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Monitoring Observability

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yonatangross
monitoring-observability

Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse v4 LLM tracing (as_type, score_current_span, should_export_span, LangfuseMedia), and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namemonitoring-observability
Stars
298
Forks
35
Bundled files
26
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 26 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yonatangross on GitHub. Read the source before you install it.

Installation

Install the Monitoring Observability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/monitoring-observability
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/monitoring-observability .claude/skills/monitoring-observability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Monitoring Observability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Monitoring Observability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Monitoring Observability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Monitoring & Observability

A wrap around Prometheus, Grafana, OpenTelemetry and Langfuse, not a re-teaching of them. This skill carries OrchestKit's delta (version floors, house decisions, scars) and points at the vendor for everything else. Start at references/ork-delta.md.

Upstream coverage (do not restate)

These topics are fully covered first-party. Read the source, do not add a local copy.

TopicFirst-party source
Prometheus metric types, RED method, cardinality, PromQLhttps://prometheus.io/docs/practices/
Alertmanager grouping, inhibition, escalation, runbookshttps://prometheus.io/docs/alerting/latest/configuration/
Grafana dashboards, Loki and LogQL, Promtailhttps://grafana.com/docs/
OpenTelemetry spans, sampling, context propagationhttps://opentelemetry.io/docs/
Langfuse Python SDK (@observe, as_type, score_current_span, should_export_span, LangfuseMedia)https://langfuse.com/docs/sdk/python
Langfuse v2 to v4 Python and v3 to v5 JS migration pathshttps://langfuse.com/docs/sdk/python/v4-migration
Langfuse self-hosting (ClickHouse, Redis, S3, Helm)https://langfuse.com/docs/deployment/self-host
Langfuse cost tracking, model pricing, Metrics API v2https://langfuse.com/docs/model-usage-and-cost
Langfuse scores, online evaluators, annotation queues, prompt managementhttps://langfuse.com/docs/scores/overview
Langfuse framework integrations (LangChain, LangGraph, CrewAI, Pydantic AI, Bedrock, LiveKit)https://langfuse.com/docs/integrations
Agent Graphs, observation types, rendered tool callshttps://langfuse.com/docs/tracing-features/agent-graphs
PSI, KS test, KL and JS divergence, Wasserstein, embedding drifthttps://www.evidentlyai.com/blog/data-drift-detection-large-datasets
EWMA control chartshttps://www.itl.nist.gov/div898/handbook/pmc/section3/pmc324.htm
structlog, Winston, correlation IDs, log samplinghttps://www.structlog.org/en/stable/

Quick Reference

CategoryRulesImpactWhen to Use
Infrastructure Monitoring1CRITICALGrafana dashboards, Golden Signals, SLO/SLI
LLM Observability1HIGHLangfuse tracing, observation types, agent graphs
Silent Failures3HIGHTool skipping, quality degradation, loop/token spike alerting

Total: 5 rules across 3 categories. Drift detection, cost tracking, eval scoring, Prometheus instrumentation and alert-rule authoring moved to the upstream sources listed above.

Quick Start

python
# Langfuse v4 LLM tracing: semantic as_type plus inline scoring
from langfuse import observe, get_client

@observe(as_type="generation", name="analyze_content")
async def analyze_content(content: str):
    get_client().update_current_trace(
        user_id="user_123", session_id="session_abc",
        tags=["production", "orchestkit"],
    )
    result = await llm.generate(content)
    get_client().score_current_span(name="response_quality", value=0.85)
    return result
python
# Prometheus RED method, wired the way this repo expects (bounded labels only)
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])

Infrastructure Monitoring

Dashboard and health-check patterns. Metric instrumentation and alert-rule syntax are upstream.

