k-dense-byok

作者 K-Dense-AI已验证

An AI co-scientist running on your desktop. Claude Science but better.

1,031
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TypeScript
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2026/8/23
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⚠️ 第三方软件声明

本 Skill 为第三方开源软件,独立托管于 GitHub。SkillTip 仅为信息目录,不控制或维护底层仓库。所显示的安全检查为自动化且范围有限,安装前请自行审查源码。

阅读服务条款

安装

添加到你的 Claude Code skills 目录:

# Add to your Claude Code skills
git clone https://github.com/K-Dense-AI/k-dense-byok

快速入门

使用 k-dense-byok 等 Skills 的指南。

安全报告

已验证

上次扫描:—

{
  "status": "PASSED",
  "issues": []
}

README.md

K-Dense BYOK

License: MIT Version Skills Workflows Databases Tests X LinkedIn YouTube Reddit

📅 Join our free live event: Getting Started with K-Dense BYOK

An online walkthrough of installing Kady and running your first research task, with live Q&A. Tuesday, August 25, 2026 · 2:00 PM PT (9:00 PM UTC) · Online

👉 Register for free on Luma

Your own AI research assistant, running on your computer, powered by the accounts and API keys you choose.

K-Dense BYOK — Kady running an end-to-end single-cell RNA-seq analysis: asking in plain language, streaming tool calls, the generated figures and report, the living lab notebook, and the skills and specialists settings

K-Dense BYOK (Bring Your Own Keys) is a free, open-source app that gives you Kady — an AI research assistant for scientists in any field. Describe a task in plain language — analyze this dataset, review my manuscript, search the literature, build this figure — and Kady works through it in a complete research workspace. It can inspect your files, write and run analysis code, search and read sources, create figures and reports, and keep a living record of what it did.

Three things to know up front:

  • No coding experience required. You describe what you want; Kady writes and runs the code and shows you its progress as it works.
  • Your workspace stays on your computer. Projects, conversations, notebooks, and results live in ordinary folders on your machine; K-Dense does not host or store them. When you use a hosted AI model, the material needed for that request is sent directly to the provider you selected under that provider's privacy terms. Use a local Ollama model when data must not leave your machine.
  • The app itself is free; provider charges and limits remain yours. Use prepaid OpenRouter, connect a supported AI subscription, or run free local models. Kady tracks paid OpenRouter usage and Anthropic OAuth's documented metered extra usage against project spending caps. ChatGPT, Copilot, and xAI subscription usage is tracked separately because those providers manage quotas and overages; a subscription login does not imply unlimited or free usage.

Beta: K-Dense BYOK is currently in beta. Many features and improvements are on the way. Star us on GitHub to stay in the loop, and follow K-Dense on X, LinkedIn, YouTube, and Reddit for release notes and tutorials.

🎬 Prefer to watch first? The Future of Research is Open: Introducing K-Dense BYOK walks through what the app does and how to get set up. More walkthroughs in Tutorial videos.

Internal benchmark

Internal benchmark comparing K-Dense BYOK with Claude Science and Biomni Lab across scientific quality and research execution

This figure compares K-Dense BYOK, Claude Science, and Biomni Lab across scientific quality and research execution in a 20-prompt benchmark. For these runs, K-Dense BYOK was configured to use Claude Opus 4.8 with the xHigh reasoning level. The benchmark was designed, run, and evaluated internally by K-Dense rather than an independent third party, so the results should be interpreted as an internal evaluation under the tested setup, not as a universal measure of platform performance.

Related reading on how we evaluate: K-Dense Web vs. Claude Science walks through the same 20-task comparison for our hosted platform, and Introducing K-Bench 01 covers our broader benchmark of nine frontier models on 178 real scientific tasks.

What can it do?

Kady is designed to carry out research work, not only answer questions. You remain the scientist in charge: you can watch each step, inspect the code and files it creates, redirect it while it works, and stop a run at any time.

