
last30days
Research any topic across Reddit, X, and web from the last 30 days. Get current trends, real community sentiment, and actionable insights in 7 minutes vs 2 hours manual research.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/brianrwagner/ai-marketing-claude-code-skills --skill last30days- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads last30days automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
/last30days Research Skill
Real-time intelligence engine: Find what's working RIGHT NOW, not last quarter.
Scans Reddit, X, and web for the last 30 days, identifies patterns, extracts community insights, and delivers actionable intelligence with copy-paste-ready prompts.
Mode
Detect from context or ask: "Quick pulse, full research, or strategic intelligence brief?"
| Mode | What you get | Best for |
|---|---|---|
quick | Reddit only, top 10 insights, 10 min | Fast topic pulse, content spark |
standard | Reddit + X + web, full synthesis with themes | Content planning, market research |
deep | Full research + strategic brief + content angles + competitive intelligence | Product decisions, campaign strategy |
Default: standard — use quick if they want a fast read. Use deep if they're making a business or product decision.
Why This vs ChatGPT?
Problem with "research [topic]": ChatGPT's training data is months/years old. It gives you general knowledge, not current signals.
Problem with Perplexity: Searches web but misses Reddit threads and X conversations where real practitioners share what's actually working.
This skill provides:
- 30-day freshness filter - Only pulls recent content (not 2023 blog posts)
- Multi-platform synthesis - Combines Reddit (detailed discussions), X (real-time signals), and web (articles) in one pass
- Pattern detection - Highlights themes mentioned 3+ times across sources
- Sentiment analysis - Shows community vibe (hype, skepticism, frustration)
- Ready-to-use outputs - Copy-paste prompts and action ideas, not just summaries
You can replicate this by manually searching Reddit, X, and Brave Search with date filters, reading 30+ sources, identifying patterns, and synthesizing insights. Takes 2+ hours. This skill does it in 7 minutes.
When to Use
Perfect for:
- Trend discovery - "What's hot in AI agents right now?"
- Strategy validation - "What content marketing tactics are working in 2026?"
- Competitive intel - "What are developers saying about Cursor vs Copilot?"
- Product research - "What do users love/hate about Notion?"
- Prompt research - "What Claude prompting techniques are trending?"
- Community sentiment - "How do marketers feel about AI tools?"
Not ideal for:
- Historical research (use regular search)
- Academic/scientific papers (use Google Scholar)
- Non-English topics (limited coverage)
- Topics with zero online discussion
Required Setup
This skill orchestrates multiple tools. Verify you have:
# 1. Brave Search API (for web_search)
# Already configured in OpenClaw by default
# 2. Bird CLI (for X/Twitter search)
source ~/.openclaw/credentials/bird.env && bird search "test" -n 1
# If this fails, install bird CLI first
# 3. Reddit Insights (optional but recommended)
# If you have reddit-insights MCP server configured, skill will use it
# Otherwise falls back to Reddit web search via Brave
Quick verification:
/last30days --check-setup
Should return:
- ✅ Brave Search: Available
- ✅ Bird CLI: Available
- ✅ Reddit Insights: Available (or "Using web search fallback")
Workflow
Step 1: Web Search (Freshness Filter = Past Month)
web_search: "[topic] 2026" + freshness=pm
web_search: "[topic] strategies trends current"
web_search: "[topic] what's working"
Purpose: Get recent articles, blog posts, tools
Step 2: Reddit Search
If reddit-insights MCP configured:
reddit_search: "[topic] discussions techniques"
reddit_get_trends: "[subreddit]"
Otherwise:
web_search: "[topic] site:reddit.com" + freshness=pm
web_search: "[topic] reddit.com/r/[relevant_sub]"
Purpose: Find detailed discussions, practitioner insights, "what's actually working" threads
Step 3: X/Twitter Search
bird search "[topic]" -n 10
bird search "[topic] 2026" -n 10
bird search "[topic] best practices" -n 10
Purpose: Real-time signals, expert takes, trending threads
Step 4: Deep Dive on Top Sources (Optional)
For the 2-3 most relevant links:
web_fetch: [article URL]
Purpose: Extract specific tactics, quotes, data points
Step 5: Synthesize & Package
- Identify patterns - What appears 3+ times across sources?
- Extract key quotes - Most upvoted Reddit comments, retweeted takes
- Assess sentiment - Hype, adoption, skepticism, frustration?
