Use when adding or changing persisted user state — settings defaults, audio, camera/orbit, panel positions, feature flags, and the in-app "Save Defaults" pipeline that snapshots localStorage into tinyworld-defaults.json. Also covers the inline-script regex gotcha that has burned us twice.
Use when adding or changing GLSL effects in Tiny World Builder — landscape water, waterfalls, foam, smoke, explosions, damage/wear overlays, or the reusable TinyShaderFX library. Covers where shaders live, the override relationship between LandscapeEngine.js and engine/landscape/.js, and the procedural-noise toolkit.
Use when editing the Tiny World Builder repo, especially tiny-world-builder.html, to preserve the static classic-script Three.js r185 app structure and local edit/reload workflow.
Use when adding or changing Tiny World Builder tile/object repeat-click behavior, terrain stacking, floors/intensity, fences, rocks, walls, crops, or Monument Valley-like height/detail growth.
Spec-driven development methodology for Claude Code. Provides skills for requirements engineering (EARS format), design documentation, task breakdown, AI prompting strategies, quality assurance, and troubleshooting.
Effective communication strategies for AI-assisted development. Learn context-first prompting, phased interactions, iterative refinement, and validation techniques to get better results from Claude and other AI coding assistants.
Create comprehensive steering documents for development projects. Generates project-specific standards, git workflows, and technology guidelines in .kiro/steering/ directory.
Transform approved requirements into comprehensive technical designs. Define system architecture, component interactions, data models, and interfaces to create a blueprint for implementation.
Transform vague feature ideas into clear, testable requirements using EARS format. Capture user stories, define acceptance criteria, identify edge cases, and validate completeness before moving to design.
Systematic three-phase approach to feature development using Requirements, Design, and Tasks phases. Transforms vague feature ideas into well-defined, implementable solutions that reduce ambiguity, improve quality, and enable effective AI collaboration.
Convert technical designs into actionable, sequenced implementation tasks. Create clear coding tasks that enable incremental progress, respect dependencies, and provide a roadmap for systematic feature development.
Diagnose and resolve common issues during spec-driven development and implementation. Learn strategies for handling spec-reality divergence, dependency blocks, unclear requirements, and other execution challenges.