last30days-skill — AI Agent 工作流中文教程
last30days-skill — AI Agent 工作流中文教程 是 AI Skill Hub 本期精选AI工具之一。在 GitHub 上收获超过 26.3k 颗 Star，综合评分 8.2 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
last30days-skill — AI Agent 工作流中文教程 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 last30days-skill — AI Agent 工作流中文教程 的版本更新，及时通知重要功能变化。
AI Agent技能工具，能跨越Reddit、X、YouTube、HN、Polymarket和网络进行主题研究，通过综合分析生成有据可查的总结报告。适合需要快速获取多源信息、进行热点话题追踪和数据研究的用户。
last30days-skill — AI Agent 工作流中文教程 是一款基于 Python 开发的开源工具，专注于 信息聚合、多源爬取、AI代理 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
AI Agent技能工具，能跨越Reddit、X、YouTube、HN、Polymarket和网络进行主题研究，通过综合分析生成有据可查的总结报告。适合需要快速获取多源信息、进行热点话题追踪和数据研究的用户。
last30days-skill — AI Agent 工作流中文教程 是一款基于 Python 开发的开源工具，专注于 信息聚合、多源爬取、AI代理 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 方式一：pip 安装（推荐）
pip install last30days-skill
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install last30days-skill
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/mvanhorn/last30days-skill
cd last30days-skill
pip install -e .
# 验证安装
python -c "import last30days_skill; print('安装成功')"
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 命令行使用
last30days-skill --help
# 基本用法
last30days-skill input_file -o output_file
# Python 代码中调用
import last30days_skill
# 示例
result = last30days_skill.process("input")
print(result)
# last30days-skill 配置文件示例（config.yml） app: name: "last30days-skill" debug: false log_level: "INFO" # 运行时指定配置文件 last30days-skill --config config.yml # 或通过环境变量配置 export LAST30DAYS_SKILL_API_KEY="your-key" export LAST30DAYS_SKILL_OUTPUT_DIR="./output"
/last30days
<p align="center"> <a href="https://github.com/mvanhorn/last30days-skill"> <img src="https://img.shields.io/badge/%231-Repository%20Of%20The%20Day-6f42c1?style=for-the-badge&logo=github&label=GITHUB%20TRENDING" alt="GitHub Trending #1 Repository Of The Day" /> </a> <br/> <a href="https://trendshift.io/repositories/21997" target="_blank"> <img src="https://trendshift.io/api/badge/repositories/21997" alt="mvanhorn/last30days-skill | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/> </a> </p>
An AI agent-led search engine scored by upvotes, likes, and real money - not editors.
This README tracks the current v3 pipeline. The runtime skill spec lives in skills/last30days/SKILL.md, which is the source of truth for the latest command and setup behavior.
Claude Code (recommended — auto-updates via marketplace):
/plugin marketplace add mvanhorn/last30days-skill
/plugin install last30daysCodex, Cursor, Copilot, Gemini CLI, or any of 50+ Agent Skills hosts:
npx skills add mvanhorn/last30days-skill -g-g installs globally for your user, available across all projects. Drop it to scope per-project.)
More install options (claude.ai web, OpenClaw, manual) in the Install section below.
Zero config. Reddit, HN, Polymarket, and GitHub work immediately. Run it once and the setup wizard unlocks X, YouTube, TikTok, and more in 30 seconds.
---
Reddit upvotes. X likes. YouTube transcripts. TikTok engagement. Polymarket odds backed by real money and insider information. That's millions of people voting with their attention and their wallets every day. /last30days searches all of it in parallel, scores it by what real people actually engage with, and an AI agent judge synthesizes it into one brief.
Google aggregates editors. /last30days searches people.
You can't get this search anywhere else because no single AI has access to all of it. Google search doesn't touch Reddit comments or X posts. ChatGPT has a deal with Reddit but can't search X or TikTok. Gemini has YouTube but not Reddit. Claude has none of them natively. Each platform is a walled garden with its own API, its own tokens, its own auth. But you can bring your own keys and browser sessions, and suddenly an AI agent can search all of them at once, score them against each other, and tell you what actually matters.
That's the unlock. Not one better search engine. A dozen disconnected platforms, bridged by an agent.
/last30days Peter SteinbergerYou have a meeting tomorrow. You Google them. You get their LinkedIn from 2023. /last30days gives you what they're actually doing this month: joined OpenAI to work on Codex, fighting Anthropic's ban on third-party agents, shipping 23 PRs at 85% merge rate, building "LobsterOS" for cross-device agent control, and r/ClaudeCode hit 569 upvotes debating whether he's a hero or "insufferable." Scattered across X posts, Reddit threads, YouTube transcripts, and GitHub commits. None of it was on Google.
