autonomous-development

Autonomous development automates the complete lifecycle by decomposing requirements into milestones, implementing features incrementally, auto-debugging issues through pattern-based fixes, and running continuous quality assurance. This minimizes manual intervention while ensuring production-ready code meets all functional and quality standards.

Milestone planning strategies decompose complex requirements into manageable phases, assess task complexity, and sequence work for efficient development. The commit-per-milestone approach ensures each phase produces a working state with clear, auditable progression toward the final deliverable.

Incremental development guides implementation through working state commits and progressive enhancement, ensuring continuous functionality. Combined with auto-debugging and comprehensive quality scoring, this approach catches and fixes issues early, preventing cascading defects in later phases.

Auto-debugging strategies classify errors and apply iterative, pattern-based fixes including connection retries, type conversions, and null safety checks. This reduces manual debugging cycles while maintaining type safety and dependency-aware parameter consistency across implementation phases.

You need clear requirements that can be decomposed into features, a version control system for auditable commit workflows, and a test environment for continuous quality validation. The skill handles planning, implementation, debugging, and quality assurance once these inputs are in place.

Yes, autonomous development targets complex multi-phase projects with dependency-aware planning and parameter consistency enforcement. It handles feature decomposition, cross-phase validation, and auditable workflows to manage large codebases with multiple interconnected components.