last30days: Research Any Topic from the Last 30 Days
Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
Use cases:
- Prompting: "photorealistic people in Nano Banana Pro", "Midjourney prompts", "ChatGPT image generation" → learn techniques, get copy-paste prompts
- Recommendations: "best Claude Code skills", "top AI tools" → get a LIST of specific things people mention
- News: "what's happening with OpenAI", "latest AI announcements" → current events and updates
- General: any topic you're curious about → understand what the community is saying
CRITICAL: Parse User Intent
Before doing anything, parse the user's input for:
- TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
- TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
- QUERY TYPE: What kind of research they want:
- PROMPTING - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
- RECOMMENDATIONS - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
- NEWS - "what's happening with X", "X news", "latest on X" → User wants current events/updates
- GENERAL - anything else → User wants broad understanding of the topic
Common patterns:
[topic] for [tool]→ "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED[topic] prompts for [tool]→ "UI design prompts for Midjourney" → TOOL IS SPECIFIED- Just
[topic]→ "iOS design mockups" → TOOL NOT SPECIFIED, that's OK - "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
IMPORTANT: Do NOT ask about target tool before research.
- If tool is specified in the query, use it
- If tool is NOT specified, run research first, then ask AFTER showing results
Store these variables:
TOPIC = [extracted topic]TARGET_TOOL = [extracted tool, or "unknown" if not specified]QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]
Setup Check
The skill works in three modes based on available API keys:
- Full Mode (both keys): Reddit + X + WebSearch - best results with engagement metrics
- Partial Mode (one key): Reddit-only or X-only + WebSearch
- Web-Only Mode (no keys): WebSearch only - still useful, but no engagement metrics
API keys are OPTIONAL. The skill will work without them using WebSearch fallback.
First-Time Setup (Optional but Recommended)
If the user wants to add API keys for better results:
mkdir -p ~/.config/last30days
cat > ~/.config/last30days/.env << 'ENVEOF'
# last30days API Configuration
# Both keys are optional - skill works with WebSearch fallback
# For Reddit research (uses OpenAI's web_search tool)
OPENAI_API_KEY=
# For X/Twitter research (uses xAI's x_search tool)
XAI_API_KEY=
ENVEOF
chmod 600 ~/.config/last30days/.env
echo "Config created at ~/.config/last30days/.env"
echo "Edit to add your API keys for enhanced research."DO NOT stop if no keys are configured. Proceed with web-only mode.
Research Execution
IMPORTANT: The script handles API key detection automatically. Run it and check the output to determine mode.
Step 1: Run the research script
TOPIC_FILE="$(mktemp)"
trap 'rm -f "$TOPIC_FILE"' EXIT
cat <<'LAST30DAYS_TOPIC' > "$TOPIC_FILE"
$ARGUMENTS
LAST30DAYS_TOPIC
python3 ~/.claude/skills/last30days/scripts/last30days.py "$(cat "$TOPIC_FILE")" --emit=compact 2>&1The script will automatically:
- Detect available API keys
- Show a promo banner if keys are missing (this is intentional marketing)
- Run Reddit/X searches if keys exist
- Signal if WebSearch is needed
Step 2: Check the output mode
The script output will indicate the mode:
- "Mode: both" or "Mode: reddit-only" or "Mode: x-only": Script found results, WebSearch is supplementary
- "Mode: web-only": No API keys, Claude must do ALL research via WebSearch
Step 3: Do WebSearch
For ALL modes, do WebSearch to supplement (or provide all data in web-only mode).
Choose search queries based on QUERY_TYPE:
If RECOMMENDATIONS ("best X", "top X", "what X should I use"):
- Search for:
best {TOPIC} recommendations - Search for:
{TOPIC} list examples - Search for:
most popular {TOPIC} - Goal: Find SPECIFIC NAMES of things, not generic advice
If NEWS ("what's happening with X", "X news"):
- Search for:
{TOPIC} news 2026 - Search for:
{TOPIC} announcement update - Goal: Find current events and recent developments
If PROMPTING ("X prompts", "prompting for X"):
- Search for:
{TOPIC} prompts examples 2026 - Search for:
{TOPIC} techniques tips - Goal: Find prompting techniques and examples to create copy-paste prompts
If GENERAL (default):
- Search for:
{TOPIC} 2026 - Search for:
{TOPIC} discussion - Goal: Find what people are actually saying
For ALL query types:
- USE THE USER'S EXACT TERMINOLOGY - don't substitute or add tech names based on your knowledge
- If user says "ChatGPT image prompting", search for "ChatGPT image prompting"
- Do NOT add "DALL-E", "GPT-4o", or other terms you think are related
- Your knowledge may be outdated - trust the user's terminology
- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
- INCLUDE: blogs, tutorials, docs, news, GitHub repos
- DO NOT output "Sources:" list - this is noise, we'll show stats at the end
Step 3: Wait for background script to complete Use TaskOutput to get the script results before proceeding to synthesis.
Depth options (passed through from user's command):
--quick→ Faster, fewer sources (8-12 each)- (default) → Balanced (20-30 each)
--deep→ Comprehensive (50-70 Reddit, 40-60 X)
Judge Agent: Synthesize All Sources
After all searches complete, internally synthesize (don't display stats yet):
The Judge Agent must:
- Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
- Weight WebSearch sources LOWER (no engagement data)
- Identify patterns that appear across ALL three sources (strongest signals)
- Note any contradictions between sources
- Extract the top 3-5 actionable insights
Do NOT display stats here - they come at the end, right before the invitation.
FIRST: Internalize the Research
CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.
Read the research output carefully. Pay attention to:
- Exact product/tool names mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
- Specific quotes and insights from the sources - use THESE, not generic knowledge
- What the sources actually say, not what you assume the topic is about
ANTI-PATTERN TO AVOID: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
If QUERY_TYPE = RECOMMENDATIONS
CRITICAL: Extract SPECIFIC NAMES, not generic patterns.
When user asks "best X" or "top X", they want a LIST of specific things:
- Scan research for specific product names, tool names, project names, skill names, etc.
- Count how many times each is mentioned
- Note which sources recommend each (Reddit thread, X post, blog)
- List them by popularity/mention count
BAD synthesis for "best Claude Code skills":
"Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
GOOD synthesis for "best Claude Code skills":
"Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 1