mcpField
O
observe-metrics

observe-metrics

github.com/ruvnetAgent workflows80.0k733 installsUpdated today
Repository

About observe-metrics

Observe Metrics is an agent skill for summarizing observability metrics and identifying anomalies in system health. It aggregates counters, gauges, and histograms, compares results with baselines, and reports trends and an overall health score.

Key features

  1. 01Retrieves observability metrics
  2. 02Aggregates counters, gauges, histograms
  3. 03Computes metric baselines
  4. 04Flags statistical anomalies
  5. 05Stores metric snapshots
  6. 06Reports trends and health score

Use cases

  1. 01Review task completion and error rates
  2. 02Monitor active agents and memory use
  3. 03Track token consumption
  4. 04Detect changes in task or span duration

MCP support

Uses Ruflo MCP tools to retrieve metric records, search for baselines, and store snapshots. The skill describes memory and pattern tools for this workflow.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill observe-metrics

After installing, type/observe-metricsin Claude Code to confirm it loaded.

Details

From the project README

Observe Metrics

Aggregate counters, gauges, and histograms from the observability namespace and flag anomalies.

When to use

When you need a snapshot of system health -- task completion rates, error rates, active agent counts, memory usage, and token consumption. Useful for monitoring swarm performance and detecting degradation.

Steps

  1. Retrieve metrics -- call mcp__plugin_ruflo-core_ruflo__memory_search --namespace observability (or memory_list) to fetch metric records for the specified period (default: 1 hour). The memory_* tool family routes by namespace; agentdb_hierarchical-* does NOT, so use memory_* here.
  2. Aggregate -- compute:
    • Counters: sum totals (tasks_completed, errors, token_usage)
    • Gauges: current values (active_agents, memory_usage_bytes)
    • Histograms: p50, p95, p99 (task_duration_ms, span_duration_ms)
  3. Compute baselines -- call mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search (ReasoningBank-routed; don't pass a namespace argument — pattern-* tools ignore it) to establish baseline values for each metric.
  4. Flag anomalies -- mark metrics deviating >2 standard deviations from baseline with direction (above/below) and severity
  5. Store patterns -- two paths (per ruflo-cost-tracker ADR-0001 dual-path pattern):
    • Pattern store (typed, recommended): mcp__plugin_ruflo-core_ruflo__agentdb_pattern-store with type: 'metric-snapshot'. No namespace arg.
    • Plain store (namespace-routable): mcp__plugin_ruflo-core_ruflo__memory_store --namespace observability for the snapshot tied to a timestamp.
  6. Report -- display: metric name, current value, baseline, deviation, trend (up/down/stable), anomaly flag; overall health score (green/yellow/red)

CLI alternative

npx @claude-flow/cli@latest memory search --query "system metrics for last hour" --namespace observability

Comments

Markdown supported