last30days-skill/SKILL.md

---
name: last30days
description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
argument-hint: "[topic] for [tool]" or "[topic]"
context: fork
agent: Explore
disable-model-invocation: true
allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
---

# 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:
- **Recommendations**: "best Claude Code skills" → get a LIST of specific skills people mention
- **News**: "what's happening with OpenAI" → get current events and updates
- **How-to**: "Midjourney prompts" → learn techniques, then get copy-paste prompts
- **General**: any topic → 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:
   - **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
   - **HOW-TO** - "how to X", "X tutorial", "learn X" → User wants educational content
   - **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

Verify API key configuration exists:

```bash
if [ ! -f ~/.config/last30days/.env ]; then
  echo "SETUP_NEEDED"
else
  echo "CONFIGURED"
fi
```

### If SETUP_NEEDED

Run NUX flow to configure API keys. Use AskUserQuestion to collect:

1. **OpenAI API Key** (optional but recommended for Reddit research)
2. **xAI API Key** (optional but recommended for X research)

Then create the config:

```bash
mkdir -p ~/.config/last30days
cat > ~/.config/last30days/.env << 'ENVEOF'
# last30days API Configuration
# At least one key is required

OPENAI_API_KEY=
XAI_API_KEY=
ENVEOF

chmod 600 ~/.config/last30days/.env
echo "Config created at ~/.config/last30days/.env"
echo "Please edit it to add your API keys, then run the skill again."
```

**STOP HERE if setup was needed.**

---

## Research Execution

**IMPORTANT: Run Reddit/X script IN BACKGROUND first, then WebSearch.** This way both run in parallel.

**Step 1: Display the research banner immediately:**
```
🔍 Researching "{TOPIC}" across the last 30 days...

🚀 Deploying research agents...
├─ 🟠 Reddit Agent: Scanning subreddits for discussions...
├─ 🔵 X Agent: Following the conversation on X...
└─ 🌐 Web Agent: Searching blogs, docs, and news...
```

**Step 2: Start Reddit/X script in background**
```bash
python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
```
Use `run_in_background: true` so it starts immediately and runs while we do WebSearch.

**Step 2: While script runs, do WebSearch**

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 HOW-TO** ("how to X", "tutorial"):
- Search for: `{TOPIC} tutorial guide 2026`
- Search for: `{TOPIC} best practices`
- Goal: Find educational content

**If GENERAL** (default):
- Search for: `{TOPIC} 2026`
- Search for: `{TOPIC} discussion`
- Goal: Find what people are actually saying

For ALL query types:
- 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, display:**
```
✅ Research complete
├─ Reddit: Found {n} threads
├─ X: Found {n} posts
└─ Web: Found {n} pages

⚖️ Synthesizing insights...
```

**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

---

## 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:**

```
📊 Research Complete

Analyzed {total_sources} sources from the last 30 days
├─ 🟠 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}

---
What I learned:

[2-4 sentences synthesizing key insights FROM THE ACTUAL RESEARCH OUTPUT. Quote or paraphrase what the sources said. The synthesis should be traceable back to the research results above.]
```

**Then, 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 NEWS/HOW-TO/GENERAL** - Show patterns:
```
KEY PATTERNS I'll use:
1. [Pattern from research]
2. [Pattern from research]
3. [Pattern from research]
```

**Then always end with:**
```
---
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:

```
---
📚 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.
```