set
If user describes something to create → write one tailored prompt

When writing prompts:

Match the FORMAT the research recommends (JSON, structured, natural language)
Use specific patterns/keywords discovered in research
Ready to paste with zero edits (or minimal [PLACEHOLDERS])

Only do new research if the user asks about a DIFFERENT topic.

Agent Skills: last30days: Research Any Topic from the Last 30 Days

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