last30days

---
name: last30days
description: Research any topic from the last 30 days on Reddit + X + Web, synthesize findings, and write copy-paste-ready prompts. Use when the user wants recent social/web research on a topic, asks "what are people saying about X", or wants to learn current best practices. Requires OPENAI_API_KEY and/or XAI_API_KEY for full Reddit+X access, falls back to web search.
---

# 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):
```
---
✅ R

... (truncated)