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Chinese

/last30days Research Skill

/last30days 研究技能

Real-time intelligence engine: Find what's working RIGHT NOW, not last quarter.
Scans Reddit, X, and web for the last 30 days, identifies patterns, extracts community insights, and delivers actionable intelligence with copy-paste-ready prompts.
实时智能引擎： 聚焦当下正在生效的内容，而非上个季度的信息。
扫描Reddit、X及全网过去30天的内容，识别模式、提取社区洞察，并提供可直接复制粘贴的提示词及可执行情报。

Why This vs ChatGPT?

为何选择它而非ChatGPT？

Problem with "research [topic]": ChatGPT's training data is months/years old. It gives you general knowledge, not current signals.
Problem with Perplexity: Searches web but misses Reddit threads and X conversations where real practitioners share what's actually working.
This skill provides:
  1. 30-day freshness filter - Only pulls recent content (not 2023 blog posts)
  2. Multi-platform synthesis - Combines Reddit (detailed discussions), X (real-time signals), and web (articles) in one pass
  3. Pattern detection - Highlights themes mentioned 3+ times across sources
  4. Sentiment analysis - Shows community vibe (hype, skepticism, frustration)
  5. Ready-to-use outputs - Copy-paste prompts and action ideas, not just summaries
You can replicate this by manually searching Reddit, X, and Brave Search with date filters, reading 30+ sources, identifying patterns, and synthesizing insights. Takes 2+ hours. This skill does it in 7 minutes.
「研究[话题]」的痛点： ChatGPT的训练数据滞后数月甚至数年，只能提供通用知识，无法给出当下的信号。
Perplexity的不足： 仅能搜索网页内容，却遗漏了Reddit帖子和X对话——这些地方才是从业者分享真实有效经验的阵地。
本技能可提供：
  1. 30天新鲜度过滤 - 仅提取近期内容（排除2023年及更早的博客文章）
  2. 多平台整合 - 一次性整合Reddit（深度讨论）、X（实时信号）和网页（文章）的信息
  3. 模式识别 - 高亮在多来源中被提及3次以上的主题
  4. 情绪分析 - 呈现社区氛围（hype、质疑、不满等）
  5. 即用型输出 - 提供可直接复制的提示词和行动建议，而非仅为摘要
手动复刻的话，你需要在Reddit、X和Brave Search中手动设置日期过滤，阅读30+来源内容，识别模式并整合洞察，耗时2小时以上。而本技能仅需7分钟即可完成。

When to Use

使用场景

Perfect for:
  • Trend discovery - "What's hot in AI agents right now?"
  • Strategy validation - "What content marketing tactics are working in 2026?"
  • Competitive intel - "What are developers saying about Cursor vs Copilot?"
  • Product research - "What do users love/hate about Notion?"
  • Prompt research - "What Claude prompting techniques are trending?"
  • Community sentiment - "How do marketers feel about AI tools?"
Not ideal for:
  • Historical research (use regular search)
  • Academic/scientific papers (use Google Scholar)
  • Non-English topics (limited coverage)
  • Topics with zero online discussion
适用场景：
  • 趋势发现 - 「当前AI Agent领域有哪些热点？」
  • 策略验证 - 「2026年哪些内容营销战术有效？」
  • 竞品情报 - 「开发者对Cursor和Copilot的评价如何？」
  • 产品研究 - 「用户对Notion的喜爱与槽点是什么？」
  • 提示词研究 - 「哪些Claude提示词技术正在流行？」
  • 社区情绪 - 「营销人员对AI工具的看法如何？」
不适用场景：
  • 历史研究（使用常规搜索工具）
  • 学术/科研论文（使用Google Scholar）
  • 非英语话题（覆盖范围有限）
  • 无线上讨论的话题

Required Setup

必要配置

This skill orchestrates multiple tools. Verify you have:
bash
undefined
本技能需协调多个工具，请确认你已具备：
bash
undefined

1. Brave Search API (for web_search)

1. Brave Search API（用于web_search）

Already configured in OpenClaw by default

OpenClaw中已默认配置

2. Bird CLI (for X/Twitter search)

2. Bird CLI（用于X/Twitter搜索）

source ~/.openclaw/credentials/bird.env && bird search "test" -n 1
source ~/.openclaw/credentials/bird.env && bird search "test" -n 1