From a research question to usable results

  • Analyze real datasets. Ask Kady to clean data, check quality, choose and run statistical methods, compare groups, fit models, or generate publication-ready figures. It writes and runs the code inside the project, so the scripts, intermediate files, tables, figures, and reports remain available for inspection and reuse.
  • Review evidence and documents. Kady can search the web and read web pages, PDFs, GitHub repositories, and YouTube videos. It can compare papers, extract methods, audit a manuscript, summarize evidence, or follow links to supporting material. Web search works without an additional account; optional search-provider keys improve capacity.
  • Work with text, data, and images. Type or dictate a request, upload files through the project browser, attach project files to a conversation, or paste/drop images directly into a message for a vision-capable model to inspect.
  • Ask before it assumes. If a study design, comparison, output format, or other requirement is ambiguous, Kady can pause and show a short in-chat question form — including multiple choice, free text, and image input — rather than silently guessing.
  • Keep working while it works. Add up to five follow-up messages to a running conversation, steer the current analysis, or continue in another chat or project.

A scientific toolkit built in

  • 149 scientific skills cover genomics, proteomics, bioinformatics, drug discovery, chemistry, materials science, clinical research, and more. Kady activates the relevant procedures automatically, and you can browse or disable them in Settings.
  • 326 guided workflow templates across 22 disciplines turn common analyses into fill-in-the-blank starting points. Choose a workflow, supply the study details, and launch it into the active chat.
  • 229 scientific and financial data resources across 18 categories give Kady guidance for finding information in biomedical, chemical, scholarly, market, earth-science, climate, and space databases. Some resources require their own free key.
  • 21 scientific specialists can take focused assignments such as statistical review, citation checking, peer review, data analysis, or literature synthesis. Kady can delegate independent work in parallel and combine the findings, or you can call a specialist by name. Learn more.
  • A Living Lab Notebook records the reasoning trail. As Kady and its specialists work, they can log hypotheses, methods, observations, decisions, confidence, code, and linked artifacts. You can connect evidence to hypotheses, pin and comment on entries, add your own notes, view one chat or the whole project, export Markdown/JSON/a bundle with artifacts, print to PDF, and generate a manuscript-style Methods draft. Learn more.

Read and inspect scientific files without leaving the app

  • Preview 60+ scientific formats alongside everyday CSV, PDF, Markdown, image, code, and Jupyter notebook files. View interactive 3D protein and molecular structures, 2D chemical structures, spectra and chromatograms, sequence alignments, phylogenetic trees, single-cell and array data, and DICOM/NIfTI/microscopy images. See the full format list.
  • Edit text and code in place, inspect tables and notebook outputs, annotate images and PDFs, reveal files cited in chat, ask Kady to organize the project folder, and download an individual result, a folder, or the complete project as a ZIP archive.
  • Write papers in LaTeX with a split source/PDF view, automatic compilation, pdfLaTeX/XeLaTeX/LuaLaTeX support, outline and word count, inline errors, autocomplete, spell check, and two-way jumps between source and PDF. AI-assisted edits and compile fixes appear as diffs you can accept or revert.

Run several lines of work at once — and return later

  • Projects are independent research workspaces. Each project has its own files, chats, notebook, model choices, tags, archive state, and spending policy. Several projects can run at the same time, and the project directory shows which ones are running, finished, waiting for your input, blocked, or errored.
  • Use up to 10 parallel chat tabs per project. Each tab has its own conversation, model, thinking level, compute choice, attachments, draft, queue, and cost, while all tabs share the project's files.
  • Refresh without losing your place. Open projects, tabs, drafts, queued messages, panel sizes, open files, and active turns are restored after a browser refresh or browser-tab closure. A live turn reconnects to the same run and continues streaming as long as the Kady backend remains running. Completed conversations stay on disk and can be reopened from Chat history.
  • Arrange the workspace for the task. Resize or collapse the file browser and chat to focus on a figure, report, notebook, or LaTeX document; Kady remembers the layout.

