Observe Metrics

Skill Verified Active

Aggregate and display system metrics with anomaly detection for a time period

Purpose

To provide users with a clear and actionable snapshot of system health by aggregating metrics and identifying anomalies, enabling quick detection of performance degradation.

Features

Aggregate system metrics (counters, gauges, histograms)
Detect anomalies based on historical baselines
Report on metric deviations, direction, and severity
Compute overall health score
Store metric patterns for future reference

Use Cases

Monitor swarm performance by tracking task completion and error rates
Detect degradation in active agent counts or memory usage
Identify unusual token consumption patterns
Analyze time-series data for performance trends

Non-Goals

Real-time, second-by-second monitoring
Long-term trend analysis beyond the specified period
Automated remediation of detected anomalies

Workflow

Retrieve metrics using `mcp__claude-flow__memory_search` or `memory_list` for the specified period.
Aggregate counters, gauges, and histograms.
Compute baselines using `mcp__claude-flow__agentdb_pattern-search`.
Flag anomalies based on deviation from baselines.
Store patterns using `mcp__claude-flow__agentdb_pattern-store` or `memory_store`.
Display metric name, value, baseline, deviation, trend, anomaly flag, and overall health score.

Practices

Observability
Anomaly Detection
System Monitoring

Prerequisites

Claude Code environment
Access to `mcp__claude-flow__memory_search`
Access to `mcp__claude-flow__memory_list`
Access to `mcp__claude-flow__agentdb_pattern-search`
Access to `mcp__claude-flow__agentdb_pattern-store`

Installation

First, add the marketplace

/plugin marketplace add ruvnet/ruflo

/plugin install ruflo-observability@ruflo

Quality Score

Verified

98 /100

Analyzed about 16 hours ago

Trust Signals

Last commitabout 18 hours ago

GitHub owner ruvnet

Stars50.2k

Downloads 68.3k

LicenseMIT

Websitecognitum.one

Status

View Source

Similar Extensions

Monitor Data Integrity

100

Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.

Skill

pjt222

Azure Monitor Query Py

100

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".

Skill

microsoft

Query Netdata Cloud

100

Query Netdata Cloud via its REST API -- metrics, logs (systemd-journal / windows-events / otel-logs), topology graphs (topology:snmp), network flows (flows:netflow), alerts, dynamic configuration (DynCfg), and generic Functions on a node. Use when the user asks about querying Netdata Cloud, fetching metrics from the cloud, querying logs / topology / netflow / sflow / ipfix through Cloud, listing or modifying configurations via DynCfg, calling agent Functions through Cloud, listing spaces/rooms/nodes, or building a curl command against `app.netdata.cloud`. Pairs with the `query-netdata-agents` skill when direct-agent access is needed.

Skill

netdata

Observability Designer

100

Observability Designer (POWERFUL)

Skill

alirezarezvani

Grafana Dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

Skill

wshobson

Monitor Stream

Stream live swarm events using the Monitor tool for real-time observability

Skill

ruvnet