Performance degradation and excessive token usage in long-form content generation due to 'full context injection'

`inkos` experiences severe performance degradation in long-form writing, with single-chapter generation times reaching 40 minutes. This is attributed to 'full context injection' where `spot-fix`, `Reviser`, and `Settler` phases feed entire project contexts, including the `chapter_summaries.md` file, to the LLM. This file grows excessively, leading to high token usage, slow responses, and model 'thinking failures.' The pain point is the lack of intelligent context pruning, making the system economically unviable and functionally impractical for extended projects. Market implication: Scalability in AI content generation hinges on efficient context management. Solutions must move beyond naive full-context injection to selective, summarized, or hierarchical context provision to maintain performance and cost-effectiveness as content volume increases.