recent-research

Uses local SearXNG (no external API keys needed) to research Reddit, X, YouTube, and the web.

Step 1: Parse Intent

Extract before doing anything:

• TOPIC: What to research. Strip meta-words (best, top, latest, techniques, what are people saying about, prompting for).
• TARGET_TOOL: If user says "for [tool]" — otherwise "unknown"
• QUERY_TYPE: PROMPTING | RECOMMENDATIONS | NEWS | GENERAL

Display before running tools:

Researching **{TOPIC}** across Reddit, X, YouTube + web (last 30 days).
- Type: {QUERY_TYPE} | Tool: {TARGET_TOOL or "not specified"}
Starting now...

Step 2: Run Research Script

node <skill>/scripts/research.mjs "{TOPIC}" [--days=30] [--quick] [--deep]

Flags (pass through from user): --days=N, --quick (5 results/source), --deep (15 results/source).

Read the full output. It has Reddit, X, YouTube, and Web sections — all needed for stats.

Step 3: Supplemental Searches

After the script, run 2 targeted searches with the existing searxng script:

# Match QUERY_TYPE:
# PROMPTING: "{TOPIC} techniques examples 2025 OR 2026"
# RECOMMENDATIONS: "best {TOPIC} recommendations list"
# NEWS: "{TOPIC} news announcement update 2026"
# GENERAL: "{TOPIC} discussion community 2026"
node <searxng-script>/searxng.mjs "..." --count=5

If a Reddit thread looks rich (lots of comments), fetch it with web_fetch for upvote counts and top comment quotes.

Step 4: Synthesize

Lead with what people are saying, not what publications wrote. Cite @handles and r/subreddits. Use publication names (not URLs). Cite sparingly — one source per point.

Output sequence:

1. What I learned — 3-5 topic threads, 1-2 sentences each, with citations:

**{Topic A}** — [what people are saying], per @handle or r/sub

**{Topic B}** — [key insight], per r/sub

2. Key Patterns — 3-5 actionable insights:

1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per [publication]

For RECOMMENDATIONS: List specific names with mention counts and sources:

🏆 Most mentioned:
[Item] — Nx | Sources: @handle, r/sub, blog

3. Stats block (use these exact characters):

---
✅ Research complete
├─ 🟠 Reddit: {N} threads │ {N} upvotes (if fetched)
├─ 🔵 X: {N} posts
├─ 🔴 YouTube: {N} videos
├─ 🌐 Web: {N} pages
└─ 🗣️ Top sources: r/{sub}, @{handle}
---

4. Invitation — 2-3 specific suggestions from what you actually found:

• PROMPTING: "I'm now an expert on {TOPIC} for {TARGET_TOOL}. What do you want to make? For example: ..."
• RECOMMENDATIONS: "I'm now an expert on {TOPIC}. Want me to go deeper? For example: ..."
• NEWS/GENERAL: "I'm now an expert on {TOPIC}. Some things you could ask: ..."

Make suggestions specific to what you found — not generic.

Step 5: Wait, Then Respond

Stop after the invitation. When the user responds:

• Question → answer from your research (no new searches)
• "Go deeper" → elaborate from findings
• "Create/make/write a prompt" → write ONE prompt in the format the research actually recommends (JSON if research says JSON, structured if structured)

Prompt format:

Here's your prompt for {TARGET_TOOL}:

---
[Prompt — in the format the research recommends, ready to paste]
---

This uses [1-line explanation of which research insight you applied].

After each prompt:

---
📚 Expert in: {TOPIC}
📊 Based on: {N} Reddit + {N} X + {N} YouTube + {N} web sources
Want another? Tell me what you're creating next.

Notes

• SearXNG time_range=month covers ~30 days. For --days=7, script uses week.
• X/Twitter results come via Google/Bing indexing of x.com — coverage varies by topic popularity.
• YouTube results are titles/descriptions only (no transcript extraction). If a video is highly relevant, use web_fetch on the YouTube URL for description details.
• After research, you are the expert. Don't re-search unless the user asks about a different topic.