This skill implements an advanced orchestration framework designed to solve the context-loss problem inherent in long-term AI development tasks. Based on Anthropic's research into effective agent harnesses, it provides a structured system for initializing projects with granular feature lists, tracking progress through dedicated markdown logs, and managing session handoffs. By breaking complex tasks into small, verifiable units and maintaining persistent state files across multiple interactions, it prevents common AI failures like 'one-shotting' or premature project completion, ensuring consistent incremental progress for large-scale software engineering.
主要功能
01Granular feature tracking supporting exhaustive lists of 30-200 individual tasks
02Automated project initialization with features.json and progress.md scaffolding
03Project status visualization to identify completed, in-progress, and remaining tasks
040 GitHub stars
05Multi-session context preservation using structured handoff logs and git history
06Environment setup automation via specialized init.sh scripts with smoke tests
使用场景
01Developing feature-heavy APIs or CLI tools through systematic, multi-session implementation cycles
02Managing large-scale refactoring projects where context must be preserved across incremental steps
03Building complex full-stack applications that require multiple days or weeks of development