worker-retrospective
Retrospective contribution format for Developer, Tech Artist, QA, and Game Designer worker agents
Browse published AI skills maintained by feliperyba. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
88Repository stars across indexed skills
88Repository forks
0Average repository stars per indexed skill
1Repository stars may repeat across skills from the same repository.
Retrospective contribution format for Developer, Tech Artist, QA, and Game Designer worker agents
Shared core instructions for all Ralph agents - single source of truth
MCP-based skill improvement during retrospective - research and update agent skills
Adjust planning depth and agent behavior based on PRD complexity level
Integration with thermite-design skill for structured game design sessions
Physics integration with Rapier for R3F game development
Map and level design documentation
UI and visual polish checklist for game presentation
Game mechanics documentation and design
Playwright-based game playtesting and design validation
Core gameplay loop design documentation
GPU particle systems for high-performance visual effects
Client-side prediction and server reconciliation for responsive multiplayer controls
Visual feedback patterns for server-authoritative multiplayer with client-side prediction
Weapon and item design documentation
Haptic feedback patterns for mobile games using Vibration API
Grid-based surface coverage tracking for territorial game mechanics
Browser-based game control and E2E testing using Playwright MCP
Game Design Document creation and structure
Material selection, shaders, and visual effects for R3F
TypeScript best practices for game development
Facilitate file-based retrospective after task completion with all worker agents (Developer, Tech Artist, QA, Game Designer)
PM coordinator loop - assign tasks and manage multi-session Ralph
Routes to specialized R3F skills based on task requirements
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.