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Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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npx skills add https://github.com/diegosouzapw/awesome-omni-skill --skill monitoring-observability命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
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| name | monitoring-observability |
| license | MIT |
| compatibility | Claude Code 2.1.56+. |
| description | Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring. |
| tags | ["monitoring","observability","prometheus","grafana","langfuse","tracing","metrics","drift-detection","logging"] |
| context | fork |
| agent | metrics-architect |
| version | 2.0.0 |
| author | OrchestKit |
| user-invocable | false |
| complexity | medium |
| metadata | {"category":"document-asset-creation"} |
Comprehensive patterns for infrastructure monitoring, LLM observability, and quality drift detection. Each category has individual rule files in rules/ loaded on-demand.
| Category | Rules | Impact | When to Use |
|---|---|---|---|
| Infrastructure Monitoring | 3 | CRITICAL | Prometheus metrics, Grafana dashboards, alerting rules |
| LLM Observability | 3 | HIGH | Langfuse tracing, cost tracking, evaluation scoring |
| Drift Detection | 3 | HIGH | Statistical drift, quality regression, drift alerting |
| Silent Failures | 3 | HIGH | Tool skipping, quality degradation, loop/token spike alerting |
Total: 12 rules across 4 categories
# Prometheus metrics with RED method
from prometheus_client import Counter, Histogram
http_requests = Counter('http_requests_total', 'Total requests', ['method', 'endpoint', 'status'])
http_duration = Histogram('http_request_duration_seconds', 'Request latency',
buckets=[0.01, 0.05, 0.1, 0.5, 1, 2, 5])
# Langfuse LLM tracing
from langfuse import observe, get_client
@observe()
async def analyze_content(content: ):
get_client().update_current_trace(
user_id=, session_id=,
tags=[, ],
)
llm.generate(content)
# PSI drift detection
import numpy as np
psi_score = calculate_psi(baseline_scores, current_scores)
if psi_score >= 0.25:
alert("Significant quality drift detected!")
Prometheus metrics, Grafana dashboards, and alerting for application health.
| Rule | File | Key Pattern |
|---|---|---|
| Prometheus Metrics | rules/monitoring-prometheus.md | RED method, counters, histograms, cardinality |
| Grafana Dashboards | rules/monitoring-grafana.md | Golden Signals, SLO/SLI, health checks |
| Alerting Rules | rules/monitoring-alerting.md | Severity levels, grouping, escalation, fatigue prevention |
Langfuse-based tracing, cost tracking, and evaluation for LLM applications.
| Rule | File | Key Pattern |
|---|---|---|
| Langfuse Traces | rules/llm-langfuse-traces.md | @observe decorator, OTEL spans, agent graphs |
| Cost Tracking | rules/llm-cost-tracking.md | Token usage, spend alerts, Metrics API |
| Eval Scoring | rules/llm-eval-scoring.md | Custom scores, evaluator tracing, quality monitoring |
Statistical and quality drift detection for production LLM systems.
| Rule | File | Key Pattern |
|---|---|---|
| Statistical Drift | rules/drift-statistical.md | PSI, KS test, KL divergence, EWMA |
| Quality Drift | rules/drift-quality.md | Score regression, baseline comparison, canary prompts |
| Drift Alerting | rules/drift-alerting.md | Dynamic thresholds, correlation, anti-patterns |
Detection and alerting for silent failures in LLM agents.
| Rule | File | Key Pattern |
|---|---|---|
| Tool Skipping | rules/silent-tool-skipping.md | Expected vs actual tool calls, Langfuse traces |
| Quality Degradation | rules/silent-degraded-quality.md | Heuristics + LLM-as-judge, z-score baselines |
| Silent Alerting | rules/silent-alerting.md | Loop detection, token spikes, escalation workflow |
| Decision | Recommendation | Rationale |
|---|---|---|
| Metric methodology | RED method (Rate, Errors, Duration) | Industry standard, covers essential service health |
| Log format | Structured JSON | Machine-parseable, supports log aggregation |
| Tracing | OpenTelemetry | Vendor-neutral, auto-instrumentation, broad ecosystem |
| LLM observability | Langfuse (not LangSmith) | Open-source, self-hosted, built-in prompt management |
| LLM tracing API | @observe + get_client() | OTEL-native, automatic span creation |
| Drift method | PSI for production, KS for small samples | PSI is stable for large datasets, KS more sensitive |
| Threshold strategy | Dynamic (95th percentile) over static | Reduces alert fatigue, context-aware |
| Alert severity | 4 levels (Critical, High, Medium, Low) | Clear escalation paths, appropriate response times |
| Resource | Description |
|---|---|
| references/ | Logging, metrics, tracing, Langfuse, drift analysis guides |
| checklists/ | Implementation checklists for monitoring and Langfuse setup |
| examples/ | Real-world monitoring dashboard and trace examples |
| scripts/ | Templates: Prometheus, OpenTelemetry, health checks, Langfuse |
defense-in-depth - Layer 8 observability as part of security architecturedevops-deployment - Observability integration with CI/CD and Kubernetesresilience-patterns - Monitoring circuit breakers and failure scenariosllm-evaluation - Evaluation patterns that integrate with Langfuse scoringcaching - Caching strategies that reduce costs tracked by Langfuse