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Professional software vendor delivering innovative solutions on the Softono platform. Specialized in both open-source and proprietary software development.

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Software by brikerman

skilless.ai
Open Source

skilless.ai

<h1 align="center">✨ skilless.ai</h1> <p align="center"> <strong>Empower Your Agent with Real Data</strong> </p> <p align="center"> <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-blue.svg?style=for-the-badge" alt="MIT License"></a> <a href="https://www.python.org/"><img src="https://img.shields.io/badge/Python-3.12+-green.svg?style=for-the-badge&logo=python&logoColor=white" alt="Python 3.12+"></a> <a href="https://github.com/brikerman/skilless.ai/stargazers"><img src="https://img.shields.io/github/stars/brikerman/skilless.ai?style=for-the-badge" alt="GitHub Stars"></a> </p> <p align="center"> <a href="#quick-install">Quick Install</a> · <a href="README.zh.md">中文</a> · <a href="#four-core-tools">Core Tools</a> · <a href="#three-ai-skills">AI Skills</a> · <a href="https://skilless.ai">Website</a> </p> --- ## Why Skilless? Your AI is great at writing and thinking — but ask it to go look something up, and it hits a wall: - 🔍 "Find recent reviews of this product" → **no good free search**, everything useful costs money - 🌐 "Read what's on this webpage" → **returns raw HTML soup**, completely unreadable - 📺 "What does this YouTube video cover?" → **can't extract subtitles**, too much manual work - 📡 "Follow these news feeds and summarize updates" → **have to write the code yourself** **Skilless turns this into one command:** ```bash curl -LsSf https://skilless.ai/install | bash ``` Auto-detects your environment, creates an isolated virtual environment, installs all dependencies. Zero system pollution, no sudo required, uninstalls cleanly. --- ## What You Can Do After Installing > 💡 **Recommended:** Use [OpenCode](https://opencode.ai) — open source, free, works great with Skilless out of the box. Also compatible with OpenClaw, Kilo Code, Cursor, and Claude Code. Just tell your AI what you need — it reads the skill files and figures out the rest: - "Find reviews of this product online" → searches the web - "Read what's on this page and summarize it" → extracts clean content from any URL - "What does this YouTube video talk about?" → pulls the transcript - "What's new in this RSS feed?" → parses and summarizes the feed **You don't need to remember any commands.** That's the point. --- ## Real-World Use Cases ### 📹 Video Download - **YouTube** → Download videos for offline viewing, extract subtitles, or get transcripts - **Bilibili** → Save Chinese videos locally, extract CC and danmaku - **TikTok, Twitter/X, Twitch, Vimeo** → Download clips, VODs, and videos without watermarks - **1700+ more sites** → Dailymotion, Rumble, Odysee, SoundCloud, Reddit video, and more ### 📝 Video Subtitles & Transcripts - Extract auto-generated or manual subtitles from any video - Organize fast-paced content into readable notes - Translate video subtitles to your language - Generate summaries from video transcripts ### 🎵 Media Processing - Extract audio from video as MP3 - Compress large video files to save space - Convert between formats (mkv→mp4, wav→mp3, etc.) ### 🔍 Web Search - "Help me compare noise-canceling headphones under $300" → searches reviews across the web - "What's the latest news on AI video generation?" → finds and summarizes recent articles - "Research the best practices for RAG systems" → semantic search for technical content ### 🌐 Web Content - "Read this article and summarize the key points" → extracts clean content - "Extract the main points from this documentation" → parses technical docs - "What does this page say about X?" → finds specific information ### 📊 Deep Research - **Multi-source