RonanCodes/skills/trend-scan

Trend Scan

Parallel fan-out across source scanners. Two modes sharing the same plumbing:

• Discover mode (no topic arg, or --discover) — "what's hot right now?" Pulls each source's hot/front feeds, extracts themes, returns a numbered picklist so the caller can pick one.
• Deep-dive mode (topic arg given) — "tell me everything about X right now." Scans each source for that query, ranks, and synthesises.

The natural loop: /ro:trend-scan → pick a theme → /ro:trend-scan "<theme>".

Usage

# discover
/ro:trend-scan                                   # top 10 themes across hn/x/reddit, last 7d
/ro:trend-scan --discover --since 24h --limit 15 # fresher, more themes
/ro:trend-scan --discover --sources hn,reddit    # skip X if you know nitter's down

# deep-dive
/ro:trend-scan "agent observability"
/ro:trend-scan "MCP servers" --sources hn,reddit --since 7d
/ro:trend-scan "vibe coding" --since 30d --limit 15

Defaults

• Sources: hn,reddit,youtube,producthunt (X and LinkedIn are opt-in — X because nitter mirrors are currently dead, LinkedIn because cookie auth is required)
• Window: 7d
• Limit: 10 items per source in deep-dive; 10 themes total in discover
• Rich mode: auto for YouTube in discover mode (transcript snippets inlined for the ~2-5 high-signal hits per scan). Override with --rich off for a lean scan, or --rich all to transcript every YT hit (slow)

Why rich mode matters

Without inline transcripts, trend-scan surfaces YouTube talks by title and view count only. A keynote like "The Future of MCP" is logged as a high-view hit; the actual substance (new concepts, timelines, quotes the user would want to act on) stays invisible until someone watches it. The auto-rich default closes that gap: for videos that match the trigger heuristic, ~1000 words of captions land inline in the scan output, so the caller (Claude or user) can react immediately.

Precedent: the "server cards" concept went untracked for 4 days in 2026-04 because its keynote was logged by title only. Since fixed.

Mode: Discover

Use this when the caller hasn't named a topic yet.

1. Fan out — pull the most-recent/highest-engagement feed from each source:
   • HN: /ro:hn-scan frontpage --limit 30 + /ro:hn-scan show --since 7d --limit 15
   • Reddit: hit 4–6 curated subs via /ro:reddit-scan r/<sub> --sort top --since week --limit 10 each. Default sub set: r/LocalLLaMA, r/ClaudeAI, r/singularity, r/programming, r/hackernews, r/OpenAI
   • YouTube: /ro:youtube-scan --rich auto in discover mode — runs 3 seed searches (AI, coding, new tool), dedupe by URL, sort by views, and inlines transcript snippets for hits matching the rich heuristic (default: views ≥ 50k OR title contains mcp/agent/claude/anthropic/openai/protocol/spec/sdk)
   • Product Hunt: /ro:producthunt-scan — today's feed, cluster by theme keywords
   • X: /ro:x-scan "search:AI" (or a set of broad seeds) — be ready for nitter failure, skip cleanly
2. Normalise — same shape as deep-dive (see below).
3. Extract themes — cluster titles/gists into themes:
   • Extract ~3-word noun phrases from titles (e.g. "agent observability", "MCP servers", "prompt caching")
   • Group items sharing a linked URL or near-identical titles
   • Score each theme by Σ(item ranks) + cross-source bonus (appearing on 2+ sources = ×1.5)
4. Return a picklist with brief evidence per theme — never a single-item verdict. The caller picks.

Discover output

# What's trending  (window: <since>, sources: <which ran>)

1. **<Theme>** (<N> mentions, sources: hn, x)
   - [HN, 700pts] <representative title>  https://news.ycombinator.com/item?id=<id>
   - [X, 4.2k♥] @<user>: <gist>            https://x.com/...
   <1-line gist of what the theme is actually about>

2. **<Theme>** ...

...

## Next step
Pick one with: `/ro:trend-scan "<theme>"` for a deep-dive.

Mode: Deep-dive

Use this when the caller has named a topic.

1. Fan out — run the selected source scans in parallel. Each is an independent curl call, so they complete in roughly the time of the slowest one.
2. Normalise — for each result, capture:
   • source (hn | x | reddit | linkedin)
   • title_or_gist (headline, tweet text, or post summary)
   • url (the thread / post URL)
   • score (points, likes, upvotes — source-specific units)
   • comments (if applicable)
   • author
   • created_at
3. Rank — combine recency + engagement. Simple heuristic:
   • rank = score / (age_hours + 2)^1.5
   • Cap score per source (HN points scale differently from X likes) by computing per-source z-scores before blending, then sort across sources.
4. Cluster (optional) — if two items clearly reference the same thing (same linked URL, or near-identical titles), merge them into a single entry with sources: [hn, x].
5. Summarise — surface the top N (default 15) across all sources, plus a short "themes" paragraph listing patterns you spot.

Deep-dive output

# Trend: "<topic>"  (window: <since>, sources: <which ran>)

## Themes
- <one-line pattern #1>
- <one-line pattern #2>
- <one-line pattern #3>

## Top items

1. [HN, <points>pts, <comments>c] <title>
   https://news.ycombinator.com/item?id=<id>
   <external url if relevant>
   <1-line takeaway>

2. [X, <likes>♥] @<user>: <gist>
   https://x.com/<user>/status/<id>

3. [Reddit, <score>, r/<sub>] <title>
   https://reddit.com/<permalink>

...

## Sources that failed or were skipped
- LinkedIn: not requested (opt-in)
- X: no results (nitter mirrors down — retry later with `--sources hn,reddit`)

Error handling

• Partial failure is OK — if X fails but HN + Reddit succeed, return what you have with a note under "Sources that failed".
• Total failure — if every source fails, explain why (rate limit, network) and suggest /ro:perplexity-research as a fallback.

When to use which tool

| Task | Use | |---|---| | "What's hot right now?" (no topic) | /ro:trend-scan (discover mode) | | "What's hot on this topic?" | /ro:trend-scan "<topic>" (deep-dive) | | "Fetch this specific thread" | /ro:hn-scan <url> or /ro:reddit-scan <url> | | "Read this tweet" | /ro:x-scan <url> | | "Research a topic with citations" | /ro:perplexity-research | | "Keep this content long-term" | llm-wiki /ingest |

Dependencies

Inherits dependencies from the scanners it calls (curl, optional firefox-cookies for LinkedIn).

See also

• /ro:hn-scan, /ro:x-scan, /ro:reddit-scan, /ro:linkedin-scan — the per-source skills this orchestrates.
• /ro:perplexity-research — citation-backed AI research; complementary (perplexity = depth, trend-scan = breadth).
• llm-wiki/.claude/skills/ingest — when you want to keep the findings.