Automate world-model reasoning and planning for AI agents across web, code, and CLI environments.
npx skills add https://github.com/formthefog/stratus-skills --skill stratus
Please help me install this Agent Skill. Skill: stratus Source: https://github.com/formthefog/stratus-skills/tree/main/claude/stratus Command: npx skills add https://github.com/formthefog/stratus-skills --skill stratus
This Skill enables AI agents to reason, plan, test, and integrate actions using a Stratus world-model, reducing reliance on pure LLM prompting and improving reliability in long-horizon tasks.
High-intent search queries and answers about installing and using this skill.
Multi-step task planning for AI agents is automated by applying world-model reasoning to predict state transitions and generate action sequences before execution. This approach reduces reliance on pure LLM prompting and improves reliability in long-horizon tasks.
A world-model approach for agent planning predicts outcomes of actions to generate reliable sequences, then validates those plans through integration testing. It shifts reliance away from pure LLM prompting toward structured state transition reasoning.
Yes, you need Python tooling to access Stratus rollout and chat endpoints. You must configure environment variables STRATUS_API_URL and STRATUS_API_KEY, along with a compatible model name such as stratus-x1ac-small-claude-sonnet-4-5.
Yes, world-model planning supports integration tests across web, code, and CLI environments. It validates generated action sequences and guides integration into real-world workflows, enabling debugging and agent orchestration.
The best way to validate AI agent plans is using a rollout-based planning endpoint to generate action sequences, then executing via chat completions with the Stratus model. You can validate outcomes and re-plan if necessary.
Pure LLM prompting fails for long-horizon agent tasks because it lacks structured state transition reasoning. Applying a world-model generates reliable action sequences and verifies outcomes, reducing errors in complex multi-step workflows.
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