- Create ready-to-use outputs - Prompts, action ideas, copy-paste tactics
Output Template
# 🔍 /last30days: [TOPIC]
*Research compiled: [DATE]*
*Sources analyzed: [NUMBER] (Reddit threads, X posts, articles)*
*Time period: Last 30 days*
---
## 🔥 Top Patterns Discovered
### 1. [Pattern Name]
**Mentioned: X times across [platforms]**
[Description of the pattern + why it matters]
**Key evidence:**
- Reddit (r/[sub]): "[Quote from highly upvoted comment]"
- X: "[Quote from popular thread]"
- Article ([Source]): "[Key insight]"
---
### 2. [Pattern Name]
[Continue same format...]
---
## 📊 Reddit Sentiment Breakdown
| Subreddit | Discussion Volume | Sentiment | Key Insight |
|-----------|-------------------|-----------|-------------|
| r/[sub] | [# threads] | 🟢 Positive / 🟡 Mixed / 🔴 Skeptical | [One-liner takeaway] |
**Top upvoted insights:**
1. "[Quote]" — u/[username] (+234 upvotes)
2. "[Quote]" — u/[username] (+189 upvotes)
---
## 🐦 X/Twitter Signal Analysis
**Trending themes:**
- [Theme 1] - [# mentions]
- [Theme 2] - [# mentions]
**Notable voices:**
- [@handle]: "[Key take]"
- [@handle]: "[Key take]"
**Engagement patterns:**
[What types of posts are getting traction?]
---
## 📈 Web Article Highlights
**Most shared articles:**
1. "[Article Title]" — [Source] — [Key insight]
2. "[Article Title]" — [Source] — [Key insight]
**Common recommendations across articles:**
- [Tactic 1]
- [Tactic 2]
- [Tactic 3]
---
## 🎯 Copy-Paste Prompt
**Based on current community best practices:**
[Ready-to-use prompt incorporating the patterns discovered]
Context: [Relevant context from research] Task: [Clear task] Style: [Tone/voice based on research] Constraints: [Any patterns to avoid based on research]
**Why this works:** [Brief explanation based on research findings]
---
## 💡 Action Ideas
**Immediate opportunities based on this research:**
1. **[Opportunity 1]**
- What: [Specific action]
- Why: [Evidence from research]
- How: [Implementation steps]
2. **[Opportunity 2]**
[Continue format...]
---
## 📌 Source List
**Reddit Threads:**
- [Thread title] - r/[sub] - [URL]
**X Threads:**
- [@handle] - [Tweet] - [URL]
**Articles:**
- [Title] - [Source] - [URL]
---
*Research complete. [X] sources analyzed in [Y] minutes.*
Real Examples
Example 1: Prompt Research
Query: /last30days Claude prompting best practices
Abbreviated Output:
# 🔍 /last30days: Claude Prompting Best Practices
## Top Patterns Discovered
### 1. XML Tags for Structure (12 mentions)
Reddit and X both emphasize using XML tags for complex prompts:
- Reddit: "XML tags changed my Claude workflow. <context> and <task> make responses 3× more accurate."
- X: "@anthropicAI's own docs now recommend XML. It's the meta."
### 2. Examples Over Instructions (9 mentions)
"Show, don't tell" — Provide 2-3 examples instead of long instructions.
### 3. Chain of Thought Explicit (7 mentions)
Add "Think step-by-step before answering" dramatically improves reasoning.
## Copy-Paste Prompt
<context>
[Your context here]
</context>
<task>
[Your task here]
</task>
<examples>
Example 1: [Show desired output style]
Example 2: [Show edge case handling]
</examples>
Think step-by-step before providing your final answer.
Example 2: Competitive Intel
Query: /last30days Notion vs Obsidian 2026
Abbreviated Output:
## Top Patterns
### 1. "Notion for Teams, Obsidian for Individuals" (18 mentions)
Strong consensus: Notion wins for collaboration, Obsidian wins for personal PKM.
### 2. Performance Complaints About Notion (11 mentions)
"Notion is slow with 1000+ pages" — recurring pain point
## Reddit Sentiment
| Subreddit | Sentiment | Key Insight |
|-----------|-----------|-------------|
| r/Notion | 🟡 Mixed | Love features, frustrated by speed |
| r/ObsidianMD | 🟢 Positive | Passionate community, local-first advocates |
## Action Ideas
**If building a PKM tool:**
1. Positioning: "Notion speed + Obsidian power" opportunity
2. Target: Teams frustrated by Notion slowness
3. Messaging: "Collaboration without the lag"
Example 3: Content Strategy
Query: /last30days LinkedIn content strategies working 2026
Abbreviated Output:
## Top Patterns
### 1. "Teach in Public" Posts Dominate (22 mentions)
Tactical, educational content outperforms thought leadership by 4-5×.
