Large language models (LLMs)-based code generation for robotic manipulation has recently shown promise by directly translating human instructions into executable code, but existing methods remain noisy, constrained by fixed primitives and limited context windows, and struggle with long-horizon tasks. While closed-loop feedback has been explored, corrected knowledge is often stored in improper formats, restricting generalization and causing catastrophic forgetting, which highlights the need for learning reusable skills. Moreover, approaches that rely solely on LLM guidance frequently fail in extremely long-horizon scenarios due to LLMs' limited reasoning capability in the robotic domain, where such issues are often straightforward for humans to identify. To address these challenges, we propose a human-in-the-loop framework that encodes corrections into reusable skills, supported by external memory and Retrieval-Augmented Generation with a hint mechanism for dynamic reuse. Experiments on Ravens, Franka Kitchen, and MetaWorld, as well as real-world settings, show that our framework achieves a 0.93 success rate (up to 27% higher than baselines) and a 42% efficiency improvement in correction rounds. It can robustly solve extremely long-horizon tasks such as "build a house", which requires planning over 20 primitives.