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:

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>&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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Last30Days"

Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags last30days ai-agents

Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>

Control Tower Integration

Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.

Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.