| name | last30days-skill |
| description | Deep research engine covering the last 30 days across 10+ sources -- Reddit, X/Twitter, YouTube, TikTok, HackerNews, Polymarket, Bluesky, and the web. Synthesizes findings into grounded, cited reports. Use when: researching trending topics, competitive intelligence, understanding what people are saying about a subject right now. |
| license | MIT |
| compatibility | Node.js 18+, requires SCRAPECREATORS_API_KEY |
| metadata | {"author":"terminal-skills","version":"2.9.5","category":"research","tags":["research","trends","deep-research","social-media","competitive-intelligence"]} |
Last 30 Days Research
Overview
Research any topic across Reddit, X/Twitter, Bluesky, Truth Social, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the web. Surfaces what people are actually discussing, recommending, betting on, and debating right now. Synthesizes findings into a grounded report with citations and engagement stats.
Instructions
Step 1: Parse User Intent
Before running research, extract from the user's input:
- TOPIC: What they want to learn about
- TARGET_TOOL: Where they will use the results (if specified, otherwise "unknown")
- QUERY_TYPE: One of RECOMMENDATIONS, NEWS, COMPARISON, PROMPTING, or GENERAL
Display your parsing to the user before calling any tools:
I'll research {TOPIC} across Reddit, X, Bluesky, YouTube, TikTok, and the web.
Parsed intent:
- TOPIC = {TOPIC}
- TARGET_TOOL = {TARGET_TOOL or "unknown"}
- QUERY_TYPE = {QUERY_TYPE}
Research typically takes 2-8 minutes. Starting now.
Step 2: Resolve X Handle (Optional)
If the topic could have its own X/Twitter account (people, brands, products, companies), do a quick WebSearch to find their handle. Pass it as --x-handle={handle} to search their posts directly. Skip for generic concepts.
Step 3: Run the Research Script
Run in foreground with a 5-minute timeout:
for dir in "." "${CLAUDE_PLUGIN_ROOT:-}" "$HOME/.claude/skills/last30days" \
"$HOME/.agents/skills/last30days" "$HOME/.codex/skills/last30days"; do
[ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
done
python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --emit=compact --no-native-web --save-dir=~/Documents/Last30Days
Read the entire output -- it contains Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, and web sections.
Step 4: Supplement with WebSearch
After the script finishes, run WebSearch queries based on QUERY_TYPE:
- RECOMMENDATIONS:
best {TOPIC} recommendations, most popular {TOPIC}
- NEWS:
{TOPIC} news 2026, {TOPIC} announcement update
- PROMPTING:
{TOPIC} prompts examples 2026
- GENERAL:
{TOPIC} 2026, {TOPIC} discussion
- COMPARISON: Run three passes (TOPIC_A alone, TOPIC_B alone, "A vs B")
Exclude reddit.com and x.com (already covered by script).
Step 5: Synthesize and Present
Weight sources by engagement signals: Reddit/X highest (upvotes, likes), YouTube high (views, transcripts), TikTok high (viral signal), web lowest (no engagement data). Cross-platform signals are strongest evidence.
Present in this order:
- "What I learned" synthesis with citations (prefer @handles and r/subreddits over web sources)
- Stats block showing counts per platform (omit platforms with 0 results)
- Invitation with 2-3 specific follow-up suggestions based on actual findings
Cite sources sparingly: 1 source per pattern, short format ("per @handle" or "per r/sub"). Never paste raw URLs. Use publication names for web sources.
Step 6: Handle Follow-ups
After presenting results, stay in expert mode. Answer follow-up questions from your research without new searches. Only run new research if the user asks about a different topic. Write prompts only when explicitly requested.
Options: --days=N (lookback period), --quick (fewer sources), --deep (comprehensive), --agent (non-interactive output).
Examples
Example 1: Research a Developer Framework
User asks: "What are developers saying about Bun runtime?"
Parsed intent:
- TOPIC = Bun runtime
- TARGET_TOOL = unknown
- QUERY_TYPE = GENERAL
Script returns 22 Reddit threads (1,840 upvotes), 35 X posts (4,200 likes), 8 YouTube videos (120K views). Key findings: developers praise startup speed (per @jaraborner), Bun 1.2 announcement drove Reddit discussion (per r/javascript), YouTube benchmarks show 3x faster cold starts vs Node (per Fireship). Pattern: adoption growing in CLI tools but not production servers yet (per r/node).
Example 2: Competitive Comparison
User asks: "Plaud vs Granola for AI meeting notes"
QUERY_TYPE = COMPARISON. Run three research passes: "Plaud" alone, "Granola" alone, "Plaud vs Granola". Synthesize as side-by-side comparison with community sentiment, strengths, weaknesses, and head-to-head table. Present specific odds and mention counts: "Plaud mentioned 18x across Reddit/X with mixed sentiment on hardware quality; Granola mentioned 12x with strong praise for transcript accuracy (per @sarahk_ai)."
Guidelines
- Always display parsed intent before running any tools
- Run the research script in foreground, never in background
- Read the entire script output -- missing sections produces incomplete stats
- Weight engagement-backed sources (Reddit, X, YouTube) over web articles
- Never paste raw URLs in output -- use publication/site names
- For RECOMMENDATIONS queries, extract specific product/tool names, not generic advice
- Polymarket odds are high-signal data -- weave them into narrative as supporting evidence
- Omit any platform line from stats that returned 0 results
- Stay in expert mode after presenting results -- answer follow-ups from existing research
- Only credential used is SCRAPECREATORS_API_KEY; X/Bluesky/Truth Social tokens are optional
- The skill reads public data only and does not post, like, or modify content on any platform