If this fails, install bird CLI first

如果执行失败，请先安装bird CLI

3. Reddit Insights (optional but recommended)

3. Reddit Insights（可选但推荐）

If you have reddit-insights MCP server configured, skill will use it

若已配置reddit-insights MCP服务器，技能会自动使用

Otherwise falls back to Reddit web search via Brave

否则将通过Brave的网页搜索替代Reddit专属搜索


**Quick verification:**
```bash
/last30days --check-setup
Should return:
  • ✅ Brave Search: Available
  • ✅ Bird CLI: Available
  • ✅ Reddit Insights: Available (or "Using web search fallback")

**快速验证：**
```bash
/last30days --check-setup
应返回：
  • ✅ Brave Search: 可用
  • ✅ Bird CLI: 可用
  • ✅ Reddit Insights: 可用（或"使用网页搜索替代"）

Workflow

工作流程

Step 1: Web Search (Freshness Filter = Past Month)

步骤1：网页搜索（新鲜度过滤=过去30天）

web_search: "[topic] 2026" + freshness=pm
web_search: "[topic] strategies trends current"
web_search: "[topic] what's working"
Purpose: Get recent articles, blog posts, tools
web_search: "[话题] 2026" + freshness=pm
web_search: "[话题] strategies trends current"
web_search: "[话题] what's working"
目的： 获取近期文章、博客、工具信息

Step 2: Reddit Search

步骤2：Reddit搜索

If reddit-insights MCP configured:
reddit_search: "[topic] discussions techniques"
reddit_get_trends: "[subreddit]"
Otherwise:
web_search: "[topic] site:reddit.com" + freshness=pm
web_search: "[topic] reddit.com/r/[relevant_sub]"
Purpose: Find detailed discussions, practitioner insights, "what's actually working" threads
若已配置reddit-insights MCP：
reddit_search: "[话题] discussions techniques"
reddit_get_trends: "[subreddit]"
否则：
web_search: "[话题] site:reddit.com" + freshness=pm
web_search: "[话题] reddit.com/r/[相关子版块]"
目的： 找到深度讨论、从业者洞察、「真实有效经验」相关帖子

Step 3: X/Twitter Search

步骤3：X/Twitter搜索

bird search "[topic]" -n 10
bird search "[topic] 2026" -n 10
bird search "[topic] best practices" -n 10
Purpose: Real-time signals, expert takes, trending threads
bird search "[话题]" -n 10
bird search "[话题] 2026" -n 10
bird search "[话题] best practices" -n 10
目的： 获取实时信号、专家观点、热门帖子

Step 4: Deep Dive on Top Sources (Optional)

步骤4：深度分析核心来源（可选）

For the 2-3 most relevant links:
web_fetch: [article URL]
Purpose: Extract specific tactics, quotes, data points
针对2-3个最相关链接：
web_fetch: [文章URL]
目的： 提取具体战术、引用、数据点

Step 5: Synthesize & Package

步骤5：整合与打包

  1. Identify patterns - What appears 3+ times across sources?
  2. Extract key quotes - Most upvoted Reddit comments, retweeted takes
  3. Assess sentiment - Hype, adoption, skepticism, frustration?
  4. Create ready-to-use outputs - Prompts, action ideas, copy-paste tactics
  1. 识别模式 - 哪些内容在多来源中被提及3次以上？
  2. 提取关键引用 - 获赞最多的Reddit评论、被转发的观点
  3. 评估情绪 - 是hype、接受、质疑还是不满？
  4. 生成即用型输出 - 提示词、行动建议、可直接复制的战术

Output Template

输出模板

markdown
undefined
markdown
undefined

🔍 /last30days: [TOPIC]

🔍 /last30days: [话题]

Research compiled: [DATE]
Sources analyzed: [NUMBER] (Reddit threads, X posts, articles)
Time period: Last 30 days
研究完成时间: [日期]
分析来源数量: [数字]（Reddit帖子、X动态、文章）
时间范围: 过去30天

