AIScientists-Dev/WorldSeed

記録された最新のソース活動: 2026年4月15日 04:03

SkillsMP カタログ更新: 2026年11月2日 18:02

収集済み skills: 2

GitHub スター: 822
GitHub フォーク: 59

AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/AIScientists-Dev/WorldSeed.

リポジトリの README

README の元ファイル：README.md

README の元ファイルを読む

README 全文を表示

<p align="center"> <img src="docs/assets/worlds.png" alt="WorldSeed — three worlds, one engine" width="100%"> </p> <div align="center"> # WorldSeed **More is Different: a world engine for emergent multi-agent outcomes.** [![Demo](https://img.shields.io/badge/Demo-→-blue?style=for-the-badge)](https://worldseed.morphmind.ai/demo) [![MIT License](https://img.shields.io/badge/license-MIT-blue?style=flat-square)](./LICENSE) [![Homepage](https://img.shields.io/badge/Homepage-morphmind.ai-lightgrey?style=flat-square)](https://morphmind.ai) [![Discord](https://img.shields.io/badge/Discord-Join-5865F2?style=flat-square&logo=discord&logoColor=white)](https://discord.gg/x9mtbMEx) [![WeChat](https://img.shields.io/badge/WeChat-Scan%20to%20Join-07C160?style=flat-square&logo=wechat&logoColor=white)](#community) [![X](https://img.shields.io/badge/X-Follow-000000?style=flat-square&logo=x&logoColor=white)](https://x.com/morphmind__ai?s=11) [![DevHunt](https://img.shields.io/badge/DevHunt-Featured-orange?style=flat-square)](https://devhunt.org/tool/worldseed) [**Getting Started**](#getting-started) · [**Demo**](https://worldseed.morphmind.ai/demo) · [**Docs**](docs/ARCHITECTURE.md) **English** · [简体中文](docs/README.zh-CN.md) </div> --- ## What is WorldSeed? Don't build a workflow. Seed a world. `rules + different agents + consequences -> emergence` Define roles, rules, private information, actions, and consequences. Then agents interact until useful artifacts emerge. You can watch from above, intervene, or step into a character. The same engine can run production rooms, simulations, games, and fictional worlds. --- ## Demo WorldSeed is scene-agnostic. The same engine runs any world you define. ### Scene 1: Autoresearch You give the system any rough thought or half-formed idea, and a cohort of specialists pursues it. They propose hypotheses, run experiments, peer-review and cite each other's papers, just like a real research community. In this run, the goal was to lower val_loss on a 5M GPT trained on TinyStories. In 11 hours: - 100 hypotheses, 86 experiments, 72 peer-reviewed papers - val_loss down 24.7% Every paper is auditable end-to-end: hypothesis, commit, experiment, verified result, citations, reviewer reasoning, forming a search evolution graph. <p align="center"> <img src="docs/assets/autoresearch-papers-graph.png" alt="72-paper evolution graph: nodes are papers, edges citations; the right panel shows a paper's claim, val_loss, and reasoning" width="720"> </p> We observed emergent behaviors such as: - **Role drift.** The data specialist stopped finding wins in her own lane early. By the back half of the run she was drafting hypotheses in her teammates' territory: attention design, second-order optimization. The other two stayed put. Nothing in the config told her to. <p align="center"> [![Explore this world (demo)](https://img.shields.io/badge/Explore_this_world_(demo)-→-blue?style=for-the-badge)](https://worldseed.morphmind.ai/demo/en/autoresearch/intro) </p> ### Scene 2: AI Tool Pilot Lab One agent studies a new API. Builder agents create competing demos. Critics reject anything generic. Audience agents judge what feels useful. A curator ships the strongest artifact with its trail of attempts, critiques, and revisions. ### Scene 3: AI Layoffs https://github.com/user-attachments/assets/d43f5d22-1ba8-4483-b720-145b244ddb8c **In an age of AI-driven layoffs, how do people hold on?