For　data-driven　dynamic　stability　assessment　(DSA)　in　modern　power　grids,　DSA　models　generally　have　to　be　learned　from　scratch　when　faced　with　new　grids,　resulting　in　high　offline　computational　costs.　To　tackle　this　undesirable　yet　often　overlooked　problem,　this　work　develops　a　light-weight　framework　for　DSA-oriented　stability　knowledge　transfer　from　off-the-shelf　test　systems　to　practical　power　grids.　A　scale-free　system　feature　learner　is　proposed　to　characterize　system-wide　features　of　various　systems　in　a　unified　manner.　Given　a　real-world　power　grid　for　DSA,　selective　stability　knowledge　transfer　is　intelligently　carried　out　by　comparing　system　similarities　between　it　and　the　available　test　systems.　Afterward,　DSA　model　fine-tuning　is　performed　to　make　the　transferred　knowledge　adapt　well　to　practical　DSA　contexts.　Numerical　test　results　on　a　realistic　system,　i.e.,　the　provincial　GD　Power　Grid　in　China,　verify　the　effectiveness　of　the　proposed　framework.