🔥 Top Patterns Discovered

🔥 发现的核心模式

1. [Pattern Name]

1. [模式名称]

Mentioned: X times across [platforms]
[Description of the pattern + why it matters]
Key evidence:
  • Reddit (r/[sub]): "[Quote from highly upvoted comment]"
  • X: "[Quote from popular thread]"
  • Article ([Source]): "[Key insight]"
提及次数: [X]次，覆盖平台[平台列表]
[模式描述+重要性说明]
关键证据：
  • Reddit (r/[子版块]): "[高赞评论引用]"
  • X: "[热门帖子引用]"
  • 文章([来源]): "[核心洞察]"

2. [Pattern Name]

2. [模式名称]

[Continue same format...]
[按相同格式继续...]

📊 Reddit Sentiment Breakdown

📊 Reddit情绪分析

SubredditDiscussion VolumeSentimentKey Insight
r/[sub][# threads]🟢 Positive / 🟡 Mixed / 🔴 Skeptical[One-liner takeaway]
Top upvoted insights:
  1. "[Quote]" — u/[username] (+234 upvotes)
  2. "[Quote]" — u/[username] (+189 upvotes)
子版块讨论量情绪核心洞察
r/[sub][# 帖子数]🟢 正面 / 🟡 中立 / 🔴 质疑[一句话结论]
高赞洞察：
  1. "[引用]" — u/[用户名] (+234 赞)
  2. "[引用]" — u/[用户名] (+189 赞)

🐦 X/Twitter Signal Analysis

🐦 X/Twitter信号分析

Trending themes:
  • [Theme 1] - [# mentions]
  • [Theme 2] - [# mentions]
Notable voices:
  • [@handle]: "[Key take]"
  • [@handle]: "[Key take]"
Engagement patterns: [What types of posts are getting traction?]
热门主题：
  • [主题1] - [# 提及次数]
  • [主题2] - [# 提及次数]
知名观点：
互动模式： [哪些类型的内容获得高曝光？]

📈 Web Article Highlights

📈 网页文章重点

Most shared articles:
  1. "[Article Title]" — [Source] — [Key insight]
  2. "[Article Title]" — [Source] — [Key insight]
Common recommendations across articles:
  • [Tactic 1]
  • [Tactic 2]
  • [Tactic 3]
分享量最高的文章：
  1. "[文章标题]" — [来源] — [核心洞察]
  2. "[文章标题]" — [来源] — [核心洞察]
文章中的通用建议：
  • [战术1]
  • [战术2]
  • [战术3]

🎯 Copy-Paste Prompt

🎯 可直接复制的提示词

Based on current community best practices:
[Ready-to-use prompt incorporating the patterns discovered]

Context: [Relevant context from research]
Task: [Clear task]
Style: [Tone/voice based on research]
Constraints: [Any patterns to avoid based on research]
Why this works: [Brief explanation based on research findings]
基于当前社区最佳实践：
[整合发现模式后的即用型提示词]

Context: [研究相关背景]
Task: [明确任务]
Style: [基于研究的语气]
Constraints: [基于研究需避免的模式]
有效性说明： [基于研究发现的简短解释]

💡 Action Ideas

💡 行动建议

Immediate opportunities based on this research:
  1. [Opportunity 1]
    • What: [Specific action]
    • Why: [Evidence from research]
    • How: [Implementation steps]
  2. [Opportunity 2] [Continue format...]
基于本次研究的即时机会：
  1. [机会1]
    • 内容: [具体行动]
    • 原因: [研究中的证据]
    • 实施步骤: [操作方法]
  2. [机会2] [按相同格式继续...]

