Plutonic Rainbows

I have completed the first major phase of refactoring my python blog generator, transforming it from a 798-line monolithic script into a modern, modular, and fully backward-compatible system. I introduced clear domain models (Post, Site, Page), a dedicated service layer, repository abstractions, and a Pydantic-validated configuration system, backed by 53 passing unit tests. The build process is now faster, with smart filtering and optimized I/O, while the new CLI offers rich options, structured logging, and robust error handling. This layered architecture, built on solid principles, dramatically improves maintainability, testability, and extensibility, enabling easier feature development, better debugging, and team scalability — without disrupting the existing workflow.

Making progress with refactoring the blog.

Found a lovely picture of Gail Elliott for Escada, 1989.

Preparing to use Claude Code so that I can refactor the blog. Some planning involved.

Planning complete. Work started.

I have successfully integrated comprehensive subagent best practices into the Claude Prompt Builder system based on the official Claude Code documentation. The implementation focused on transforming the existing agent recommendation system (v3.6.0) into a sophisticated subagent orchestration framework that follows the key principles of specialized AI assistants with focused responsibilities, restricted tool access, and intelligent delegation patterns. I enhanced the role templates in anthropic_techniques.py to include "PROACTIVELY" language and specific tool restrictions for each agent type, ensuring that security-engineers only have Read/Grep/Edit access for analysis, while implementation agents like python-engineer and frontend-engineer have appropriate modification tools but are restricted to their domain-specific file types.

The core enhancement involved creating a complete subagent orchestration system that automatically detects task complexity and generates detailed delegation plans with primary coordinators, sequential and parallel agent assignments, and clear completion criteria. I updated the pre-execution instructions in enhanced_prompt_builder.py to include comprehensive subagent delegation strategies, modified the prompt assembly process to include orchestration plans in generated outputs, and integrated complexity-based delegation decisions into the enhancement_intelligence.py system. The implementation includes intelligent pattern detection for security and testing requirements, adaptive threshold management based on user feedback, and seamless integration with the existing v3.6.0 architecture. All changes have been tested and verified to work correctly, with the PM2 service successfully restarted to deploy the enhancements to the production environment.