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From ruflo-intelligence
Intelligence dashboard -- stats, metrics, model routing, routing rationale on demand
How this command is triggered — by the user, by Claude, or both
Slash command
/ruflo-intelligence:intelligenceThe summary Claude sees in its command listing — used to decide when to auto-load this command
Show the intelligence system dashboard: 1. Call `mcp__claude-flow__hooks_intelligence_stats` to get pattern counts, trajectory history, and SONA learning state. 2. Call `mcp__claude-flow__hooks_metrics` to get the metrics dashboard (trajectory throughput, learn-cycle latency, pattern delta). 3. Call `mcp__claude-flow__hooks_model-stats` for 3-tier model routing distribution (Tier 1 booster / Tier 2 Haiku / Tier 3 Sonnet/Opus). 4. Call `mcp__claude-flow__neural_status` for neural pattern + SONA / MoE state. 5. **(Optional `--why <task>`)** — Call `mcp__claude-flow__hooks_explain` with the t...
Show the intelligence system dashboard:
mcp__claude-flow__hooks_intelligence_stats to get pattern counts, trajectory history, and SONA learning state.mcp__claude-flow__hooks_metrics to get the metrics dashboard (trajectory throughput, learn-cycle latency, pattern delta).mcp__claude-flow__hooks_model-stats for 3-tier model routing distribution (Tier 1 booster / Tier 2 Haiku / Tier 3 Sonnet/Opus).mcp__claude-flow__neural_status for neural pattern + SONA / MoE state.--why <task>) — Call mcp__claude-flow__hooks_explain with the task to get the routing rationale.Present a summary table:
--why given)If patterns are stale, suggest:
mcp tool call hooks_pretrain --json
mcp tool call agentdb_consolidate --json
5plugins reuse this command
First indexed May 13, 2026
npx claudepluginhub zekiog/ruflo --plugin ruflo-intelligence/intelligenceProvides AI-powered predictive analytics for Jira: predicts issue complexity/risk/estimates, prioritizes backlogs via WSJF, analyzes velocity. Specify operation and target.