📌 Source List

📌 来源列表

Reddit Threads:
  • [Thread title] - r/[sub] - [URL]
X Threads:
  • [@handle] - [Tweet] - [URL]
Articles:
  • [Title] - [Source] - [URL]
Research complete. [X] sources analyzed in [Y] minutes.
undefined
Reddit帖子：
  • [帖子标题] - r/[子版块] - [链接]
X动态：
  • [@账号] - [推文内容] - [链接]
文章：
  • [标题] - [来源] - [链接]
研究完成。共分析[X]个来源，耗时[Y]分钟。
undefined

Real Examples

真实案例

Example 1: Prompt Research

案例1：提示词研究

Query: 
/last30days Claude prompting best practices
Abbreviated Output:
markdown
undefined
查询： 
/last30days Claude prompting best practices
简化输出：
markdown
undefined

🔍 /last30days: Claude Prompting Best Practices

🔍 /last30days: Claude提示词最佳实践

Top Patterns Discovered

发现的核心模式

1. XML Tags for Structure (12 mentions)

1. XML标签结构化（12次提及）

Reddit and X both emphasize using XML tags for complex prompts:
  • Reddit: "XML tags changed my Claude workflow. <context> and <task> make responses 3× more accurate."
  • X: "@anthropicAI's own docs now recommend XML. It's the meta."
Reddit和X均强调在复杂提示词中使用XML标签：
  • Reddit: "XML标签彻底改变了我的Claude工作流。<context>和<task>标签让响应准确率提升3倍。"
  • X: "@anthropicAI官方文档现已推荐XML格式，这是当前最优方案。"

2. Examples Over Instructions (9 mentions)

2. 示例优先于指令（9次提及）

"Show, don't tell" — Provide 2-3 examples instead of long instructions.
"展示而非说教"——提供2-3个示例替代长篇指令。

3. Chain of Thought Explicit (7 mentions)

3. 明确思维链（7次提及）

Add "Think step-by-step before answering" dramatically improves reasoning.
添加"逐步思考后再作答"可显著提升推理能力。

Copy-Paste Prompt

可直接复制的提示词

<context> [Your context here] </context> <task> [Your task here] </task> <examples> Example 1: [Show desired output style] Example 2: [Show edge case handling] </examples>
Think step-by-step before providing your final answer.

---
<context> [你的背景信息] </context> <task> [你的任务] </task> <examples> 示例1: [展示期望输出风格] 示例2: [展示边缘情况处理] </examples>
逐步思考后再提供最终答案。

---

Example 2: Competitive Intel

案例2：竞品情报

Query: 
/last30days Notion vs Obsidian 2026
Abbreviated Output:
markdown
undefined
查询： 
/last30days Notion vs Obsidian 2026
简化输出：
markdown
undefined

Top Patterns

核心模式

1. "Notion for Teams, Obsidian for Individuals" (18 mentions)

1. "团队用Notion，个人用Obsidian"（18次提及）

Strong consensus: Notion wins for collaboration, Obsidian wins for personal PKM.
共识明确：Notion在协作场景胜出，Obsidian在个人知识管理（PKM）场景更优。

2. Performance Complaints About Notion (11 mentions)

2. Notion性能投诉（11次提及）

"Notion is slow with 1000+ pages" — recurring pain point
"当页面超过1000个时，Notion会变得卡顿"——这是反复出现的痛点

Reddit Sentiment

Reddit情绪分析

SubredditSentimentKey Insight
r/Notion🟡 MixedLove features, frustrated by speed
r/ObsidianMD🟢 PositivePassionate community, local-first advocates
子版块情绪核心洞察
r/Notion🟡 中立喜爱功能，但对速度不满
r/ObsidianMD🟢 正面社区热情高，支持本地优先

Action Ideas

行动建议

If building a PKM tool:
  1. Positioning: "Notion speed + Obsidian power" opportunity
  2. Target: Teams frustrated by Notion slowness
  3. Messaging: "Collaboration without the lag"

---
若开发PKM工具：
  1. 定位："Notion速度 + Obsidian能力"的机会点
  2. 目标用户：因Notion卡顿而不满的团队
  3. 宣传语："无卡顿的协作体验"

---

Example 3: Content Strategy

案例3：内容策略

Query: 
/last30days LinkedIn content strategies working 2026
Abbreviated Output:
markdown
undefined
查询： 
/last30days LinkedIn content strategies working 2026
简化输出：
markdown
undefined

Top Patterns

核心模式

1. "Teach in Public" Posts Dominate (22 mentions)

1. "公开教学"类帖子主导（22次提及）

Tactical, educational content outperforms thought leadership by 4-5×.
战术性、教育类内容的表现比思想领导力内容高4-5倍。

