last30days

What this skill does

# 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.

## When to Use

- User wants to know what people are saying about a topic in the last 30 days
- User asks for recent community sentiment, recommendations, or trending discussions
- User wants to research a topic before making a decision (tools, products, techniques)
- User asks "what's new with X" or "best Y" or "what are people saying about Z"

## When NOT to Use

- User wants historical data older than 30 days
- User needs official documentation or API references (use docs tools instead)
- User wants to search a specific codebase (use code search tools)
- User asks a factual question that doesn't need community sentiment

## 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]`

**DISPLAY your parsing to the user.** Before running any tools, output:

```
I'll research {TOPIC} across Reddit, X, and the web to find what's been discussed in the last 30 days.

Parsed intent:
- TOPIC = {TOPIC}
- TARGET_TOOL = {TARGET_TOOL or "unknown"}
- QUERY_TYPE = {QUERY_TYPE}

Starting now.
```

If TARGET_TOOL is known, mention it in the intro: "...to find {QUERY_TYPE}-style content for use in {TARGET_TOOL}."

This text MUST appear before you call any tools. It confirms to the user that you understood their request.

---

## Research Execution

**Step 1: Run the research script**
```bash
python3 "{baseDir}/scripts/last30days.py" "$ARGUMENTS" --emit=compact 2>&1
```

The script will automatically:
- Detect available API keys
- Run Reddit/X searches if keys exist
- Signal if WebSearch is needed

---

## STEP 2: DO WEBSEARCH WHILE SCRIPT RUNS

The script auto-detects sources (API keys, etc). While waiting for it, 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
- 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

**Options** (passed through from user's command):
- `--days=N` -> Look back N days instead of 30 (e.g., `--days=7` for weekly roundup)
- `--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?
- 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

---

## THEN: Show Summary + Invite Vision

**Display in this EXACT sequence:**

**FIRST - What I learned (based on QUERY_TYPE):**

**If RECOMMENDATIONS** - Show specific things mentioned with sources:
```
Most mentioned:

[Tool Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle1, @handle2, r/sub, blog.com

[Tool Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle3, r/sub2, Complex

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

**CRITICAL for RECOMMENDATIONS:**
- Each item MUST have a "Sources:" line with actual @handles from X posts (e.g., @LONGLIVE47, @ByDobson)
- Include subreddit names (r/hiphopheads) and web sources (Complex, Variety)
- Parse @handles from research output and include the highest-engagement