alekseiul/sprut-agent-kit/last30days

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.

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}
 
Research typically takes 2-8 minutes (niche topics take longer). 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

python3 "${CLAUDE_PLUGIN_ROOT:-$HOME/.claude/skills/last30days}/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 (Bird CLI, 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 ones
• Format naturally - tables work well for wide terminals, stacked cards for narrow

If PROMPTING/NEWS/GENERAL - Show synthesis and patterns:

CITATION RULE: Cite sources sparingly to prove research is real.

• In the "What I learned" intro: cite 1-2 top sources total, not every sentence
• In KEY PATTERNS: cite 1 source per pattern, short format: "per @handle" or "per r/sub"
• Do NOT include engagement metrics in citations (likes, upvotes) - save those for stats box
• Do NOT chain multiple citations: "per @x, @y, @z" is too much. Pick the strongest one.

CITATION PRIORITY (most to least preferred):

1. @handles from X — "per @handle" (these prove the tool's unique value)
2. r/subreddits from Reddit — "per r/subreddit"
3. Web sources — ONLY when Reddit/X don't cover that specific fact

The tool's value is surfacing what PEOPLE are saying, not what journalists wrote. When both a web article and an X post cover the same fact, cite the X post.

URL FORMATTING: NEVER paste raw URLs in the output.

• BAD: "per https://www.rollingstone.com/music/music-news/kanye-west-bully-1235506094/"
• GOOD: "per Rolling Stone"
• GOOD: "per Complex"

Use the publication name, not the URL. The user doesn't need links — they need clean, readable text.

BAD: "His album is set for March 20 (per Rolling Stone; Billboard; Complex)." GOOD: "His album BULLY drops March 20 — fans on X are split on the tracklist, per @honest30bgfan_" GOOD: "Ye's apology got massive traction on r/hiphopheads" OK (web, only when Reddit/X don't have it): "The Hellwatt Festival runs July 4-18 at RCF Arena, per Billboard"

Lead with people, not publications. Start each topic with what Reddit/X users are saying/feeling, then add web context only if needed. The user came here for the conversation, not the press release.

What I learned:
 
**{Topic 1}** — [1-2 sentences about what people are saying, per @handle or r/sub]
 
**{Topic 2}** — [1-2 sentences, per @handle or r/sub]
 
**{Topic 3}** — [1-2 sentences, per @handle or r/sub]
 
KEY PATTERNS from the research:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per @handle

THEN - Stats (right before invitation):

CRITICAL: Calculate actual totals from the research output.

• Count posts/threads from each section
• Sum engagement: parse [Xlikes, Yrt] from each X post, [Xpts, Ycmt] from Reddit
• Identify top voices: highest-engagement @handles from X, most active subreddits

Copy this EXACTLY, replacing only the {placeholders}:

---
✅ All agents reported back!
├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts (via Bird/xAI)
├─ 🌐 Web: {N} pages (supplementary)
└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
---

If Reddit returned 0 threads, write: "├─ 🟠 Reddit: 0 threads (no results this cycle)" NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji.

SELF-CHECK before displaying: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If you catch yourself projecting your own knowledge instead of the research, rewrite it.

LAST - Invitation (adapt to QUERY_TYPE):

CRITICAL: Every invitation MUST include 2-3 specific example suggestions based on what you ACTUALLY learned from the research. Don't be generic — show the user you absorbed the content by referencing real things from the results.

If QUERY_TYPE = PROMPTING:

---
I'm now an expert on {TOPIC} for {TARGET_TOOL}. What do you want to make? For example:
- [specific idea based on popular technique from research]
- [specific idea based on trending style/approach from research]
- [specific idea riffing on what people are actually creating]
 
Just describe your vision and I'll write a prompt you can paste straight into {TARGET_TOOL}.

If QUERY_TYPE = RECOMMENDATIONS:

---
I'm now an expert on {TOPIC}. Want me to go deeper? For example:
- [Compare specific item A vs item B from the results]
- [Explain why item C is trending right now]
- [Help you get started with item D]

If QUERY_TYPE = NEWS:

---
I'm now an expert on {TOPIC}. Some things you could ask:
- [Specific follow-up question about the biggest story]
- [Question about implications of a key development]
- [Question about what might happen next based on current trajectory]