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.

The script now generates AI-synthesized output automatically. Just run it and display the result.

Quick Start

Run the research script with the user's topic:

python .github/skills/last30days/scripts/last30days.py "[TOPIC]" --emit=compact

The script will output a fully synthesized response including:

• What I learned: - Narrative synthesis paragraph
• KEY PATTERNS: - Actionable patterns with bold names
• Stats tree - Source counts with engagement totals
• CONTEXT STORED - Topic summary for follow-up questions

Just display the script output directly. The synthesis is done by AI in the script.

Options:

• --quick → Faster research with fewer sources (8-12 each)
• --deep → Comprehensive research (50-70 Reddit, 40-60 X)
• --emit=json → Machine-readable output with synthesis
• --emit=md → Full markdown report

Expected Output Format

The script produces output like this:

What I learned:
The Seattle housing market shows signs of cooling with inventory levels rising. Recent data indicates a 5.2% year-over-year price adjustment to an average of ~$750K (single-family homes averaging $890K). King County inventory is up to 4,200 active listings, a 3.1% increase, with homes taking about 28 days to sell on average.

KEY PATTERNS:

- **Price adjustments ongoing** - Sellers becoming more realistic with asking prices
- **Inventory is normalizing** - Rising active listings creating buyer negotiation power  
- **Regional trends align** - Seattle mirrors broader Pacific Northwest market dynamics

✅ All agents reported back!
├─ 🟠 Reddit: 0 threads (no relevant discussions found)
├─ 🔵 X: 19 posts │ 12,797 likes │ 2,596 reposts
├─ 🌐 Web: 10+ pages │ zillow.com, redfin.com, realtor.com
└─ Top voices: @RealEstateExpert, @HousingAnalyst │ Zillow, Redfin

📚 CONTEXT STORED - I'm now an expert on this topic.

For the rest of our conversation, I have this research loaded:

TOPIC: Seattle housing market
KEY FINDINGS: 5.2% YoY change, inventory up 3.1%, median days on market rising

What would you like to know about this market?

Use Cases

• Prompting: "photorealistic people in Nano Banana Pro", "Midjourney prompts" → learn techniques, get prompts
• Recommendations: "best Claude Code skills", "top AI tools" → get a LIST of specific things
• News: "what's happening with OpenAI", "latest AI announcements" → current events
• Market Research: "Seattle housing market", "Austin rent prices" → real-time data
• General: any topic you're curious about → understand what the community is saying

After Displaying Results

Once you show the synthesized output, you are now an EXPERT on this topic.

For follow-up questions:

• DO NOT run new searches - you already have research
• Answer from what you learned - cite the sources from the output
• If they ask for a prompt - write one using the patterns you learned
• Only do new research if user asks about a DIFFERENT topic

• --quick → Faster research with fewer sources (8-12 each)
• --deep → Comprehensive research (50-70 Reddit, 40-60 X)
• --emit=json → Machine-readable output
• --emit=md → Full markdown report

How It Works

How It Works

1. Script searches Reddit + X using OpenAI and xAI APIs (if configured)
2. AI synthesizes findings into "What I learned" and "KEY PATTERNS"
3. Returns formatted output ready to display to the user

Setup (Optional but Recommended)

The skill works without API keys using WebSearch fallback. For better results with real engagement metrics, configure via one of these methods (checked in order):

Option A — Windows Credential Manager (recommended on Windows):

pip install keyring
python -c "import keyring; keyring.set_password('automation/api', 'openai_api_key', 'your-openai-key')"
python -c "import keyring; keyring.set_password('automation/api', 'xai_api_key', 'your-xai-key')"

Option B — Config file:

New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.config\last30days"
@"
# last30days API Configuration
OPENAI_API_KEY=your-openai-key
XAI_API_KEY=your-xai-key
"@ | Out-File -FilePath "$env:USERPROFILE\.config\last30days\.env" -Encoding UTF8

Option C — Environment variables (OPENAI_API_KEY, XAI_API_KEY)

RECOMMENDATIONS Query Type

When the user asks for "best X", "top X", or "what X should I use", look for specific names in the synthesis and list them:

🏆 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)

Notable mentions: [other specific things with 1-2 mentions]

BAD for "best Claude Code skills":

"Skills are powerful. Keep them under 500 lines."

GOOD for "best Claude Code skills":

"Most mentioned: /commit (5 mentions), remotion skill (4x), git-worktree (3x)"

WHEN USER WANTS A PROMPT: Write ONE Perfect Prompt

Based on what they want to create, write a single, highly-tailored prompt.

If research says to use a specific prompt FORMAT, use it:

• Research says "JSON prompts" → Write prompt AS JSON
• Research says "structured parameters" → Use key: value format
• Research says "natural language" → Use conversational prose

Output Format:

Here's your prompt for {TARGET_TOOL}:

---

[The actual prompt IN THE FORMAT RESEARCH RECOMMENDS]

---

This uses [1-line explanation of research insight applied].

Weighting Rules

• Reddit/X > Web - Engagement signals indicate real community validation
• Recent > Old - Prefer sources from last 7 days over 30 days
• High engagement > Low - 500+ upvotes matters more than 5 upvotes
• Named authors > Anonymous - @handles and usernames add credibility

Output Interpretation

The script outputs:

• What I learned - Narrative synthesis paragraph
• KEY PATTERNS - Actionable patterns with bold names
• Stats tree - Source counts with engagement totals and top voices
• CONTEXT STORED - Topic summary for follow-up questions

Related Skills

• project-guide - For understanding this project's architecture
• code-review - For reviewing code changes