AI team memory and specialist agents
rcodeskillsetup L2★0
hanzlahabib/rihal-code ↗What it does
Maintain persistent project memory, orchestrate multi-phase sprints, and track architectural decisions
Best for
Multi-week solo or small-team projects where session-to-session coherence and onboarding context are critical.
Inputs
- · Project context
- · Decision records (ADR)
- · Sprint roadmaps
Outputs
- · .rcode/memory/ markdown files
- · SPRINT.md task lists
- · Agent-readable context at session start
Requires
- · Claude Code
- · Cursor
- · Gemini
- · VS Code
- · git
Preconditions
Project initialized with /rcode-new-project; .rcode/ folder exists; decisions.md live in repo
Failure modes
- · Agent re-explains architecture each session → context not persisted
- · Late decision shifts with no audit trail → team lost on intent
- · Specialist review agents lack council state → contradictions undetected
Trust signals
- · 457 automated tests, 45 agents, 116 commands—actively dogfooded weekly
- · Decision lives in .rcode/memory/, agent reads at startup (~5K tokens auto-loaded)
- · Specialist council mode (/rcode-council) runs 5 agents in parallel with round-2 debate