The old v1 skill worked because context: fork was SILENTLY IGNORED due to Claude Code bug #17283. The skill ran inline in the main conversation. Claude Code 2.1+ fixed the bug and now properly honors fork mode, creating an isolated subagent that ignores all instruction ordering (text output, bash-first, etc.). Fix: remove context: fork so the skill runs inline again, matching the behavior the user knows works. Also restored v1-style instruction flow: parse intent first, then run script, then WebSearch. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
9.8 KiB
name, description, argument-hint, allowed-tools
| name | description | argument-hint | allowed-tools |
|---|---|---|---|
| last30days | 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. | "[topic] for [tool]" or "[topic]" | 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.
CRITICAL: Parse User Intent
Before doing anything, parse the user's input for:
- TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
- TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
- 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]
Research Execution
Step 1: Run the research script
python3 ~/.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
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, internally synthesize (don't display stats yet):
The Judge Agent must:
- Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
- Weight WebSearch sources LOWER (no engagement data)
- Identify patterns that appear across ALL three sources (strongest signals)
- Note any contradictions between sources
- 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
- 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
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
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.
BAD: "His album is set for March 20 (per @cocoabutterbf; Rolling Stone; HotNewHipHop; Complex)." GOOD: "His album BULLY is set for March 20 via Gamma, per Rolling Stone."
What I learned:
**{Topic 1}** — [1-2 sentences about this storyline, per source]
**{Topic 2}** — [1-2 sentences, per source]
**{Topic 3}** — [1-2 sentences, per source]
KEY PATTERNS from the research:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per source
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 │ {domain1}, {domain2}, {domain3}
└─ 🗣️ 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.
LAST - Invitation:
---
Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
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 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.
Output Format:
Here's your prompt for {TARGET_TOOL}:
---
[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS]
---
This uses [brief 1-line explanation of what research insight you applied].
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.
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 + {n} X posts + {n} web pages
Want another prompt? Just tell me what you're creating next.