byagentsope· 48 skills
Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.
$npx -y skills add agentsope/skillalchemy --skill agentsop-cost-tiered-modelsInstalls into the current project.
Run `npx skills use "https://github.com/agentsope/skillalchemy" --skill "agentsope/skillalchemy/agentsop-cost-tiered-models"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/agentsope/skillalchemy" that are relevant to the current task. Run `npx skills add "https://github.com/agentsope/skillalchemy"` and select the relevant skills, then follow their instructions.