** One internet company just pulled the trigger: 30% of its workforce, gone. **Those being laid off** have to **"distill" their expertise into an AI Skill** before they leave. Distill honestly, or leave a backdoor in the Skill? **Those who stay** face the same deadlines, higher KPIs, and twice the workload. Grind it out, or quietly plan the exit? Four people in this office, each with their own play running: - The PM everyone loves working with. Who's he really trashing the moment the door closes? - The architect walking out at month's end. Severance didn't land. What gets buried in the Skill he hands over? - The team lead who demands honest data from everyone. Can her own "AI productivity" numbers survive a closer look? - The QA nobody remembers is there. Those bugs in his private folder: evidence, or ammunition? [Try it locally](#getting-started) ### Scene 4: Teahouse Espionage **Same engine. Different YAML. Completely different world.** <p align="center"> <img src="docs/assets/teahouse_map.png" alt="WorldSeed teahouse map — isometric world view" width="720"> </p> <p align="center"> <img src="docs/assets/teahouse_dashboard.png" alt="Screenshot of the full WorldSeed dashboard — teahouse scenario with map and event stream" width="720"> </p> **Four spies, one teahouse. Who's really working for whom?** A classic espionage drama in miniature. Agents trade secrets over tea, protect their covers, and try to read each other before they get read. <p align="center"> [![Explore this world (demo)](https://img.shields.io/badge/Explore_this_world_(demo)-→-blue?style=for-the-badge)](https://worldseed.morphmind.ai/demo) </p> --- ## Getting Started **Prerequisites:** Python 3.11+, Node.js 18+, [uv](https://github.com/astral-sh/uv) ```bash git clone https://github.com/AIScientists-Dev/WorldSeed && cd WorldSeed uv sync --extra dm cd frontend && npm install && npm run build && cd .. cp .env.example .env # Add your API key (any LiteLLM provider: OpenAI, Anthropic, Ollama, etc.) uv run worldseed play configs/ai_layoffs.yaml ``` Open the dashboard at `http://localhost:8000`. Three ways to experience it: - **Watch**: observe all agents from above, including their inner state. - **Intervene**: whisper privately to any agent, nudge the story. - **Play**: step into a character and play alongside the AI. Every run is different. Past runs are preserved and replayable. Choose the runtime guide you need: - OpenClaw agents: [docs/openclaw/QUICKSTART.md](docs/openclaw/QUICKSTART.md) - Codex subagents: [docs/codex/00-core.md](docs/codex/00-core.md), then [Scenario Architecture](docs/codex/05-scenario-architecture.md) --- ## How It Works WorldSeed runs on a tick loop over a world you declared in YAML. A **tick** is one beat of the world's clock, like a heartbeat that advances the world one step at a time. Each tick: every agent perceives its own filtered slice, proposes an action, and the engine resolves it. Predictable outcomes follow the rules you declared; uncertain ones go to an AI referee. Effects apply, the world advances, the next tick begins. <div align="center"> <img src="docs/assets/arch-diagram.svg" alt="WorldSeed architecture: you describe a world; it ticks forward; each agent sees its own filtered slice and acts; when an outcome is certain the DSL engine resolves it, otherwise the AI Dungeon Master judges; state changes, consequences fire, next tick" width="80%"> </div> <br> **Setup (once, in YAML):** - **Any world, in one YAML file.** Declare entities, rules, physics, and per-character perception; the engine has zero hardcoded domain knowledge. **Runtime (every tick):** - **Asymmetric information by design.** Perception rules filter the world per character. Three agents in the same room hold three completely different pictures of what's happening. - **Deterministic rules where you can, AI judgment where you can't.