Research Current Trends Across Web, Reddit, and X

Researches any topic across Reddit, X, and the web to surface what people are actually discussing, recommending, and debating right now, then synthesizes findings into a single, tailored, copy-paste-ready prompt for whatever tool the user names. It covers four query types: prompting (learn techniques, get prompts), recommendations (get a list of specific named things people mention), news (current events/updates), and general topic understanding.

Use for researching what a community is saying about a topic before writing prompts, choosing tools, or catching up on recent developments - it parses the user's input for TOPIC, an optional TARGET_TOOL, and QUERY_TYPE (recommendations/news/prompting/general) before acting, and deliberately does not ask about the target tool before research runs.

The skill runs in three modes depending on optional API keys: Full Mode (Reddit + X + WebSearch, with engagement metrics), Partial Mode (one of Reddit/X plus WebSearch), or Web-Only Mode (WebSearch only, no engagement data) - keys are entirely optional and setup falls back gracefully:

mkdir -p ~/.config/last30days
cat > ~/.config/last30days/.env << 'ENVEOF'
OPENAI_API_KEY=
XAI_API_KEY=
ENVEOF

Research execution runs ~/.claude/skills/last30days/scripts/last30days.py with the topic, which auto-detects available keys and signals whether WebSearch must supply all data; depth is controlled via --quick (8-12 sources each), default (20-30 each), or --deep (50-70 Reddit, 40-60 X). WebSearch queries are tailored per query type (e.g. best {TOPIC} recommendations for RECOMMENDATIONS, {TOPIC} news 2026 for NEWS) and must use the user's exact terminology rather than substituted terms from the model's own knowledge, since that knowledge may be outdated.

A Judge Agent step synthesizes all sources internally: weighting Reddit/X higher since they carry engagement signals (upvotes, likes), weighting WebSearch lower, identifying patterns that appear across all three sources, noting contradictions, and extracting the top 3-5 actionable insights - critically grounded in what the sources actually say rather than the model's prior assumptions (the source gives an explicit anti-pattern: research about a differently-named product must not be conflated with a similarly-named but distinct one just because both mention "skills").

Output is displayed in a fixed sequence: a "what I learned" section (specific named items with mention counts for RECOMMENDATIONS, or key patterns for other query types), engagement stats (Reddit threads/upvotes, X posts/likes, web pages/domains - or a note to add API keys in web-only mode), and an invitation for the user to share their creative vision. After the user responds, the skill writes exactly one tailored prompt - critically matching whatever format the research itself recommended (JSON, structured parameters, natural language, or keyword lists), never defaulting to plain prose if the research said otherwise. Once research is internalized, follow-up questions are answered from that research rather than triggering new searches, unless the user asks about a genuinely different topic.

Uses OpenAI's web_search tool for Reddit research (via OPENAI_API_KEY) and xAI's x_search tool for X/Twitter research (via XAI_API_KEY), both optional, plus the general WebSearch tool as a universal fallback and supplement.

Anyone who wants to know what a community is actually saying about a topic in the last 30 days - to pick up prompting techniques, get a list of specifically-named recommended tools, or catch up on news - and then get one well-researched, correctly-formatted prompt to use immediately.