Traditional language-conditioned manipulation agent adaptation to new skills leads to catastrophic forgetting of old skills, limiting more practical dynamic scene deployment. In this paper, we propose SkillsCrafter, a novel robotic manipulation model designed to continually learn multiple manipulation skills while reducing catastrophic forgetting of old skills. Specifically, to achieve lifelong learning of new skills, we propose a Manipulation Skills Adaptation to achieve retaining the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills. Meanwhile, we perform the singular value decomposition on the diverse manipulation skills instructions to obtain a common skill semantic subspace projection matrices to record skills essential semantic space. To achieve forget-less and generalization manipulation, we propose a Skills Specialization Aggregation to compute inter-skills similarity in skill semantic subspaces to achieve aggregation of the previously learned skill knowledge for any new or unknown skill manipulation. Extensive simulator experiments and real-world deployments demonstrate the effectiveness and superiority of the proposed SkillsCrafter.