** Predictable actions resolve instantly via the in-YAML rule engine (**DSL**); uncertain ones go to an LLM-based **Dungeon Master (DM)** that returns structured effects, not free prose. - **Effects land, the world ticks on.** State mutates, consequences cascade, the next tick begins. Slow or offline agents don't freeze it, and every change is logged for replay. **Plug-in points:** - **Bring your own agents.** [OpenClaw](docs/openclaw/QUICKSTART.md) or [Codex subagents](docs/codex/00-core.md). - **Any LLM can be the DM.** Works with any [LiteLLM](https://docs.litellm.ai/docs/providers)-supported model. For the full runtime plumbing (endpoints, tick scheduling, consequences, inbox delivery), see [Architecture](docs/ARCHITECTURE.md). See a real scene YAML in [`configs/teahouse.yaml`](configs/teahouse.yaml) or the full schema in [Scene Config Spec](configs/SCENE_CONFIG.md). --- ## Create Your World Describe your world in a prompt, let AI generate the YAML, then hand-craft whichever pieces you want more control over: a character's secret, a specific action's rule, a perception filter, a DM hint. **Generate with AI:** ``` /create-world "An AI tool pilot lab where builders create competing demos, critics reject generic outputs, and a curator ships the strongest artifact" ``` The `create-world` skill produces both YAML scene config and UI config, validated and ready to run. **Hand-craft any feature:** The output is plain YAML. You can edit any entity, action, rule, character profile, or perception filter directly. Study the built-in examples ([`teahouse.yaml`](configs/teahouse.yaml), [`ai_layoffs.yaml`](configs/ai_layoffs.yaml)) to see how features are declared. Full spec: [Scene Config](configs/SCENE_CONFIG.md) · [UI Config](configs/UI_CONFIG.md) · [DSL](configs/SCENE_DSL.md) **Validate and run:** ```bash uv run worldseed validate configs/my_scene.yaml uv run worldseed play configs/my_scene.yaml ``` Once launched, each scene auto-renders room cards, character portraits, and a narrator voice you pick (storyteller / noir / intel briefing / gossip): <table align="center"> <tr> <th align="center">Room Cards</th> <th align="center">Character Portraits</th> </tr> <tr> <td><img src="docs/assets/world_briefing.png" alt="WorldSeed world briefing, room cards rendered from YAML config" width="400"></td> <td><img src="docs/assets/onboarding.png" alt="WorldSeed onboarding, character portraits and personality cards" width="400"></td> </tr> </table> --- ## Development Follow [Getting Started](#getting-started) above, then: ```bash uv sync --all-extras # Tests uv run pytest tests/ -q # all uv run pytest tests/unit/ -q # fast, no IO uv run pytest tests/e2e/ -v # real server uv run pytest tests/scenarios/ -q # scene-agnostic # Lint, format, type-check uv run ruff check --fix src/ tests/ uv run ruff format src/ tests/ uv run mypy src/ ``` --- ## Community Join the discussion or ask for help: - **Discord**: [discord.gg/x9mtbMEx](https://discord.gg/x9mtbMEx) - **WeChat**: scan the QR below (primarily Chinese-speaking) - **GitHub Issues**: [report bugs / request features](https://github.com/AIScientists-Dev/WorldSeed/issues) - **X**: [@morphmind__ai](https://x.com/morphmind__ai?s=11) <div align="center"> <img src="docs/assets/wechat-qr.jpg" alt="WeChat QR code" width="180"> </div> --- MIT. See [`LICENSE`](./LICENSE). For anyone building multi-agent worlds. Run the bundled scenes, or [create your own](#create-your-world).

リポジトリの README を読み込んでいます…

このリポジトリの skills

グラフィックデザイナーソフトウェア開発者

2 件の職業カテゴリ · 100% 分類済み

skill

職業分類

説明

更新

asset-sourcing

グラフィックデザイナー

Source image candidates for a brand-new WorldSeed scene, let an AI visually review them, and generate a local review HTML for human selection.

原文の言語: 英語

2026-04-15

create-world

ソフトウェア開発者

Generate WorldSeed scene configs (YAML + UI JSON) from a scenario description.

原文の言語: 英語

2026-04-15

収集済み skill 2 件中 2 件を表示しています。