Intelligent search: the killer feature
The v3 engine doesn't just search for your topic. It figures out where to search before the search begins. Type "OpenClaw" and the engine resolves @steipete (Peter Steinberger, the creator), r/openclaw, r/ClaudeCode, and the right YouTube channels and TikTok hashtags - all via a new Python pre-research brain built by @j-sperling. The old engine searched keywords. The new engine understands your topic first, then searches the right people and communities.
This is why v3 finds content v2 never could. "Paperclip" resolves @dotta. "Dave Morin" resolves @davemorin plus @OpenClaw plus the TWiST podcast. "Peter Steinberger" resolves @steipete on X and steipete on GitHub. Bidirectional: person to company, product to founder, name to GitHub profile. The right subreddits, the right handles, the right hashtags - resolved before a single API call fires.
Install
| Surface | Install | Updates |
|---|---|---|
| **Claude Code** (recommended) | /plugin marketplace add mvanhorn/last30days-skill | Auto via marketplace, or claude plugin update last30days@last30days-skill |
| **Codex, Cursor, Copilot, Gemini CLI, GitHub Copilot, or any of 50+ [Agent Skills](https://agentskills.io) hosts** | npx skills add mvanhorn/last30days-skill -g | npx skills update last30days -g |
| **claude.ai** (web) | [Download last30days.skill](https://github.com/mvanhorn/last30days-skill/releases/latest/download/last30days.skill) and upload via Settings > Capabilities > Skills > + | Re-download and re-upload |
| **OpenClaw** | clawhub install last30days-official | clawhub update last30days-official |
Interactive setup — prompts for each known key, skip with empty input
skills/last30days/scripts/setup-keychain.sh
macOS Keychain (optional)
On macOS you can store keys in the system Keychain instead of a .env file. The skill picks them up automatically as the lowest-priority source — .env files and process environment still win on collision.
```bash
Configuration
Two things you'll likely want to know on day one:
Where research files are saved. LAST30DAYS_MEMORY_DIR defaults to ~/Documents/Last30Days/ (Windows: C:\Users\<you>\Documents\Last30Days\). Override by setting that env var to any path in your shell, or --save-dir <path> per run. Use --save-suffix=<name> to keep multiple variations of the same topic separate (e.g. per client). Each run produces <slug>-raw[-suffix].md.
Trend monitoring across runs. The default mode produces a fresh markdown snapshot per run. To accumulate findings over time, add --store to persist into a SQLite database, then use scripts/watchlist.py for scheduled runs (with optional Slack / webhook delivery on new findings) and scripts/briefing.py for daily / weekly digests. The full cadence pattern is in CONFIGURATION.md.
Per-client wrapper scripts, custom category-peer subreddits, and the experimental beta channel for in-progress customizations are also documented in CONFIGURATION.md.
Single-pass comparisons
"CLI vs MCP" used to run three serial passes (12+ minutes). v3 runs one pass with entity-aware subqueries for both sides simultaneously. Same depth, 3 minutes.
Auto-discovered competitor comparisons
/last30days OpenAI --competitors tells the hosting reasoning model to discover the top 2 peers via WebSearch (Anthropic, xAI), run Step 0.55 per entity, and invoke the engine with "OpenAI vs Anthropic vs xAI" and a per-entity --competitors-plan JSON. The engine fans out 3 full pipelines in parallel, saves a *-raw.md file per entity, and merges them into a 3-way comparison. Same mechanics power /last30days "OpenAI vs Anthropic vs xAI" directly.
功能完整度高，多源聚合设计优秀，Claude集成成熟。23K星社区认可度强，适合专业研究和内容创作。维护活跃度良好。
- 使用 Cursor 编辑器、希望提升 AI 编程效率的开发者
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- Cursor rules 控制在 80 行内，否则模型上下文成本会显著上升
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- Python 依赖冲突：建议用 venv / uv 隔离环境
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 使用 Cursor 编辑器、希望提升 AI 编程效率的开发者
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- Cursor rules 控制在 80 行内，否则模型上下文成本会显著上升
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- Python 依赖冲突：建议用 venv / uv 隔离环境
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
- +GitHub 26.3k Star，社区高度认可
- +MIT 协议，可免费商用
- +AI Skill Hub 精选推荐
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
经综合评估，last30days-skill — AI Agent 工作流中文教程 在AI工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | last30days-skill |
| 原始描述 | AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary |
| Topics | 信息聚合多源爬取AI代理Claude自动化研究 |
| GitHub | https://github.com/mvanhorn/last30days-skill |
| License | MIT |
| 语言 | Python |
收录时间：2026-05-22 · 更新时间：2026-05-22 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。