comparison** → "Compare the top 5 project management tools and recommend one for a small team" - **Travel planning** → "Create a 7-day Tokyo guide with food and attraction recommendations" - **Industry analysis** → "Research all video generation AI models and create a comprehensive report" - **Fact-checked reports** → cross-validated information from multiple sources ### ✍️ Content Writing - Draft professional emails on any topic - Write articles backed by real research - Create documentation with proper citations --- **Real Example:** A user recently asked: *"Research all the video generation models on the market and create a comprehensive report."* Skilless automatically searched, read sources, cross-checked facts, and produced a structured multi-section report — all in one conversation. --- ## Three AI Skills Skills are installed as `SKILL.md` files under `~/.agents/skills/`. Your Agent reads them automatically and knows when to use which capability: | Skill | Purpose | |-------|---------| | **Brainstorming** | Turn vague ideas into actionable plans through dialogue — proposes 2-3 options with trade-offs | | **Research** | Multi-source cross-validated deep research — transforms AI from text generator to active researcher | | **Writing** | Produce articles, docs, and reports backed by real research data | ### Skill Ecosystem The three skills work together as a connected system — each can invoke the others when needed: ```mermaid graph LR B["🧠 Brainstorming"] -->|need data| R["🔍 Research"] B -->|document plan| W["✍️ Writing"] R -->|write report| W R -->|clarify scope| B W -->|gather data| R W -->|clarify brief| B ``` --- ## Quick Install **Mac / Linux** ```bash curl -LsSf https://skilless.ai/install | bash ``` **Windows (PowerShell)** ```powershell Invoke-RestMethod https://skilless.ai/install.ps1 | Invoke-Expression ``` <details> <summary>What does this command actually install?</summary> 1. **Installs uv** — ultra-fast Python package manager, placed in `~/.local/bin` 2. **Network detection** — auto-detects your environment and switches to local mirrors (Tsinghua TUNA in China) if needed 3. **Isolated deployment** — creates a fully isolated virtual environment in `~/.agents/skills/skilless/` with all dependencies: `yt-dlp` `fastmcp` `jina reader` `feedparser` 4. **Exposes CLI** — generates the `skilless.ai` executable, ready to use immediately *Zero system pollution · No sudo required · Easy to uninstall* </details> --- ## Technology Stack | Tool | Purpose | |------|---------| | [Jina Reader](https://github.com/jina-ai/reader) | Web page extraction | | [Exa](https://exa.ai) | AI semantic search, free, no key needed | | [yt-dlp](https://github.com/yt-dlp/yt-dlp) | Video & subtitle extraction, 1700+ sites | | [feedparser](https://github.com/kurtmckee/feedparser) | RSS/Atom parsing | | [uv](https://github.com/astral-sh/uv) | Ultra-fast Python package manager, isolated deployment | --- ## FAQ <details> <summary><strong>Do I need API keys?</strong></summary> No. All tools use free tiers: Exa search is accessed for free via MCP, Jina Reader requires no key, yt-dlp runs entirely locally. </details> <details> <summary><strong>Does this modify my system environment?</strong></summary> No. Everything is installed in an isolated virtual environment under `~/.agents/skills/skilless/`. No sudo, no changes to global Python or Node.js. </details> <details> <summary><strong>Which AI tools are supported?</strong></summary> Any tool that reads SKILL.md files from `~/.agents/skills/` will work. We recommend **[OpenCode](https://opencode.ai)** — it's open source and free. Also works with OpenClaw, Kilo Code, Cursor, and Claude Code. </details> <details> <summary><strong>How do I uninstall?</strong></summary> ```bash rm -rf ~/.agents/skills/skilless rm -rf ~/.agents/skills/skilless-* ``` </details> --- ## License [MIT](LICENSE)

