By dylanbaker24Updated 2026-05-17last30days-lite@clawhub_dylanbaker24/last30days-lite
Data InsightsWeb ScrapingCompetitor Analysis#Twitter
According to AlphaMoat, as of 2026-10, Last 30 Days (Lite) has 7,235 downloads and 2 stars — ranked by downloads #3,879 among 10,914 AI skills in this ranking, and #362 of 915 in Data Analysis.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| ClawHub ↗ | 4.8K | 2 | - |
| SkillHub ↗ | 7.2K | 2 | — |
Platforms count differently and update at different times.
The source describes searching web, Reddit, and X/Twitter with freshness filters, then synthesizing findings into key patterns, common mistakes, techniques, and source links. Recency and coverage depend on search services, platform access, and retrieved results. Social posts are not automatically verified facts. Distinguish sources, dates, personal experiences, and conclusions, and avoid exposing private query content. The guide calls bird optional for X, while metadata lists it as required; X access also needs configured cookies. Queries go to external search services.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 7.2K | — | 2 |
| 2026-09 | 5.1K | — | 2 |
| 2026-08 | 4.5K | +4.7% | 2 |
| 2026-07 | 4.3K | — | 2 |
| 2026-05 | 3.8K | — | 2 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
---
name: last30days
description: Research any topic across Reddit, X/Twitter, and the web from the last 30 days. Synthesizes findings into actionable insights or copy-paste prompts.
metadata: {"clawdbot":{"emoji":"📅","requires":{"bins":["bird"]}}}
---
# last30days 📅
Research any topic using recent (last 30 days) discussions from Reddit, X/Twitter, and the web. Returns synthesized insights and actionable prompts.
## Overview The AI world reinvents itself monthly. This skill keeps you current by researching what people are actually saying *right now* - not what worked six months ago. **What it does:** - Searches the web, Reddit, and X/Twitter with freshness filters (last 30 days) - Finds real practitioner experiences, not just SEO content - Synthesizes findings into actionable insights - Generates copy-paste prompts based on current best practices **Best for:** - Prompt research (what techniques actually work for ChatGPT, Midjourney, Claude, etc.) - Trend discovery (what's viral, what people are recommending) - Product feedback (what do real users think about X?) - Fast-moving topics where recency matters **Requirements:** - Brave Search (built into Clawdbot) - `bird` CLI for X/Twitter (optional but recommended) - No extra API keys needed ## Usage When user asks for recent info on a topic, or uses "/last30days [topic]": ### Step 1: Web Search (Brave with freshness) ``` web_search(query="[topic]", freshness="pm", count=5) ``` - `pm` = past month - Also try: `pd` (24h), `pw` (week) ### Step 2: Reddit Search ``` web_search(query="site:reddit.com [topic]", freshness="pm", count=5) ``` Focus on r/ClaudeAI, r/ChatGPT, r/LocalLLaMA, r/MachineLearning, r/StableDiffusion, etc. ### Step 3: X/Twitter Search ```bash bird search "[topic]" -n 10 --plain ``` Look for practitioners sharing real experiences, not just engagement bait. ### Step 4: Deep Dive (optional) For promising URLs, use `web_fetch` to get full content: ``` web_fetch(url="https://reddit.com/...", maxChars=10000) ``` ### Step 5: Synthesize Combine findings into: 1. **Key patterns** - What are people actually doing that works? 2. **Common mistakes** - What should be avoided? 