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Research a topic from the last 30 days. Also triggered by 'last30'. Sources: Reddit, X, YouTube, web. Become an expert and write copy-paste-ready prompts.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/johnalbertini14-glitch/openclaw-skills --skill last30daysコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SOC 職業分類に基づく
SKILL.md を表示中
| name | last30days |
| version | 2.1 |
| description | Research a topic from the last 30 days. Also triggered by 'last30'. Sources: Reddit, X, YouTube, web. Become an expert and write copy-paste-ready prompts. |
| argument-hint | last30 AI video tools, last30 best project management tools |
| allowed-tools | Bash, Read, Write, AskUserQuestion, WebSearch |
| homepage | https://github.com/mvanhorn/last30days-skill |
| user-invocable | true |
| disable-model-invocation | true |
| metadata | {"clawdbot":{"emoji":"📰","requires":{"env":["OPENAI_API_KEY"],"bins":["node","python3"]},"primaryEnv":"OPENAI_API_KEY","files":["scripts/*"],"homepage":"https://github.com/mvanhorn/last30days-skill","tags":["research","reddit","x","youtube","trends","prompts"]}} |
Research ANY topic across Reddit, X, YouTube, and the web. Surface what people are actually discussing, recommending, and debating right now.
Before doing anything, parse the user's input for:
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[topic] → "iOS design mockups" → TOOL NOT SPECIFIED, that's OKIMPORTANT: Do NOT ask about target tool before research.
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.
Step 1: Run the research script (FOREGROUND — do NOT background this)
CRITICAL: Run this command in the FOREGROUND with a 5-minute timeout. Do NOT use run_in_background. The full output contains Reddit, X, AND YouTube data that you need to read completely.
# Find skill root — works in repo checkout, Claude Code, or Codex install
for dir in \
"." \
"${CLAUDE_PLUGIN_ROOT:-}" \
"$HOME/.claude/skills/last30days" \
"$HOME/.agents/skills/last30days" \
"$HOME/.codex/skills/last30days"; do
[ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
done
if [ -z "${SKILL_ROOT:-}" ]; then
echo "ERROR: Could not find scripts/last30days.py" >&2
exit 1
fi
python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact
Use a timeout of 300000 (5 minutes) on the Bash call. The script typically takes 1-3 minutes.
The script will automatically:
Read the ENTIRE output. It contains THREE data sections in this order: Reddit items, X items, and YouTube items. If you miss the YouTube section, you will produce incomplete stats.
YouTube items in the output look like: **{video_id}** (score:N) {channel_name} [N views, N likes] followed by a title, URL, and optional transcript snippet. Count them and include them in your synthesis and stats block.
After the script finishes, do WebSearch to supplement with blogs, tutorials, and news.
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"):
best {TOPIC} recommendations{TOPIC} list examplesmost popular {TOPIC}If NEWS ("what's happening with X", "X news"):
{TOPIC} news 2026{TOPIC} announcement updateIf PROMPTING ("X prompts", "prompting for X"):
{TOPIC} prompts examples 2026{TOPIC} techniques tipsIf GENERAL (default):
{TOPIC} 2026{TOPIC} discussionFor ALL query types:
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)--deep → Comprehensive (50-70 Reddit, 40-60 X)After all searches complete, internally synthesize (don't display stats yet):
The Judge Agent must:
Do NOT display stats here - they come at the end, right before the invitation.
CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.
Read the research output carefully. Pay attention to:
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.
CRITICAL: Extract SPECIFIC NAMES, not generic patterns.
When user asks "best X" or "top X", they want a LIST of specific things:
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."
Identify from the ACTUAL RESEARCH OUTPUT:
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:
If PROMPTING/NEWS/GENERAL - Show synthesis and patterns:
CITATION RULE: Cite sources sparingly to prove research is real.
CITATION PRIORITY (most to least preferred):
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: "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.
[Xlikes, Yrt] from each X post, [Xpts, Ycmt] from RedditCopy this EXACTLY, replacing only the {placeholders}:
---
✅ All agents reported back!
├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts
├─ 🔴 YouTube: {N} videos │ {N} views │ {N} with transcripts
├─ 🌐 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)" If YouTube returned 0 videos or yt-dlp is not installed, omit the YouTube line entirely. 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]
If QUERY_TYPE = GENERAL:
---
I'm now an expert on {TOPIC}. Some things I can help with:
- [Specific question based on the most discussed aspect]
- [Specific creative/practical application of what you learned]
- [Deeper dive into a pattern or debate from the research]
Example invitations (to show the quality bar):
For /last30days nano banana pro prompts for Gemini:
I'm now an expert on Nano Banana Pro for Gemini. What do you want to make? For example:
- Photorealistic product shots with natural lighting (the most requested style right now)
- Logo designs with embedded text (Gemini's new strength per the research)
- Multi-reference style transfer from a mood board
Just describe your vision and I'll write a prompt you can paste straight into Gemini.
For /last30days kanye west (GENERAL):
I'm now an expert on Kanye West. Some things I can help with:
- What's the real story behind the apology letter — genuine or PR move?
- Break down the BULLY tracklist reactions and what fans are expecting
- Compare how Reddit vs X are reacting to the Bianca narrative
For /last30days war in Iran (NEWS):
I'm now an expert on the Iran situation. Some things you could ask:
- What are the realistic escalation scenarios from here?
- How is this playing differently in US vs international media?
- What's the economic impact on oil markets so far?
After showing the stats summary with your invitation, STOP and wait for the user to respond.
Read their response and match the intent:
Only write a prompt when the user wants one. Don't force a prompt on someone who asked "what could happen next with Iran."
When the user wants a prompt, write a single, highly-tailored prompt using your research expertise.
If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT.
ANTI-PATTERN: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
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].
Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
After delivering a prompt, offer to write more:
Want another prompt? Just tell me what you're creating next.
For the rest of this conversation, remember:
CRITICAL: After research is complete, you are now an EXPERT on this topic.
When the user asks follow-up questions:
Only do new research if the user explicitly asks about a DIFFERENT topic.
After delivering a prompt, end with:
---
📚 Expert in: {TOPIC} for {TARGET_TOOL}
📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} YouTube videos ({sum} views) + {n} web pages
Want another prompt? Just tell me what you're creating next.
What this skill does:
api.openai.com) for Reddit discoveryapi.x.ai) for X searchyt-dlp locally for YouTube search and transcript extraction (no API key, public data)reddit.com for engagement metricsWhat this skill does NOT do:
disable-model-invocation: true)Bundled scripts: scripts/last30days.py (main research engine), scripts/lib/ (search, enrichment, rendering modules), scripts/lib/vendor/bird-search/ (vendored X search client, MIT licensed)
Review scripts before first use to verify behavior.