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everos
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
5 skills6 commands+1+113k
@evermind-aiAgent Orchestration
Plugin
raven
One Surface, All Agents: Raven generates DAGs and orchestrates multiple specialized agents for complex tasks. Raven is the harness of harnesses, built for recursive self-improvement (RSI).
Plugin Signal84/100Strong
maintained100
capable74
documented75
adopted79
Install
$ npx -y skills add EverMind-AI/Raven --agent claude-code
In this plugin
- skills
- deck-to-pptx
- image-gen
- build-content-websites
- build-games-and-playful-experiences
- build-interactive-explainers
- build-polished-visual-frontends
- build-product-and-tool-interfaces
- build-ui-components-and-systems
- create-data-visualizations
- create-illustrations-and-scenes
- create-maps-and-spatial-plans
- create-marketing-graphics
- create-technical-diagrams
- design-brand-identities
- design-editorial-and-presentations
- design-icons-and-symbols
- design-typefaces-and-lettering
- review-against-ai-patterns
- visual-artifact-design
- raven-self-config
- subagent-dag-orchestration
- weather
- git-story-film
- agents
What's inside
Skill(23 skills)
/deck-to-pptxBuild a PowerPoint .pptx file with python-pptx, on this deployment, without the deck engine. Use when the deliverable is a .pptx on disk: presentation, deck, slides, pitch deck, keynote, report deck, 幻灯片, 演示文稿, 做一份 PPT, 出一个 pptx, 汇报材料. Carries what is specific to this deployment/image-genGenerate images via Nano Banana (Gemini 2.5/3.1 Flash Image) on OpenRouter. Use when the user asks to draw, illustrate, render or generate any kind of picture/diagram/scene./build-content-websites构建以内容实体的发布、发现、阅读、引用、修订与归档为核心的网站；适用于单篇出版物、小型静态站、出版或机构站、文档与知识库、目录与档案、CMS 内容产品及内容迁移；不用于渠道图形、组件系统本体，或以反复操作业务对象为核心的产品界面。
- /build-games-and-playful-experiences
- /build-interactive-explainers
- /build-polished-visual-frontends
- /build-product-and-tool-interfaces
- /build-ui-components-and-systems
- /create-data-visualizations
- /create-illustrations-and-scenes
- /create-maps-and-spatial-plans
- /create-marketing-graphics
- /create-technical-diagrams
- /design-brand-identities
- /design-editorial-and-presentations
- /design-icons-and-symbols
- /design-typefaces-and-lettering
- /review-against-ai-patterns
- /visual-artifact-design
- /raven-self-config
- /subagent-dag-orchestration
- /weather
- /git-story-film
Agent(11 agents)
BUILDINGThe from-zero guide to a new Raven agent: one command scaffolds a folder, the folder is discovered, and its server answers an ACP handshake. **The bar this guide holds itself to: a developer who has never read the source reaches a green handshake in ten minutes.** If any stepCODE_CONDUCT_anthropicYou are helping with software engineering tasks: fixing bugs, changing behavior, adding features, and explaining code. Use the instructions below together with the tools available to you.CODE_CONDUCT_defaultYou are helping with software engineering tasks: fixing bugs, changing behavior, adding features, and explaining code. Use the instructions below together with the tools available to you.
Badges
README
Website · Documentation · 中文
What is Raven
Raven is the harness of harnesses, built for recursive self-improvement (RSI). As a Host Agent, it brings built-in and third-party agents together to carry out complex tasks. Its modular architecture supports iterative improvement of Raven's own harness: proposing changes to how agents plan and act, evaluating those changes, and adopting improvements that pass validation. Powered by EverOS, Raven carries memory and context across sessions to support this process.
Built-in Agents: Raven-Research, Raven-Code, Raven-Design, and Raven-Oncall support research, coding, visual design, and unattended workflow automation.
Raven is pre-alpha. Interfaces and configuration may change quickly.
❯❯ Showcase
These are three complete projects delivered by Raven. In each case, Raven drove a team of specialized agents from the initial brief or objective through execution to a complete set of final deliverables.
❯ THRESHOLD: a complete game development project
The brief came from a person; Raven completed the entire project. Working autonomously for about 4 days, Raven completed 42 rounds of planning, development, and verification to build a playable first-person shooter in Godot 4, centered on an arena boss fight. The full deliverable includes the game, its poster, presentation, and website, all produced by Raven.
https://github.com/user-attachments/assets/44724e3e-6564-467a-b422-473aa8f474bd
❯ Raven RSI: recursive self-improvement in practice
AI that improves AI, with the entire project completed by Raven. Given a task and evaluation criteria it cannot modify, Raven RSI independently plans each round, writes code, runs experiments, and evaluates the results. In nanochat pre-training experiments, it completed 172 training runs across 7 rounds without a single crash, reducing val_bpb by 5.8% within the same 20-minute, single-GPU budget. The same process reduced overshoot in a dam-break simulation by three orders of magnitude and completed an FEA limit-load search in 8 rounds of bisection. The complete deliverable includes the experimental results, visualizations, poster, presentation, and project website, all produced by Raven.
❯ Raven: a complete product launch project
One raven, a whole flock of specialists. Raven completed the entire project. Its launch kit brings together a browser-based physics mini-game, a 16-slide product overview, posters in English and Chinese, and the README you are reading now, all produced by Raven.
https://github.com/user-attachments/assets/dd186b6d-3752-4c59-89c7-eeea5c6fa962
❯❯ Built-in Agents
Raven's modular architecture is designed for harness self-evolution and subagent creation. Its four built-in agents deliver state-of-the-art (SOTA) performance in their respective domains, combining reusable harness components with domain-specific tools, skills, and agent loops. Raven can delegate a focused task to a single agent or orchestrate multiple agents within a shared workflow. The harness they share is refined by the Raven Evolver, a separate tool that consumes Raven as a library and evaluates candidate harness changes against benchmarks; it develops the agents rather than running inside them.
All four agents are built in and ready for orchestration out of the box.
❯ Raven-Research
Raven-Research enables autonomous deep research for complex questions, literature reviews, and technical analysis. It delivers clear, structured reports with traceable sources, helping users understand unfamiliar domains, compare alternatives, and make informed decisions.
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raven is a Claude Code plugin with 23 hand-picked skills for agent orchestration work, indexed on Flowy. Install it with the command on its page. It includes deck-to-pptx, image-gen, build-content-websites. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
- What is raven?
- One Surface, All Agents: Raven generates DAGs and orchestrates multiple specialized agents for complex tasks. Raven is the harness of harnesses, built for recursive self-improvement (RSI).
- How do I install raven?
- Run these in Claude Code: npx -y skills add EverMind-AI/Raven --agent claude-code. Then prompt normally.
- Does raven auto-invoke its skills?
- Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
- Is raven free?
- Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.
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