3. **Tools/techniques** - Specific methods mentioned 4. **Copy-paste prompt** (if applicable) - Ready-to-use prompt incorporating best practices ## Output Format ```markdown ## 📅 Last 30 Days: [Topic] ### What's Working - [Pattern 1] - [Pattern 2] ### Common Mistakes - [Mistake 1] ### Key Techniques - [Technique with source] ### Sources - [URL 1] - [brief description] - [URL 2] - [brief description] ### Ready-to-Use Prompt (if applicable) ``` [Generated prompt based on findings] ``` ``` ## Examples - `/last30days Midjourney v7 prompting` - `/last30days Claude Code best practices` - `/last30days what are people saying about M4 MacBook` - `/last30days Suno music prompts that actually work` ## Notes - No extra API keys needed (uses Brave + bird) - Bird requires X/Twitter cookies (already configured) - Focus on signal over noise - prioritize upvoted content and verified practitioners
中国金融机构数据爬取与分析综合 Skill v12.0.0。2334家/43大类机构名单、60+财经媒体源舆情、 A股报告/公告/研报、60+种查询多源金融数据（自动降级+缓存）、爬取调度与网页操作增强、 实时数据中枢、24 技术指标引擎、多源证据融合、110 MCP 工具 + 10 Resource + 14 Prompt。 🆕 v12.0.0 四条线（信息获取 / 打包下载 / 数据分析 / 登录）： 编排：gather_info() 一次跑完"路由→抓取→去占位→质量分→能力边界→回执→打包"， 8 类意图各带问法、输入输出、典型耗时与 not_for（说清哪件事做不了）。 诚实：反爬占位条目一律剔除并记账，空结果报 E_SOURCE_EMPTY，不把假数据判成 B 级成功。 交付：包内自带 manifest.json（逐文件 sha256），verify 查得出改动/缺失/多塞， 批量下载单条失败不中断整批，失败清单可直接只重下那几条。 登录：Cookie 由用户导出或扫码接管，档案化 + 健康度四档，对外输出全脱敏（值永不回显）。 何时触发：爬取机构、舆情爬取、东方财富、公告下载、实时行情、技术指标、统一查询、 交付包、批量重下、我已经登录了、登录态还有效吗（完整触发词见下方 auto_trigger）。
大宗商品价格数据查询技能。用于查询大宗商品期现货价格、宏观经济指标、产业链供需数据、进出口库存数据、金融市场数据等。当用户询问：(1) 期货/现货价格查询，如"螺纹钢价格"、"铜期货收盘价"； (2) 历史行情或趋势分析；(3) 区域或企业价格对比；(4) 宏观经济指标，如GDP、CPI；(5) 产业链上下游数据；(6) 多市场金融数据（期权、股票、债券、外汇）；(7) 结算确认单生成时触发此技能。
将数据文件（CSV/TSV/TXT/XLSX/XLS/JSON）转化为独立交互式 ECharts HTML 图表——32 类图表、3 套主题，全离线无需 CDN。出图前可先取数据画像（--profile：每列 dtype/基数/缺失/样本/统计、实体粒度、候选洞察信号、疑似非数据行），让 agent 在用户没说想看什么时自己读懂数据并决定出什么图——哪列是 ID、哪列是维度、出什么图，全部由 agent 从画像的客观事实自行判断，技能不代笔；每张图内置事实卡（plot_stats + data_preview），其数值是解读时唯一可引用的数字。本技能负责出图与算事实，不产出书面报告或叙事文档。触发：用户提供或指向表格数据文件并要求出图；提到「画图/出图/可视化/生成图表/generate a chart/make a graph」；想把表格渲染成交互式 HTML 图表。不触发：要的是书面报告、叙事分析或幻灯片；没有数据文件；需要后端的仪表盘；图片/音频/视频等非表格内容。
The data layer for AdMapix: turn ad and app queries into API calls that return raw JSON only.
论文配图一键生成：一条命令从 JSON/CSV/Excel 生成顶刊级学术图表，科研绘图 与期刊配图全场景覆盖。40 种图表（森林图/KM 生存曲线（自动风险表）/竞争风险累计发生率/ROC/校准 曲线/决策曲线 DCA/列线图/RCS 非线性剂量反应/亚组分析森林图/RMST 限制平均生存时间/Galbraith 径向图/火山图/雨云图/山脊图/哑铃图/游泳图（逐例时长+里程碑）/ 肿瘤缓解瀑布图/UpSet 交集/PRISMA 流程图/漏斗/BA 一致性/PCA/热图/箱线/小提琴/ 组合图/Cox 多因素回归森林图等），9 套配色（含色盲安全 Okabe-Ito 与期刊色板），9 种期刊投稿预设， YAML 流水线一条命令出全套论文图，一次渲染多格式导出（TIFF/PNG/PDF）， PDF 重叠门禁，600dpi。纯本地运行，数据不出机，中文零配置。适用：写论文/ 投稿要配图、把数据变成可投稿图表、批量重出全套图。不适用：文本润色/翻译/ 改写（请用写作类工具）；商业报表/仪表盘/海报装饰；无数据支撑的纯示意图。
Intelligent chart generation and data analysis skill. Reads user-supplied data files (CSV/Excel/JSON), analyzes data characteristics with LLM assistance, auto-recommends and generates interactive ECharts visualizations. Use when the user asks to analyze data, generate charts, create visualizations, or work with tabular data files.
全球智能投研工具（stock-researcher）v13.0：多源数据 + 概率预测 + 可导出报告。 核心能力： - 多源数据路由：按市场/类别选源，逐级降级；每个结果标注来源与时效等级；源不可用时明确标 unavailable - 全资产量化：股票/ETF/场外基金/指数/期货（期限结构、基差、持仓、折溢价）多因子分析 - 概率预测：1d/5d/1M/3M/6M 经验分布 + 分位数 + 方向概率 + 区间，kNN 邻域收缩融合全局分布； 样本不足自动弃权，可按历史命中样本（同桶）做概率校准 - 证据链简报：每条结论绑定来源、时间与数据质量，冲突信号保留而不抹平 - 报告导出：Word/PPT/PDF/Markdown/JSON/纯文本，纯标准库渲染 + 可选第三方引擎 - MCP Server 24 工具，Claude Code 可直接调用；知识库 12 适配器双向同步 三条铁律： - 数据不装：拿不到就报不可用，不拿 0 价、不拿空表冒充 - 预测不装：概率来自历史相似条件的经验分布，样本不足就说不足，不硬给方向 - 评分≠盈利保证：任何结论都带免责声明
Searches the public web through Tavily and returns structured results with links and snippets.
Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.