2. Carousels Are Fading (14 mentions)

2. 轮播图热度下降（14次提及）

"LinkedIn is deprioritizing carousels" — multiple reports of engagement drops.
"LinkedIn正在降低轮播图权重"——多个报告显示互动量下滑。

3. Comment Engagement = Reach (16 mentions)

3. 评论互动=曝光量（16次提及）

"Spend 30 min/day commenting on others' posts. Doubled my reach."
"每天花30分钟评论他人帖子，我的曝光量翻倍了。"

Action Ideas

行动建议

  1. Shift to educational threads
    • Format: Problem → Solution (step-by-step) → Result
    • Evidence: Posts using this format getting 3-5× more impressions
  2. Abandon carousel strategy
    • Data: Engagement down 40-60% since December
  3. Allocate 30 min/day to comments
    • Tactic: Comment on posts from your ICP 10 min after posting (algorithm boost)
undefined
  1. 转向教育类帖子
    • 格式：问题→解决方案（分步）→结果
    • 证据：采用该格式的帖子曝光量提升3-5倍
  2. 放弃轮播图策略
    • 数据：自12月以来互动量下降40-60%
  3. 每天分配30分钟用于评论
    • 战术：在目标用户（ICP）发帖10分钟后评论（算法加权）
undefined

Real Case Study

真实用户案例

User: B2B SaaS marketer researching content trends quarterly
Before using skill:
  • Manual research: 2-3 hours per topic
  • Visited 20-30 sites, took scattered notes
  • Hard to identify patterns across sources
  • No systematic approach
After implementing /last30days:
  • Research time: 7-10 minutes per topic
  • Consistent output format (easy to reference later)
  • Pattern detection automatic
  • Copy-paste prompts immediately usable
Impact after 3 months:
  • 10 trend reports created (vs 2-3 before)
  • Content strategy pivots based on current signals, not guesses
  • Team shares research reports across org (became go-to intelligence source)
  • Time saved: ~20 hours/month
Quote: "I used to spend half a day researching trends, now it's 7 minutes. The pattern detection alone is worth it—I'd miss things reading manually."
用户： B2B SaaS营销人员，每季度研究内容趋势
使用本技能前：
  • 手动研究：每个话题耗时2-3小时
  • 访问20-30个网站，笔记零散
  • 难以跨来源识别模式
  • 无系统化方法
使用/last30days后：
  • 研究时间：每个话题仅需7-10分钟
  • 输出格式统一（便于后续参考）
  • 自动识别模式
  • 可直接复制的提示词即时可用
3个月后的影响：
  • 生成10份趋势报告（之前仅2-3份）
  • 基于当下信号调整内容策略，而非凭猜测
  • 团队内部共享研究报告（成为核心情报来源）
  • 每月节省时间：约20小时
用户评价： "我以前要花半天时间研究趋势，现在只需7分钟。单是模式识别功能就很值——手动研究时我总会遗漏这些信息。"

Configuration Options

配置选项

Standard Mode (default)

标准模式（默认）

/last30days [topic]
  • Searches web, Reddit, X
  • Synthesizes top patterns
  • Generates prompts + action ideas
/last30days [话题]
  • 搜索网页、Reddit、X
  • 整合核心模式
  • 生成提示词+行动建议

Deep Dive Mode

深度模式

/last30days [topic] --deep
  • Fetches and analyzes top 5 articles in full
  • More detailed quotes and data points
  • Takes 12-15 minutes instead of 7
/last30days [话题] --deep
  • 完整获取并分析前5篇文章
  • 提供更详细的引用和数据点
  • 耗时12-15分钟（而非7分钟）

Reddit-Only Mode

仅Reddit模式

/last30days [topic] --reddit-only
  • Focuses exclusively on Reddit discussions
  • Best for: Community sentiment, practitioner insights
/last30days [话题] --reddit-only
  • 仅聚焦Reddit讨论
  • 最适合：社区情绪分析、从业者洞察

Quick Brief Mode

快速简报模式

/last30days [topic] --quick
  • Top 3 patterns only
  • No detailed synthesis
  • 3-minute output
/last30days [话题] --quick
  • 仅输出前3个核心模式
  • 无详细整合
  • 3分钟即可完成输出

