Software Development & Delivery · Public

world-model-optimizer

World Model Optimizer

Best fitUsers who want source-backed project understanding before installing it.

Check whether this project matches your task before installing it.

What it can doskill, recipe, host_instruction, eval, preflight

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshotstars unavailable

forks unavailable · contributors unavailable

Doramagic.ai Last verification date: 2026-07-27 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-07-27

What is world-model-optimizer?

World Model Optimizer
Best fit: Users who want source-backed project understanding before installing it.
Not for: Not for users who want to skip sandbox verification or cannot accept configuration, permission, or maintenance overhead.
Capability added to an AI workflow: skill, recipe, host_instruction, eval, preflight
First safe verification step: Verify the smallest path in an isolated environment and keep a rollback path.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/experientiallabs/world-model-optimizer, https://news.ycombinator.com/item?id=49063454, https://github.com/experientiallabs/world-model-optimizer#readme, Human Manual

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forUsers who want source-backed project understanding before installing it.

Match the project to your task before installing it.

Capabilityskill, recipe, host_instruction, eval, preflight

World Model Optimizer

Repositoryexperientiallabs/world-model-optimizer

stars unavailable · forks unavailable

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Introduction and Getting Started

Related topics: System Architecture and Core Modules

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

System Architecture and Core Modules

Related topics: Introduction and Getting Started, World Models, Optimization, and Distillation

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

World Models, Optimization, and Distillation

Related topics: System Architecture and Core Modules, Data Ingestion, Providers, and Platform Integrations

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

Data Ingestion, Providers, and Platform Integrations

Related topics: System Architecture and Core Modules, World Models, Optimization, and Distillation

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/experientiallabs/world-model-optimizer, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Starsstars unavailable

Forksforks unavailable

Contributorscontributors unavailable

Licenseunknown

Community Discussion Evidence

3 source-linked items

Review these external discussions before using world-model-optimizer with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
world-model-optimizer v0.2.0
github / github_release
02
world-model-harness v0.1.0
github / github_release
03
Capability evidence risk requires verification
GitHub / issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install world-model-optimizer

Official start command · https://github.com/experientiallabs/world-model-optimizer#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

world-model-optimizer Manual

world-model-optimizer is a Python toolkit for working with world models in machine learning workflows. It exposes a single importable package (wmo) and a console entry point (wmo) that tog...

Open the full manual

https://github.com/experientiallabs/world-model-optimizer Project Manual
Table of Contents
Introduction and Getting Started
Related Pages
Overview
Installation
Quick Start
Migration from `world-model-harness`

Introduction and Getting Started

Related topics: System Architecture and Core Modules

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

System Architecture and Core Modules

Related topics: Introduction and Getting Started, World Models, Optimization, and Distillation

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

World Models, Optimization, and Distillation

Related topics: System Architecture and Core Modules, Data Ingestion, Providers, and Platform Integrations

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

Data Ingestion, Providers, and Platform Integrations

Related topics: System Architecture and Core Modules, World Models, Optimization, and Distillation

Source: https://github.com/experientiallabs/world-model-optimizer / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

medium

Capability evidence risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

low

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.

low

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.