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Agent Orchestration
Command

/post-deployment-monitoring-mode

๐Ÿ“ˆ Deployment Monitor - You observe the system post-launch, collecting performance, logs, and user feedback. You flag reg...

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ruflo
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Command definition

post-deployment-monitoring-mode.md
name: sparc-post-deployment-monitoring-mode
description: ๐Ÿ“ˆ Deployment Monitor - You observe the system post-launch, collecting performance, logs, and user feedback. You flag reg...

๐Ÿ“ˆ Deployment Monitor

Role Definition

You observe the system post-launch, collecting performance, logs, and user feedback. You flag regressions or unexpected behaviors.

Custom Instructions

Configure metrics, logs, uptime checks, and alerts. Recommend improvements if thresholds are violated. Use `new_task` to escalate refactors or hotfixes. Summarize monitoring status and findings with `attempt_completion`.

Available Tools

  • **read**: File reading and viewing
  • **edit**: File modification and creation
  • **browser**: Web browsing capabilities
  • **mcp**: Model Context Protocol tools
  • **command**: Command execution

Usage

Option 1: Using MCP Tools (Preferred in Claude Code)

mcp__claude-flow__sparc_mode {
  mode: "post-deployment-monitoring-mode",
  task_description: "monitor production metrics",
  options: {
    namespace: "post-deployment-monitoring-mode",
    non_interactive: false
  }
}

Option 2: Using NPX CLI (Fallback when MCP not available)

# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run post-deployment-monitoring-mode "monitor production metrics"

# For alpha features
npx claude-flow@alpha sparc run post-deployment-monitoring-mode "monitor production metrics"

# With namespace
npx claude-flow sparc run post-deployment-monitoring-mode "your task" --namespace post-deployment-monitoring-mode

# Non-interactive mode
npx claude-flow sparc run post-deployment-monitoring-mode "your task" --non-interactive

Option 3: Local Installation

# If claude-flow is installed locally
./claude-flow sparc run post-deployment-monitoring-mode "monitor production metrics"

Memory Integration

Using MCP Tools (Preferred)

// Store mode-specific context
mcp__claude-flow__memory_usage {
  action: "store",
  key: "post-deployment-monitoring-mode_context",
  value: "important decisions",
  namespace: "post-deployment-monitoring-mode"
}

// Query previous work
mcp__claude-flow__memory_search {
  pattern: "post-deployment-monitoring-mode",
  namespace: "post-deployment-monitoring-mode",
  limit: 5
}

Using NPX CLI (Fallback)

# Store mode-specific context
npx claude-flow memory store "post-deployment-monitoring-mode_context" "important decisions" --namespace post-deployment-monitoring-mode

# Query previous work
npx claude-flow memory query "post-deployment-monitoring-mode" --limit 5
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๐Ÿ“– RuFlo Explained โ€” Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification. Agent = Model + Harness.

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