### 2. Carousels Are Fading (14 mentions)
"LinkedIn is deprioritizing carousels" — multiple reports of engagement drops.
### 3. Comment Engagement = Reach (16 mentions)
"Spend 30 min/day commenting on others' posts. Doubled my reach."
## Action Ideas
1. **Shift to educational threads**
- Format: Problem → Solution (step-by-step) → Result
- Evidence: Posts using this format getting 3-5× more impressions
2. **Abandon carousel strategy**
- Data: Engagement down 40-60% since December
3. **Allocate 30 min/day to comments**
- Tactic: Comment on posts from your ICP 10 min after posting (algorithm boost)
Real Case Study
User: B2B SaaS marketer researching content trends quarterly
Before using skill:
- Manual research: 2-3 hours per topic
- Visited 20-30 sites, took scattered notes
- Hard to identify patterns across sources
- No systematic approach
After implementing /last30days:
- Research time: 7-10 minutes per topic
- Consistent output format (easy to reference later)
- Pattern detection automatic
- Copy-paste prompts immediately usable
Impact after 3 months:
- 10 trend reports created (vs 2-3 before)
- Content strategy pivots based on current signals, not guesses
- Team shares research reports across org (became go-to intelligence source)
- Time saved: ~20 hours/month
Quote: "I used to spend half a day researching trends, now it's 7 minutes. The pattern detection alone is worth it—I'd miss things reading manually."
Configuration Options
Standard Mode (default)
/last30days [topic]
- Searches web, Reddit, X
- Synthesizes top patterns
- Generates prompts + action ideas
Deep Dive Mode
/last30days [topic] --deep
- Fetches and analyzes top 5 articles in full
- More detailed quotes and data points
- Takes 12-15 minutes instead of 7
Reddit-Only Mode
/last30days [topic] --reddit-only
- Focuses exclusively on Reddit discussions
- Best for: Community sentiment, practitioner insights
Quick Brief Mode
/last30days [topic] --quick
- Top 3 patterns only
- No detailed synthesis
- 3-minute output
Pro Tips
- Use specific topics - "AI writing tools" better than "AI"
- Add context - "for B2B SaaS" or "for developers" narrows results
- Run monthly - Track trends over time, spot shifts early
- Combine with /reddit-insights - For deeper Reddit analysis
- Export to Notion - Keep a trends database
- Share with team - Intelligence is more valuable when distributed
Common Use Cases
| Goal | Query Example | Output Value |
|---|---|---|
| Content ideas | /last30days AI productivity tools | Topics getting engagement now |
| Competitive research | /last30days Superhuman vs Spark email | User sentiment, pain points |
| Positioning | /last30days project management frustrations | Language customers use |
| Product validation | /last30days AI coding assistant pain points | Real problems to solve |
| Marketing tactics | /last30days cold email strategies 2026 | What's working in market |
Quality Indicators
A good /last30days report has:
- 3-5 clear patterns (not just random insights)
- Quotes from actual users (not just article summaries)
- Sentiment assessment (what's the vibe?)
- Ready-to-use prompt (copy-paste quality)
- Specific action ideas (not vague suggestions)
- Source links for credibility
- Recency verified (nothing from >30 days)
Limitations
This skill does NOT:
- Access paywalled content (uses public sources only)
- Provide academic-quality research (for speed, not depth)
- Replace domain expertise (synthesizes existing knowledge)
- Guarantee completeness (samples popular discussions)
Best for: Fast, directional intelligence. Not dissertation-level research.
Installation
# Copy skill to your skills directory
cp -r last30days $HOME/.openclaw/skills/
# Verify dependencies
/last30days --check-setup
# First run
/last30days "your topic here"
Support
Issues or missing sources? Provide:
- Topic searched
- Expected vs actual sources found
- Any error messages
- Your setup verification output
Built to replace 2-hour research sessions with 7-minute intelligence reports.
Know what's working RIGHT NOW. Not last quarter. Not last year. Today.
FAQ
What does the last30days skill do?
Research any topic across Reddit, X, and web from the last 30 days. Get current trends, real community sentiment, and actionable insights in 7 minutes vs 2 hours manual research.
How do I install last30days?
Run npx skills add https://github.com/brianrwagner/ai-marketing-claude-code-skills --skill last30days in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/last30days/ (personal) or .claude/skills/last30days/ (project).
Which AI agents can use last30days?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is last30days safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains last30days?
It is published by brianrwagner in brianrwagner/ai-marketing-claude-code-skills. It has 476 recorded installs.
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