Choose the right model and compute for each task

  • Connect supported subscriptions directly through Pi OAuth. In Settings → Model providers, connect ChatGPT Plus/Pro (openai-codex), Claude Pro/Max (anthropic), GitHub Copilot, or xAI. Kady handles the provider's browser, device-code, or manual sign-in flow and makes its available models appear in the picker.
  • Use major hosted models from OpenAI, Anthropic, Google, xAI, Qwen, and others through one OpenRouter account. Change the model and reasoning level independently in each chat.
  • Use NVIDIA NIM models directly with an API key from build.nvidia.com — Nemotron, Llama, GPT-OSS, and more, billed against your NVIDIA API credits rather than per-token dollar pricing.
  • Run free local models with Ollama or any OpenAI-compatible server (LM Studio, vLLM, …) when cost or data locality matters. Local models appear in the same model picker.
  • Ask a panel of models with OpenRouter Fusion. A preset can send one question to several models and use a judge model to synthesize their perspectives into one response; the picker shows the combined price and benchmark information. Fusion remains OpenRouter-only and requires an OpenRouter API key.
  • Move demanding computation to Modal. Select an on-demand cloud CPU or single-/multi-GPU environment for a chat. Kady persists and monitors the job, stages validated inputs, brings outputs atomically back into the local project, and reserves estimated compute cost against the project budget. Long jobs survive chat turns and backend restarts and remain controllable from the Compute tab.

Stay in control

  • See usage and cost as work happens. Kady records model tokens, specialist usage, and Modal compute by run and project. OpenRouter and Anthropic OAuth metered usage count toward an optional hard dollar limit; provider-managed ChatGPT, Copilot, xAI subscription and NVIDIA NIM credit usage shows token and reference-price information without consuming that cap.
  • Watch local resource use. A compact system monitor shows CPU, memory, and GPU activity while analyses are running on your computer.
  • Manage capabilities without editing configuration files. Settings lets you connect model providers, add API keys, enable or disable skills, create or customize specialists, manage Fusion presets, and change appearance. Disabling a capability does not delete it.
  • Connect your existing research tools through MCP, a plug-in standard for AI assistants. Add reference managers, GitHub, databases, and other services, test the connection in the app, and make their tools available to Kady.
  • Your work is stored in ordinary local files. Projects can be backed up, moved, inspected with other software, or archived independently of the app.

Get started in 5 minutes

You need a compatible computer and at least one model source:

  1. A computer running macOS, Linux, or Windows 10/11.
    • On Windows, install Node.js 22+ (or winget install OpenJS.NodeJS.LTS) and Git for Windows first — Kady's agent runs its shell commands through the Git Bash that Git for Windows provides. (Prefer a Linux environment? WSL works too.)
  2. One of:
    • an OpenRouter API key for broad pay-as-you-go model access,
    • an NVIDIA API key for NIM-served models billed against NVIDIA API credits,
    • a supported ChatGPT Plus/Pro, Claude Pro/Max, GitHub Copilot, or xAI subscription that you connect after launch, or
    • free local models through Ollama.

Open a terminal (on a Mac: press Cmd+Space, type "Terminal", press Enter) and run these four lines:

git clone https://github.com/K-Dense-AI/k-dense-byok.git
cd k-dense-byok
cp .env.example .env    # optional: add an OpenRouter key or other settings
./start.sh

On Windows (press Win, type "PowerShell" or "Terminal", press Enter):

git clone https://github.com/K-Dense-AI/k-dense-byok.git
cd k-dense-byok
copy .env.example .env    # optional: add an OpenRouter key or other settings
.\start.cmd

In plain terms: the first two lines download the app and step into its folder; the third creates an optional local settings file; the last starts the app. If you use a supported subscription instead of OpenRouter, connect it in Settings → Model providers once Kady opens.

The first start installs everything automatically (it takes a few minutes); then your browser opens to http://localhost:3000 — that address is your own computer, not a website. Press Ctrl+C in the terminal to stop the app. You can connect subscriptions under Model providers and add or change keys under API keys anytime — no restart needed.

That's it. Create a project, drop in your data, and ask Kady for what you want — for example: "Run a differential expression analysis on counts.csv comparing treated vs control, and plot a volcano plot."

