TheMasonX

Monitor CI conservatively with snapshot-first status checks and actionable failure triage.

Submit a pull request, request review, triage comments, fix issues, and use script-driven review waits with a bounded timeout before merge/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 MemorySmith services/controllers.

Run a repeatable hygiene audit over Data/Memories and Data/Pages for drift, stale facts, broken source links, and cleanup candidates.

Run a MemorySmith LLM council review for high-impact decisions. Use when evaluating schema changes, retrieval/search behavior, chat or agent write governance, wiki conventions, or long-term architecture tradeoffs with explicit dissent, risks, confidence, and acceptance criteria.

Run an agent self-review for skill and prompt improvements, including script extraction opportunities and token-conscious workflow upgrades.

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

Run a /tasks-first implementation loop from active task selection through validation evidence and task status/comment updates.

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 an LLM council review with 6 diverse subagent personalities. Use for architecture review, implementation plan critique, risk assessment, or quality audit. Produces a structured council report with ratings, MUST-FIX items, and a consolidated verdict.

Debug MemorySmith.Agent by querying rolling log files — agent, adapter, chat, or LLM context — with time windows, level filters, app-instance isolation, and tail mode.