AI Agents Browser Automation
191 Github Stars
Kashgari
Open Source

Kashgari

<!-- prettier-ignore-start --> <!-- markdownlint-disable --> <h1 align="center"> <a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'>Kashgari</a> </h1> <p align="center"> <a href="https://github.com/BrikerMan/kashgari/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/BrikerMan/kashgari.svg?color=blue&style=popout"> </a> <a href="https://join.slack.com/t/kashgari/shared_invite/enQtODU4OTEzNDExNjUyLTY0MzI4MGFkZmRkY2VmMzdmZjRkZTYxMmMwNjMyOTI1NGE5YzQ2OTZkYzA1YWY0NTkyMDdlZGY5MGI5N2U4YzM"> <img alt="Slack" src="https://img.shields.io/badge/chat-Slack-blueviolet?logo=Slack&style=popout"> </a> <a href="https://travis-ci.com/BrikerMan/Kashgari"> <img src="https://travis-ci.com/BrikerMan/Kashgari.svg?branch=master"/> </a> <a href='https://coveralls.io/github/BrikerMan/Kashgari?branch=master'> <img src='https://coveralls.io/repos/github/BrikerMan/Kashgari/badge.svg?branch=master' alt='Coverage Status'/> </a> <a href="https://pepy.tech/project/kashgari"> <img src="https://pepy.tech/badge/kashgari"/> </a> <a href="https://pypi.org/project/kashgari/"> <img alt="PyPI" src="https://img.shields.io/pypi/v/kashgari.svg"> </a> </p> <h4 align="center"> <a href="#overview">Overview</a> | <a href="#performance">Performance</a> | <a href="#installation">Installation</a> | <a href="https://kashgari.readthedocs.io/">Documentation</a> | <a href="https://kashgari.readthedocs.io/about/contributing/">Contributing</a> </h4> <!-- markdownlint-enable --> <!-- prettier-ignore-end --> 🎉🎉🎉 We released the 2.0.0 version with TF2 Support. 🎉🎉🎉 If you use this project for your research, please cite: ``` @misc{Kashgari author = {Eliyar Eziz}, title = {Kashgari}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/BrikerMan/Kashgari}} } ``` ## Overview Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks. - **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify. - **Powerful and simple**. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification. - **Built-in transfer learning**. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model. - **Fully scalable**. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure. - **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on the cloud. ## Our Goal - **Academic users** Easier experimentation to prove their hypothesis without coding from scratch. - **NLP beginners** Learn how to build an NLP project with production level code quality. - **NLP developers** Build a production level classification/labeling model within minutes. ## Performance Welcome to add performance report. | Task | Language | Dataset | Score | | -------------------------- | -------- | --------------------------- | ----- | | [Named Entity Recognition] | Chinese | [People's Daily Ner Corpus] | 95.57 | | [Text Classification] | Chinese | [SMP2018ECDTCorpus] | 94.57 | ## Installation The project is based on Python 3.6+, because it is 2019 and type hinting is cool. | Backend | kashgari version | desc | | ---------------- | -------------------------------------- | --------------------- | | TensorFlow 2.2+ | `pip install 'kashgari>=2.0.2'` | TF2.10+ with tf.keras | | TensorFlow 1.14+ | `pip install 'kashgari>=1.0.0,<2.0.0'` | TF1.14+ with tf.keras | | Keras | `pip install 'kashgari<1.0.0'` | keras version | You also need to install `tensorflow_addons` with TensorFlow. | TensorFlow Version | tensorflow_addons version | | ------------------------ | --------------------------------------- | | TensorFlow 2.1 | `pip install tensorflow_addons==0.9.1` | | TensorFlow 2.2 | `pip install tensorflow_addons==0.11.2` | | TensorFlow 2.3, 2.4, 2.5 | `pip install tensorflow_addons==0.13.0` | ## Tutorials Here is a set of quick tutorials to get you started with the library: - [Tutorial 1: Text Classification](./docs/tutorial/text-classification.md) - [Tutorial 2: Text Labeling](./docs/tutorial/text-labeling.md) - [Tutorial 3: Seq2Seq](./docs/tutorial/seq2seq.md) - [Tutorial 4: Language Embedding](./docs/embeddings/index.md) There are also articles and posts that illustrate how to use Kashgari: - [基于 Kashgari 2 的短文本分类: 数据分析和预处理](https://eliyar.biz/short_text_classificaion_with_kashgari_v2_part_1/index.html) - [基于 Kashgari 2 的短文本分类: 训练模型和调优](https://eliyar.biz/nlp/short_text_classificaion_with_kashgari_v2_part_2/index.html) - [基于 Kashgari 2 的短文本分类: 模型部署](https://eliyar.biz/nlp/short_text_classificaion_with_kashgari_v2_part_3/index.html) - [15 分钟搭建中文文本分类模型](https://eliyar.biz/nlp_chinese_text_classification_in_15mins/) - [基于 BERT 的中文命名实体识别(NER)](https://eliyar.biz/nlp_chinese_bert_ner/) - [BERT/ERNIE 文本分类和部署](https://eliyar.biz/nlp_train_and_deploy_bert_text_classification/) - [五分钟搭建一个基于BERT的NER模型](https://www.jianshu.com/p/1d6689851622) - [Multi-Class Text Classification with Kashgari in 15 minutes](https://medium.com/@BrikerMan/multi-class-text-classification-with-kashgari-in-15mins-c3e744ce971d) Examples: - [Neural machine translation with Seq2Seq](./examples/translate_with_seq2seq.ipynb) ## Contributors ✨ Thanks goes to these wonderful people. And there are many ways to get involved. Start with the [contributor guidelines](./docs/about/contributing.md) and then check these open issues for specific tasks. [Named Entity Recognition]: /tutorial/text-labeling/#chinese-ner-performance [People's Daily Ner Corpus]: /apis/corpus/#kashgari.corpus.ChineseDailyNerCorpus [Text Classification]: /tutorial/text-classification/#short-sentence-classification-performance [SMP2018ECDTCorpus]: /apis/corpus/#kashgari.corpus.SMP2018ECDTCorpus

ML Frameworks
2.4K Github Stars