➡️ Step-by-step details, optional API keys, and troubleshooting: Installation guide ➡️ Your first session and everyday features: Basic usage ➡️ Prefer to watch first? Tutorial videos

Tutorial videos

Recorded walkthroughs of Kady working through real research tasks, from the K-Dense YouTube channel:

VideoWhat it covers
K-Dense BYOK Workflow Demo: Write a Review ArticleUsing a guided workflow to research and draft a review article
K-Dense BYOK in Action: End-to-End Workflow DemoA full run from research question to results, start to finish
K-Dense BYOK Workflow Demo: Write a RebuttalResponding to reviewer comments with a point-by-point rebuttal
K-Dense BYOK Workflow Demo: Draft a ProtocolTurning a planned experiment into a written protocol
The Future of Research is Open: Introducing K-Dense BYOKWhat the app is, what it does, and how to get started
Literature Review and Hypothesis GenerationSearching the literature and generating grounded hypotheses
BYOK Workflow Highlights: Write a Review PaperHighlights from a guided review-paper run
Can AI Reproduce a Nature Medicine Paper?An end-to-end reproduction attempt on a published analysis

Documentation

All guides live in the docs/ folder:

GuideWhat it covers
Codebase summaryOne-page overview of what K-Dense BYOK is, what it can do, and why it matters
InstallationFull setup walkthrough, subscriptions, optional API keys, updating, troubleshooting
Basic usageFirst session, chat tabs, files, workflows, databases, costs, tips
File previewsEvery scientific format Kady can render — structures, spectra, imaging, arrays, and more
Living Lab NotebookReal-time record of Kady's work — structured entries, export, and PDF
Sub-agentsKady's team of 21 scientific specialists and how to customize them
Connecting external tools (MCP)Give Kady extra abilities like GitHub, reference managers, and databases
Local modelsRun everything on free local models (Ollama or any OpenAI-compatible server), no API keys required
Model selectionOpenRouter, Pi subscription, NVIDIA NIM, Ollama, model refs, and billing behavior
OpenRouter FusionMulti-model deliberation presets — what they are and how the integration works
ArchitectureHow the two local services fit together (for the technically curious)
Contributing workflowsAdd new workflow templates to the library
Known limitationsRough edges to be aware of in the current beta

From the K-Dense blog

Background reading on the research and evaluation work behind Kady, from the K-Dense blog:

PostWhat it covers
Introducing K-Bench 01Our internal benchmark of nine frontier models across 178 real scientific tasks, and how often confident answers are wrong
K-Dense Web vs. Claude ScienceA 20-task comparison focused on execution and auditable research output
AI Scientists Need Lab Escape Rooms, Not More ExamsWhy hidden lab environments test AI scientists better than exam-style benchmarks
Reproduction, Not Generation, Is AI's Killer App for ScienceThe case for reproducing existing research over generating novel claims, and why Kady keeps a full record of its work
AI Co-Scientists, Answered20 questions from a live session with a university research center, including current limitations
Benchmarking NVIDIA BioNeMo Agent Toolkit Skills for NIM microservicesA controlled evaluation of ten BioNeMo skills, relevant if you run NVIDIA NIM models
Benchmarking Nano Banana 2 Lite for Scientific Image GenerationA 240-image benchmark of scientific image models, speed versus figure quality
Benchmarking Google's Omni Flash for Scientific Video35 case studies on scientific video generation, strong visuals but unreliable text and physics
K-Dense Web and Litmus ScienceClosing the loop from hypothesis to experiment with lab execution

Want more?

K-Dense BYOK is great for getting started, but if you want end-to-end research workflows with managed infrastructure, team collaboration, and no setup required, check out K-Dense Web — our full platform built for professional and academic research teams.

Issues, bugs, or feature requests

If you run into a problem or have an idea for something new, please open a GitHub issue — a free GitHub account is all you need. We read every one.

About K-Dense

K-Dense BYOK is open source because K-Dense believes in giving back to the community that makes this kind of work possible.

Star History

Star History Chart

常见问题

What is k-dense-byok?

k-dense-byok is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by K-Dense-AI. An AI co-scientist running on your desktop. Claude Science but better. It has 1,031 GitHub stars.

Is k-dense-byok safe to use?

Yes. k-dense-byok passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install k-dense-byok?

Clone the repository with "git clone https://github.com/K-Dense-AI/k-dense-byok" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is k-dense-byok written in?

k-dense-byok is primarily written in TypeScript. It is open-source under K-Dense-AI on GitHub, so you can review or fork the full source.

Are there alternatives to k-dense-byok?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh k-dense-byok against similar tools.

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k-dense-byok — Claude Code AI Skill | SkillTip