Automated LLM workflow design often relies on expensive per-task search, which is computationally expensive and does not explicitly reuse structural knowledge across tasks. We propose SWIFT (Synthesizing Workflows via Few-shot Transfer), which reuses prior search trajectories and demonstrations to synthesize target-task workflows without iterative target-task optimization. SWIFT distills compositional heuristics and output-interface contracts from source-task trajectories, then conditions a single LLM generation pass on these priors and cross-task workflow demonstrations to produce an executable workflow. Across 5 benchmarks, SWIFT outperforms the SOTA search-based baseline while reducing marginal per-task optimization cost by three orders of magnitude given an existing source bank. It also improves performance on 4 additional held-out benchmarks and transfers from GPT-4o-mini to Grok, Qwen, and Gemma execution backbones. These results suggest that prior workflow search can expose reusable structural priors that reduce optimization cost while preserving or improving accuracy on related tasks.