Streamlit

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

[Question] How to extract/package a specific "Claude Code Skill" workflow into a standalone app? (中文：如何将特定的 Claude Code Skill 流程提取并封装为独立 App？)

Introduction: I’ve been using Claude Code and created several custom workflows using SKILL.md. These "Skills" work great within the terminal, but I want to take it a step further: How can I extract a specific Skill’s logic and package it into a standalone application (Web or Desktop) for others to use without them needing to install Claude Code? Key Challenges: Context Injection: In Claude Code, the SKILL.md is automatically injected into the context. In a custom app, should I just paste it into the System Prompt, or is there a better way to handle the metadata (triggers, permissions)? Action Execution: Many Skills rely on Claude Code’s ability to run shell commands or edit files. If I move this to a Web App, what’s the best alternative for this "Agentic" loop? (e.g., using MCP, LangGraph, or custom tool-calling?) Existing Projects: Are there any open-source projects or frameworks that specifically facilitate the migration of Claude Skills to standalone agents/apps? What I'm looking for: Best practices for "Skill-to-App" migration. Recommended tech stacks (e.g., Streamlit + Anthropic SDK vs. Electron + MCP). Any existing GitHub repos that serve as a "Skill Wrapper." 中文翻译（方便你根据讨论回复）： 简介： 我一直在使用 Claude Code，并利用 SKILL.md 创建了一些自定义工作流。这些 Skill 在终端里运行得很好，但我想更进一步：如何提取特定的 Skill 逻辑，并将其封装成一个独立的应用程序（Web 或桌面端），让其他没安装 Claude Code 的用户也能使用？ 核心难题： 上下文注入： Claude Code 会自动注入 SKILL.md。在自定义 App 中，是直接把内容贴进 System Prompt，还是有更好的处理元数据（触发器、权限）的方法？ 动作执行： 很多 Skill 依赖 Claude Code 运行 Shell 或编辑文件的能力。如果迁移到 Web App，实现这种“智能体循环”的最佳替代方案是什么？（比如 MCP, LangGraph 还是自定义 Tool-calling？） 现有项目： 有没有专门将 Claude Skills 迁移为独立 Agent/App 的开源项目或框架？ 我希望得到： “Skill 转 App”的最佳实践。 推荐的技术栈。 任何可以作为“Skill 包装器”的 GitHub 仓库。 submitted by /u/Electronic_Film2004 [link] [comments]

Looking for Feedback & Improvement Ideas[P]

Hey everyone, I recently built a machine learning project and would really appreciate some honest feedback from this community. LINK- https://predictlab9.streamlit.app/ PredictLab is an interactive ML web platform that lets users explore classification, regression, NLP, clustering, time series, and recommendation systems — all in one place. Built with Python, Streamlit, and Scikit-learn, it makes machine learning hands-on and accessible without writing a single line of code. I’d love to get your thoughts on a few things: Is this project strong enough to include on a resume for ML/DS roles? What features or improvements would make it more “real-world” or impactful? Any feedback on the approach, model selection, or overall design? submitted by /u/teabagdiplomat [link] [comments]

Vibe coding is about to make drag-and-drop BI feel obsolete

I built Lumyr, a tool that lets you describe the dashboard you want in plain English and get back a live, shareable dashboard. What I find interesting is that the value here is not just speed. In a lot of cases, the result can actually be better than what teams typically build in drag-and-drop BI tools, because you are not constrained by the limits of the UI. Claude is doing the heavy lifting on the dashboard generation side, while Lumyr provides the harness around it to handle the Python and Streamlit layer automatically. So the user does not need to: write Python open Streamlit deploy anything set up hosting manage infra They just ask for the dashboard they want. I built this because a lot of analytics workflows still feel much slower and more rigid than they should. The common pattern is still: ask someone for a dashboard, wait, get something half-right, then go back and forth again. I wanted to see how far Claude could push a more direct workflow. It’s free to try here if anyone wants to test it: lumyr.io submitted by /u/Serious_Control829 [link] [comments]

I built a global news intelligence platform with Claude Code in 3 days

Wanted to see if pattern recognition across global news data could surface insights (and if I could build something like it). Not predictions. Just tracking where the world's attention is going, where it's leaving, and what patterns emerge over time. How it works: Pulls 1,600+ articles/day from RSS feeds and 3 news APIs (GNews, Currents, World News API) across 20+ regions. Each article gets structured metadata stored in SQLite: spaCy handles entity extraction locally (people, orgs, locations) Mistral (free tier) tags topics, sentiment, and narrative framing scikit-learn TF-IDF + cosine similarity finds related articles across regions without keyword matching NetworkX builds co-occurrence graphs between topics, regions, and entities Anomaly detection (Isolation Forest) flags when a region's topic distribution breaks its own 30-day baseline The key design is layered analysis. The daily API calls aren't the insight, they're raw material. Layer 1: Daily fetch. 1,600 articles tagged and stored. Layer 2: Daily synthesis. Mistral reads all of today's data as one global snapshot. Flags stuff like "four unrelated countries discussed semiconductor supply chains today." Layer 3: Weekly. Feed it all seven daily syntheses. Now it compares across time and geography together. Layer 4: Monthly. This is where the interesting stuff should emerge. Trend lines and narrative shifts you'd never catch reading daily headlines. Each layer compresses signal. You never ask the LLM to remember 3,000 articles. You feed it 7 summaries that already distilled those articles. Hindsight Mode is the part I don't fully know what to do with yet. It builds signature vectors from known events (topics, sentiment, narrative framing), then scores today's live news against them using cosine similarity. So it might say "current patterns are 71% similar to what things looked like before the last trade war." Cool, but what do you actually do with that information? Open to ideas here. What I'm watching for: Did water scarcity coverage start spiking in East Africa, show up in South American outlets two weeks later, then hit policy discussions in Europe a month after? That kind of propagation is invisible if you only read one region's news. Built the whole thing with Claude Code. Used dullnote to track the build log and roadmap between sessions so Claude had full context on what was done and what was next. Stack: Python, SQLite, Streamlit, Mistral free tier, spaCy, scikit-learn, NetworkX, sentence-transformers and dullnote Still early. Need 60+ days of data before the cross-region stuff gets really interesting, but the layered analysis is already producing non-obvious outputs. Happy to answer questions or tell me what I should add. I added the video to the post but it didnt render so: Dashboard Demo video: https://streamable.com/tx70b4 submitted by /u/Minute_Bit8225 [link] [comments]