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Agent Skill · 研究检索

last30days(近30天热点研究)

last30days 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

last30days 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 65,745。

总安装 275周安装 11GitHub Stars 13下载 89核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:last30days(近30天热点研究)
来源仓库:https://github.com/sales-skills/sales
仓库路径:skills/last30days
安装命令:
npx skills add https://github.com/sales-skills/sales --skill last30days
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/sales-skills/sales --skill last30days

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于根据关键词或任务场景进行信息检索与筛选的研究场景。通过 npx skills add 命令从指定仓库安装并使用。安装前建议确认权限范围和维护状态,注意可能触发联网或文件读写操作。

last30days 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/last30days

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/sales-skills/sales --skill last30days;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

last30days 怎么安装?

在终端执行 npx skills add https://github.com/sales-skills/sales --skill last30days。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

last30days 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

last30days 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

last30days 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

last30days 用的人多吗?

公开数据是累计安装 275 次,近一周安装 11 次,下载 89 次,GitHub 13 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

last30days 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
last30days

近30天热点研究 · 本页

27513接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

/last30days Research Skill

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.

Mode

Detect from context or ask: *"Quick pulse, full research, or strategic intelligence brief?"*

ModeWhat you getBest for
quickReddit only, top 10 insights, 10 minFast topic pulse, content spark
standardReddit + X + web, full synthesis with themesContent planning, market research
deepFull research + strategic brief + content angles + competitive intelligenceProduct decisions, campaign strategy

Default: standard — use quick if they want a fast read. Use deep if they're making a business or product decision.


Why This vs 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.

When to Use

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

Required Setup

This skill orchestrates multiple tools. Verify you have:

# 1. Brave Search API (for web_search)
# Already configured in OpenClaw by default

# 2. Bird CLI (for X/Twitter search)
source ~/.openclaw/credentials/bird.env && bird search "test" -n 1
# If this fails, install bird CLI first

# 3. Reddit Insights (optional but recommended)
# If you have reddit-insights MCP server configured, skill will use it
# Otherwise falls back to Reddit web search via Brave

Quick verification:

/last30days --check-setup

Should return:

  • ✅ Brave Search: Available
  • ✅ Bird CLI: Available
  • ✅ Reddit Insights: Available (or "Using web search fallback")

Workflow

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

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

Step 2: Reddit Search

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

Step 3: X/Twitter Search

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

Step 4: Deep Dive on Top Sources (Optional)

For the 2-3 most relevant links:

web_fetch: [article URL]

Purpose: Extract specific tactics, quotes, data points

Step 5: Synthesize & Package

  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

Output Template

# 🔍 /last30days: [TOPIC]
*Research compiled: [DATE]*
*Sources analyzed: [NUMBER] (Reddit threads, X posts, articles)*
*Time period: Last 30 days*

---

## 🔥 Top Patterns Discovered

### 1. [Pattern Name]
**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]"

---

### 2. [Pattern Name]
[Continue same format...]

---

## 📊 Reddit Sentiment Breakdown

| Subreddit | Discussion Volume | Sentiment | Key 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)

---

## 🐦 X/Twitter Signal Analysis

**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?]

---

## 📈 Web Article Highlights

**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]

---

## 🎯 Copy-Paste Prompt

**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]

---

## 💡 Action Ideas

**Immediate opportunities based on this research:**

1. **[Opportunity 1]**
   - What: [Specific action]
   - Why: [Evidence from research]
   - How: [Implementation steps]

2. **[Opportunity 2]**
   [Continue format...]

---

## 📌 Source List

**Reddit Threads:**
- [Thread title] - r/[sub] - [URL]

**X Threads:**
- [@handle] - [Tweet] - [URL]

**Articles:**
- [Title] - [Source] - [URL]

---

*Research complete. [X] sources analyzed in [Y] minutes.*

Real Examples

Example 1: Prompt Research

Query: /last30days Claude prompting best practices

Abbreviated Output:

# 🔍 /last30days: Claude Prompting Best Practices

## Top Patterns Discovered

### 1. XML Tags for Structure (12 mentions)
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."

### 2. Examples Over Instructions (9 mentions)
"Show, don't tell" — Provide 2-3 examples instead of long instructions.

### 3. Chain of Thought Explicit (7 mentions)
Add "Think step-by-step before answering" dramatically improves reasoning.

## Copy-Paste Prompt

<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.

Example 2: Competitive Intel

Query: /last30days Notion vs Obsidian 2026

Abbreviated Output:

## Top Patterns

### 1. "Notion for Teams, Obsidian for Individuals" (18 mentions)
Strong consensus: Notion wins for collaboration, Obsidian wins for personal PKM.

### 2. Performance Complaints About Notion (11 mentions)
"Notion is slow with 1000+ pages" — recurring pain point

## Reddit Sentiment

| Subreddit | Sentiment | Key Insight |
|-----------|-----------|-------------|
| r/Notion | 🟡 Mixed | Love features, frustrated by speed |
| r/ObsidianMD | 🟢 Positive | Passionate community, local-first advocates |

## Action Ideas

**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"

Example 3: Content Strategy

Query: /last30days LinkedIn content strategies working 2026

Abbreviated Output:

## Top Patterns

### 1. "Teach in Public" Posts Dominate (22 mentions)
Tactical, educational content outperforms thought leadership by 4-5×.

### 2. Carousels Are Fading (14 mentions)
"LinkedIn is deprioritizing carousels" — multiple reports of engagement drops.

### 3. Comment Engagement = Reach (16 mentions)
"Spend 30 min/day commenting on others' posts. Doubled my reach."

## Action Ideas

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)

Real Case Study

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."

Configuration Options

Standard Mode (default)

/last30days [topic]
  • Searches web, Reddit, X
  • Synthesizes top patterns
  • Generates prompts + action ideas

Deep Dive Mode

/last30days [topic] --deep
  • Fetches and analyzes top 5 articles in full
  • More detailed quotes and data points
  • Takes 12-15 minutes instead of 7

Reddit-Only Mode

/last30days [topic] --reddit-only
  • Focuses exclusively on Reddit discussions
  • Best for: Community sentiment, practitioner insights

Quick Brief Mode

/last30days [topic] --quick
  • Top 3 patterns only
  • No detailed synthesis
  • 3-minute output

Pro Tips

  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

Common Use Cases

GoalQuery ExampleOutput Value
Content ideas/last30days AI productivity toolsTopics getting engagement now
Competitive research/last30days Superhuman vs Spark emailUser sentiment, pain points
Positioning/last30days project management frustrationsLanguage customers use
Product validation/last30days AI coding assistant pain pointsReal problems to solve
Marketing tactics/last30days cold email strategies 2026What's working in market

Quality Indicators

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)

Limitations

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.

Installation

# Copy skill to your skills directory
cp -r last30days $HOME/.openclaw/skills/

# Verify dependencies
/last30days --check-setup

# First run
/last30days "your topic here"

Support

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.

平台分布

Codex

34.76%
按下载量换算31

Claude

28.69%
按下载量换算26

Cursor

16.74%
按下载量换算15

Gemini CLI

10.11%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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