Agent skill
detect-metric-anomaly
Analyze a metric for statistical anomalies using z-score analysis and threshold checking. Detects spikes, drops, and trends. Use for proactive monitoring and early warning. Keywords: anomaly, detection, metric, z-score, threshold, alert, statistical.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/detect-metric-anomaly
Metadata
Additional technical details for this skill
- domain
- general
- category
- analytics
- confidence
- 0.85
- mcp servers
-
[]
- requires approval
- NO
SKILL.md
Detect Metric Anomaly
Preconditions
Before applying this skill, verify:
- Metric name and current value available
- Historical baseline exists (or can be established)
- Threshold configuration available
Actions
1. Retrieve Baseline Statistics
Get or calculate baseline from history:
metric: $metric_name
window_size: 1000 # Last N data points
statistics:
- mean
- std_dev
- percentile_95
2. Calculate Z-Score
Determine how far current value deviates from baseline:
z_score = (current_value - baseline.mean) / baseline.std_dev
Interpret z-score:
- |z| < 2: Normal variation
- 2 <= |z| < 3: Warning (unusual)
- |z| >= 3: Critical (anomaly)
3. Check Absolute Thresholds
Compare against configured thresholds:
cpu_percent:
warning: 80
critical: 95
memory_percent:
warning: 80
critical: 90
error_rate:
warning: 0.01
critical: 0.05
4. Detect Trends
Analyze recent window for sustained deviation:
if 80% of last 10 values > baseline.mean:
trend = "increasing"
elif 80% of last 10 values < baseline.mean:
trend = "decreasing"
else:
trend = "stable"
5. Generate Alert
If anomaly detected:
alert:
metric: $metric_name
severity: $calculated_severity
type: $anomaly_type # spike, drop, trend
z_score: $z_score
current_value: $current_value
baseline_mean: $mean
recommendation: $suggested_action
Success Criteria
The skill succeeds when:
- Metric analyzed against baseline
- Z-score calculated correctly
- Alert generated if anomaly present
Failure Handling
If analysis fails:
- Insufficient data: Wait for more samples
- No baseline: Initialize with current values
- Calculation error: Log and skip this check
Examples
Input Context:
{
"metric": "cpu_percent",
"current_value": 92,
"node": "worker-1"
}
Expected Output:
{
"anomaly_detected": true,
"severity": "warning",
"type": "threshold_breach",
"z_score": 2.3,
"baseline_mean": 45,
"baseline_std_dev": 15,
"percentile_rank": 97,
"recommendation": "Investigate high CPU usage on worker-1"
}
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
majiayu000/claude-skill-registry
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
majiayu000/claude-skill-registry
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
majiayu000/claude-skill-registry
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
majiayu000/claude-skill-registry
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
majiayu000/claude-skill-registry
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
majiayu000/claude-skill-registry
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
Didn't find tool you were looking for?