SplitBrain.AI

Monitor CI conservatively with snapshot-first status checks and actionable failure triage. Use when tracking pipeline runs, investigating build failures, or validating PR readiness.

Run a full deep-dive, evidence-first codebase audit and convert findings into prioritized sprint plans with implementation-ready task documents. Use when you need comprehensive architecture, quality, risk, and delivery planning from current code and docs.

Run a SplitBrain.AI LLM council review for high-impact decisions. Use when evaluating architecture tradeoffs, orchestration design, schema changes, retrieval/search behavior, agent governance, or long-term infrastructure decisions.

Submit a pull request, request review, triage comments, fix issues, and use bounded wait checks before merge and cleanup. Use when implementation is complete and the branch is ready for PR handling end to end.

Audit and reconcile drift between agent/prompt docs and actual runtime capabilities in SplitBrain.AI services, controllers, and MCP endpoints.

Run an agent self-review for skill and prompt improvements. Use periodically to audit skill design, token-cost hotspots, and workflow upgrades across the SplitBrain.AI customizations.

Shared core workflow for SplitBrain.AI task execution: scope, evidence, minimal-change implementation, validation, and tracking updates.

Run a task-backed implementation loop from active task selection through validation evidence and task status updates. Use when delivery should be anchored to Data/Tasks records.

Run a repeatable training sprint loop: implement, validate, commit, push, and CI check with clear evidence and scoped commits. Use for short delivery rounds where each round ends in a pushed checkpoint.

Run a repeatable hygiene audit over Docs/Memories and Docs/ for drift, stale facts, broken source links, and cleanup candidates. Use for dogfooding and knowledge quality maintenance.