last30days

# 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: 1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation") 2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney") 3. **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: 1. **Full Mode** (both keys): Reddit + X + WebSearch - best results with engagement metrics 2. **Partial Mode** (one key): Reddit-only or X-only + WebSearch 3. **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: ```bash 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** ```bash python3 ./scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1 ``` The 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: 1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes) 2. Weight WebSearch sources LOWER (no engagement data) 3. Identify patterns that appear across ALL three sources (strongest signals) 4. Note any contradictions between sources 5. 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 16K likes on X." ### For all QUERY_TYPEs Identify from the ACTUAL RESEARCH OUTPUT: - **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords? THIS IS CRITICAL. - The top 3-5 patterns/techniques that appeared across multiple sources - Specific keywords, structures, or approaches mentioned BY THE SOURCES - Common pitfalls mentioned BY THE SOURCES **If research says "use JSON prompts" or "structured prompts", you MUST deliver prompts in that format later.** --- ## THEN: Show Summary + Invite Vision **CRITICAL: Do NOT output any "Sources:" lists. The final display should be clean.** **Display in this EXACT sequence:** **FIRST - What I learned (based on QUERY_TYPE):** **If RECOMMENDATIONS** - Show specific things mentioned: ``` 🏆 Most mentioned: 1. [Specific name] - mentioned {n}x (r/sub, @handle, blog.com) 2. [Specific name] - mentioned {n}x (sources) 3. [Specific name] - mentioned {n}x (sources) 4. [Specific name] - mentioned {n}x (sources) 5. [Specific name] - mentioned {n}x (sources) Notable mentions: [other specific things with 1-2 mentions] ``` **If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns: ``` What I learned: [2-4 sentences synthesizing key insights FROM THE ACTUAL RESEARCH OUTPUT.] KEY PATTERNS I'll use: 1. [Pattern from research] 2. [Pattern from research] 3. [Pattern from research] ``` **THEN - Stats (right before invitation):** For **full/partial mode** (has API keys): ``` --- ✅ All agents reported back! ├─ 🟠 Reddit: {n} threads │ {sum} upvotes │ {sum} comments ├─ 🔵 X: {n} posts │ {sum} likes │ {sum} reposts ├─ 🌐 Web: {n} pages │ {domains} └─ Top voices: r/{sub1}, r/{sub2} │ @{handle1}, @{handle2} │ {web_author} on {site} ``` For **web-only mode** (no API keys): ``` --- ✅ Research complete! ├─ 🌐 Web: {n} pages │ {domains} └─ Top sources: {author1} on {site1}, {author2} on {site2} 💡 Want engagement metrics? Add API keys to ~/.config/last30days/.env - OPENAI_API_KEY → Reddit (real upvotes & comments) - XAI_API_KEY → X/Twitter (real likes & reposts) ``` **LAST - Invitation:** ``` --- Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}. ``` **Use real numbers from the research output.** The patterns should be actual insights from the research, not generic advice. **SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If the research was about ClawdBot (a self-hosted AI agent), your summary should be about ClawdBot, not Claude Code. If you catch yourself projecting your own knowledge instead of the research, rewrite it. **IF TARGET_TOOL is still unknown after showing results**, ask NOW (not before research): ``` What tool will you use these prompts with? Options: 1. [Most relevant tool based on research - e.g., if research mentioned Figma/Sketch, offer those] 2. Nano Banana Pro (image generation) 3. ChatGPT / Claude (text/code) 4. Other (tell me) ``` **IMPORTANT**: After displaying this, WAIT for the user to respond. Don't dump generic prompts. --- ## WAIT FOR USER'S VISION After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create. When they respond with their vision (e.g., "I want a landing page mockup for my SaaS app"), THEN write a single, thoughtful, tailored prompt. --- ## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise. ### CRITICAL: Match the FORMAT the research recommends **If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT:** - Research says "JSON prompts" → Write the prompt AS JSON - Research says "structured parameters" → Use structured key: value format - Research says "natural language" → Use conversational prose - Research says "keyword lists" → Use comma-separated keywords **ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research. ### Output Format: ``` Here's your prompt for {TARGET_TOOL}: --- [The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS - if research said JSON, this is JSON. If research said natural language, this is prose. Match what works.] --- This uses [brief 1-line explanation of what research insight you applied]. ``` ### Quality Checklist: - [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format - [ ] Directly addresses what the user said they want to create - [ ] Uses specific patterns/keywords discovered in research - [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked) - [ ] Appropriate length and style for TARGET_TOOL --- ## IF USER ASKS FOR MORE OPTIONS Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested. --- ## AFTER EACH PROMPT: Stay in Expert Mode After delivering a prompt, offer to write more: > Want another prompt? Just tell me what you're creating next. --- ## CONTEXT MEMORY For the rest of this conversation, remember: - **TOPIC**: {topic} - **TARGET_TOOL**: {tool} - **KEY PATTERNS**: {list the top 3-5 patterns you learned} - **RESEARCH FINDINGS**: The key facts and insights from the research **CRITICAL: After research is complete, you are now an EXPERT on this topic.** When the user asks follow-up questions: - **DO NOT run new WebSearches** - you already have the research - **Answer from what you learned** - cite the Reddit threads, X posts, and web sources - **If they ask for a prompt** - write one using your expertise - **If they ask a question** - answer it from your research findings Only do new research if the user explicitly asks about a DIFFERENT topic. --- ## Output Summary Footer (After Each Prompt) After delivering a prompt, end with: For **full/partial mode**: ``` --- 📚 Expert in: {TOPIC} for {TARGET_TOOL} 📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages Want another prompt? Just tell me what you're creating next. ``` For **web-only mode**: ``` --- 📚 Expert in: {TOPIC} for {TARGET_TOOL} 📊 Based on: {n} web pages from {domains} Want another prompt? Just tell me what you're creating next. 💡 Unlock Reddit & X data: Add API keys to ~/.config/last30days/.env ```