Detecting completeness in natural language (NL) requirements traditionally involves domain modeling to establish reference models, which requires substantial manual effort and proves to be time- and labor-intensive. Large Language Models (LLMs), with an extensive knowledge base and advanced capabilities for contextual understanding and reasoning, present an opportunity for automated completeness detection in NL requirements. To better complete this task, we first propose the CMD model for implied completeness, which formally characterizes the essential nature of external completeness in NL requirements. We further establish formal definitions for requirements compositions and incompleteness dimensions, providing a qualitative analytical foundation for completeness detection. We then present ReCompGPT (Requirements Completing GPT), an LLM-powered method for detecting and repairing requirements completeness. Grounded in the proposed definitions, we construct a fine-grained chain-of-thought augmented by an action diffusion approach that provides the model with multi-perspective insights into requirements. It eliminates the need for a reference model, verifying the completeness of requirements specifications from natural language to natural language (NL2NL). Simultaneously, we develop a new dataset dedicated to detecting the external completeness in NL requirements, on which our method achieves an overall detection success rate of 93.8%, significantly enhancing performance relative to the vanilla prompts and the vanilla chain-of-thought, respectively. Overall, ReCompGPT demonstrates superior capabilities in both simple scenarios and complex cases characterized by implied requirements incompleteness. Our method proves to be effective and flexible.