Intelligence Route
This is a multi-tier routing system that picks the right model complexity for your task before you waste tokens on Opus for something Haiku could handle. It queries past patterns, predicts outcomes, then spawns an agent at the appropriate tier: sub-millisecond WASM transforms for simple stuff like converting var to const, Haiku for straightforward fixes, Sonnet/Opus for architecture work. The interesting part is the feedback loop. You're supposed to report success or failure after each task so the router actually learns. Pass the --why flag and it explains its reasoning via hooks_explain. If you're burning through API costs on overpowered models or your tasks keep timing out because you picked Haiku for refactoring, this addresses that.
- skills.sh installs
- 743
- GitHub stars
- 81.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add ruvnet/ruflo --skill intelligence-route --agent claude-codeInstalls into .claude/skills of the current project.
Files
Intelligence Routing
Pick the optimal agent + model tier for a task using learned patterns + the 3-tier router. Emits a hooks_explain rationale so the choice is auditable.
When to use
Before starting any non-trivial task. Replaces manual agent selection with data-driven decisions.
Steps
Sponsors
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- Repository
- github.com/ruvnet/ruflo
- Categories
- Frontend Development, Documentation
- First seen
- Jun 3, 2026
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