RuleFileKey Pattern
Grafana Dashboardsrules/monitoring-grafana.mdGolden Signals, SLO/SLI, health checks

CC 2.1.161 — OTEL resource attributes as metric labels: OTEL_RESOURCE_ATTRIBUTES values are now attached as labels on metric datapoints, so usage metrics can be sliced by custom dimensions (team, repo, environment). Add label selectors to dashboards for multi-tenant / per-team cost and usage tracking.

LLM Observability

Langfuse-based tracing for LLM applications. Cost tracking, scoring and drift statistics are upstream; what stays here is how this repo wires traces.

RuleFileKey Pattern
Langfuse Tracesrules/llm-langfuse-traces.md@observe decorator, OTEL spans, agent graphs

Silent Failures

Detection and alerting for silent failures in LLM agents.

RuleFileKey Pattern
Tool Skippingrules/silent-tool-skipping.mdExpected vs actual tool calls, Langfuse traces
Quality Degradationrules/silent-degraded-quality.mdHeuristics + LLM-as-judge, z-score baselines
Silent Alertingrules/silent-alerting.mdLoop detection, token spikes, escalation workflow

CC 2.1.169 — OTEL client-cert paths require trust: untrusted project settings can no longer set OTEL client-certificate paths without a trust confirmation. If your OTEL exporter uses client certs configured in project .claude/settings.json, expect a one-time trust prompt on first use in an untrusted project — telemetry silently not flowing after 2.1.169 is usually this gate, not the collector.

Key Decisions

DecisionRecommendationRationale
Metric methodologyRED method (Rate, Errors, Duration)Industry standard, covers essential service health
Log formatStructured JSONMachine-parseable, supports log aggregation
TracingOpenTelemetryVendor-neutral, auto-instrumentation, broad ecosystem
LLM observabilityLangfuse (not LangSmith)Open-source, self-hosted, built-in prompt management
LLM tracing API@observe(as_type=...) + score_current_span()v4: semantic types, inline scoring, span filtering
Langfuse APIsObservations API v2 + Metrics API v2v4 (Mar 2026): faster querying, aggregations at scale
Hook telemetry transportJSONL under ~/.claude/analytics/, never an SDK in-processHooks are per-event processes; SDK init would be paid on every spawn (references/ork-delta.md)

Detailed Documentation

ResourceDescription
references/ork-delta.mdStart here. Floors, house decisions and scars that upstream docs do not carry
references/langfuse-js-v5.mdJS/TS SDK v5 delta from Python 4.x: package map, phantom packages, SpanProcessor vs exporter. Read before writing any JS Langfuse code
references/experiments-api.mdLangfuse experiments and dataset runs as this repo uses them
references/evaluation-scores.mdScore shapes and scoring pipeline wiring
references/session-tracking.mdSession and user grouping across multi-step workflows
references/metrics-collection.mdClaude Code OTEL metric inventory and collector-side joins
references/dashboards.mdDashboard layout conventions
references/structured-logging.mdStructured log field conventions
references/dev-agent-lens.mdLiteLLM proxy layer for API-boundary observability
examples/orchestkit-monitoring-dashboard.mdWorked monitoring dashboard example
scripts/Templates: Prometheus, OpenTelemetry, health checks, Langfuse

Related Skills

  • defense-in-depth - Layer 8 observability as part of security architecture
  • devops-deployment - Observability integration with CI/CD and Kubernetes
  • resilience-patterns - Monitoring circuit breakers and failure scenarios
  • llm-evaluation - Evaluation patterns that integrate with Langfuse scoring
  • caching - Caching strategies that reduce costs tracked by Langfuse

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Monitoring Observability AI skill do?

Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse v4 LLM tracing (as_type, score_current_span, should_export_span, LangfuseMedia), and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.

Why use Monitoring Observability on TypingMind?

Because you install it once and use it with any model. Monitoring Observability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Monitoring Observability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/monitoring-observability. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Monitoring Observability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Monitoring Observability?

As many as you like. As long as a model supports skills, you can use Monitoring Observability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Monitoring Observability AI skill free?

Yes. It is published on GitHub by yonatangross under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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