Pro Tips

专业技巧

  1. Use specific topics - "AI writing tools" better than "AI"
  2. Add context - "for B2B SaaS" or "for developers" narrows results
  3. Run monthly - Track trends over time, spot shifts early
  4. Combine with /reddit-insights - For deeper Reddit analysis
  5. Export to Notion - Keep a trends database
  6. Share with team - Intelligence is more valuable when distributed
  1. 使用具体话题 - 「AI写作工具」比「AI」效果更好
  2. 添加上下文 - 「针对B2B SaaS」或「针对开发者」可缩小结果范围
  3. 每月运行一次 - 跟踪趋势变化，及早发现转向
  4. 与/reddit-insights结合使用 - 进行更深度的Reddit分析
  5. 导出到Notion - 建立趋势数据库
  6. 与团队共享 - 情报在共享时价值更高

Common Use Cases

常见使用场景

GoalQuery ExampleOutput Value
Content ideas
/last30days AI productivity tools
Topics getting engagement now
Competitive research
/last30days Superhuman vs Spark email
User sentiment, pain points
Positioning
/last30days project management frustrations
Language customers use
Product validation
/last30days AI coding assistant pain points
Real problems to solve
Marketing tactics
/last30days cold email strategies 2026
What's working in market
目标查询示例输出价值
内容创意
/last30days AI productivity tools
当前获得互动的话题
竞品研究
/last30days Superhuman vs Spark email
用户情绪、痛点
定位策略
/last30days project management frustrations
客户使用的语言
产品验证
/last30days AI coding assistant pain points
需要解决的真实问题
营销战术
/last30days cold email strategies 2026
市场上有效的方法

Quality Indicators

质量指标

A good /last30days report has:
  • 3-5 clear patterns (not just random insights)
  • Quotes from actual users (not just article summaries)
  • Sentiment assessment (what's the vibe?)
  • Ready-to-use prompt (copy-paste quality)
  • Specific action ideas (not vague suggestions)
  • Source links for credibility
  • Recency verified (nothing from >30 days)
一份优质的/last30days报告应具备：
  • 3-5个清晰的模式（而非随机洞察）
  • 来自真实用户的引用（而非仅文章摘要）
  • 情绪评估（社区氛围如何？）
  • 可直接复制的提示词（高质量）
  • 具体的行动建议（而非模糊建议）
  • 来源链接（确保可信度）
  • 时效性验证（无超过30天的内容）

Limitations

局限性

This skill does NOT:
  • Access paywalled content (uses public sources only)
  • Provide academic-quality research (for speed, not depth)
  • Replace domain expertise (synthesizes existing knowledge)
  • Guarantee completeness (samples popular discussions)
Best for: Fast, directional intelligence. Not dissertation-level research.
本技能无法：
  • 访问付费墙内容（仅使用公开来源）
  • 提供学术级研究（优先速度而非深度）
  • 替代领域专业知识（仅整合现有知识）
  • 保证完整性（仅采样热门讨论）
最佳用途： 快速获取方向性情报，而非论文级研究。

Installation

安装方法

bash
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bash
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Copy skill to your skills directory

将技能复制到你的技能目录

cp -r last30days $HOME/.openclaw/skills/
cp -r last30days $HOME/.openclaw/skills/

Verify dependencies

验证依赖

/last30days --check-setup
/last30days --check-setup

First run

首次运行

/last30days "your topic here"
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/last30days "你的话题"
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Support

支持

Issues or missing sources? Provide:
  • Topic searched
  • Expected vs actual sources found
  • Any error messages
  • Your setup verification output
Built to replace 2-hour research sessions with 7-minute intelligence reports.
Know what's working RIGHT NOW. Not last quarter. Not last year. Today.
遇到问题或缺少来源？请提供：
  • 搜索的话题
  • 预期与实际发现的来源差异
  • 错误信息
  • 你的配置验证输出
旨在将2小时的研究工作替换为7分钟的情报报告。
了解当前真正有效的内容。不是上个季度，不是去年，就是现在。