Safety　monitoring　of　power　operations　in　power　stations　is　crucial　for　preventing　accidents　and　ensuring　stable　power　supply.　However,　conventional　methods　such　as　wearable　devices　and　video　surveillance　have　limitations,　such　as　high　cost,　dependence　on　light,　and　visual　blind　spots.　WiFi-based　human　pose　estimation　is　a　suitable　method　for　monitoring　power　operations　due　to　its　low　cost,　device-free,　and　robustness　to　various　illumination　conditions.　In　this　article,　a　novel　channel　state　information　(CSI)-based　pose　estimation　framework,　namely,　PowerSkel,　is　developed　to　address　these　challenges.　PowerSkel　utilizes　self-developed　CSI　sensors　to　form　a　mutual　sensing　network　and　constructs　a　CSI　acquisition　scheme　specialized　for　power　scenarios.　It　significantly　reduces　the　deployment　cost　and　complexity　compared　to　the　existing　solutions.　To　reduce　interference　with　CSI　in　the　electricity　scenario,　a　sparse　adaptive　filtering　algorithm　is　designed　to　preprocess　the　CSI.　CKDformer,　a　knowledge　distillation　network　based　on　collaborative　learning　and　self-attention,　is　proposed　to　extract　the　features　from　CSI　and　establish　the　mapping　relationship　between　CSI　and　keypoints.　The　experiments　are　conducted　in　a　real-world　power　station,　and　the　results　show　that　the　PowerSkel　achieves　high　performance　with　a　PCK@50　of　96.27%　and　realizes　a　significant　visualization　on　pose　estimation,　even　in　dark　environments.　Our　work　provides　a　novel　low-cost　and　high-precision　pose　estimation　solution　for　power　operation.