Invoking skill packages as subagents improves reasoning for long‑horizon tasks despite extra token overhead.
Researchers compared two execution methods for language‑model agents: loading skill instructions into the main context versus spawning subagents with fresh contexts. Experiments showed subagent execution outperforms the traditional skill‑loading approach when skills have clear input‑output contracts, though it incurs additional communication tokens.
Implement subagent spawning to isolate skill execution and avoid context bloat.
When building an LLM‑driven workflow that chains many skills, replace in‑context skill loading with subagent calls to maintain high reasoning quality.
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