prometheus-configuration

Overview of prometheus-configuration skill

What prometheus-configuration does

The prometheus-configuration skill helps an agent produce practical Prometheus setup guidance for metric scraping, retention, alerting, and recording rules. It is aimed at the real work of getting Prometheus running in Kubernetes, Docker Compose, or a more traditional server setup—not just explaining what Prometheus is.

Who should use this skill

This skill is best for platform engineers, SREs, DevOps teams, and developers who need to stand up monitoring fast and want the agent to generate usable configuration patterns. It is especially relevant if you are working on Observability and need help translating a monitoring goal into Prometheus config structure.

The job to be done

Most users are trying to answer one of these questions:

• How do I install Prometheus in my environment?
• How should I define scrape targets and jobs?
• How do I add alerting and recording rules without guessing the file layout?
• How do I move from “monitor this service” to a concrete Prometheus config?

The prometheus-configuration skill is useful because it narrows the prompt space around those tasks and gives the agent a stronger default frame than a generic “write me a Prometheus config” request.

What differentiates this skill

Compared with an ordinary prompt, the prometheus-configuration skill is centered on configuration workflows: architecture, installation paths, scrape configuration, service discovery, and rules. The source material is compact, but it does include concrete install examples and an opinionated scope, which makes it more actionable than a broad observability prompt.

When this skill is a good fit

Use prometheus-configuration for Observability when you already know you want Prometheus and need help with:

• initial deployment choices
• scrape configuration patterns
• alert and recording rule structure
• adapting setup examples to your environment

If you need vendor-neutral monitoring strategy, OpenTelemetry pipeline design, or deep Grafana dashboard design, this skill is only one part of the solution.

How to Use prometheus-configuration skill

Install context for prometheus-configuration

The repository does not surface a dedicated install command inside SKILL.md, so users typically add the parent skill collection, then invoke prometheus-configuration by name within their agent environment. If your toolchain supports skill installation by repository URL, use the repository path for wshobson/agents and select the prometheus-configuration skill.

A common pattern is:

1. Add the skill source repository to your agent tool.
2. Enable or reference prometheus-configuration.
3. Prompt the agent with your deployment context, targets, and constraints.

Read this file first

Start with:

• plugins/observability-monitoring/skills/prometheus-configuration/SKILL.md

Because this skill has no extra scripts, references, or metadata files surfaced here, SKILL.md is the primary source of truth. That means your output quality depends heavily on how much deployment context you provide in the prompt.

What input the skill needs

For strong prometheus-configuration usage, provide:

• environment: Kubernetes, Docker Compose, VM, bare metal
• targets: apps, node exporters, kube-state-metrics, blackbox probes, databases
• scale: number of services, expected cardinality, retention needs
• alerting needs: latency, error rate, resource saturation, up/down
• storage constraints: disk, retention days, long-term storage plans
• discovery model: static configs, Kubernetes service discovery, cloud discovery

Without these inputs, the agent can still produce examples, but they will be generic and may not match your topology.

Turn a rough goal into a strong prompt

Weak prompt:

• “Set up Prometheus for my app.”

Stronger prompt:

• “Use the prometheus-configuration skill to design a Prometheus setup for a Kubernetes cluster with 20 services. We need 30-day retention, scraping app /metrics endpoints, node metrics, and alerting for pod restarts, high CPU, and 5xx rate. Show Helm-based install choices, example scrape configs, and starter recording and alert rules.”

This works better because it gives the skill the deployment model, scale, retention target, and outcome format.

Practical workflow for prometheus-configuration usage

A good workflow is:

1. Ask for an installation plan for your environment.
2. Ask for the base prometheus.yml or Helm values.
3. Add scrape jobs and service discovery.
4. Add recording rules for expensive or frequently reused queries.
5. Add alert rules with thresholds tied to your SLOs or operating norms.
6. Review retention, storage, and cardinality risks before deploying.

This staged approach produces better results than asking for “complete monitoring” in one step.

Use the built-in installation patterns

The source skill explicitly includes installation guidance for:

• Kubernetes with Helm
• Docker Compose

That makes prometheus-configuration install most useful if you are choosing between those common deployment paths. If you are on Kubernetes, ask the agent to adapt the Helm example into a values override file rather than pasting a long inline command. If you are on Compose, ask for a complete docker-compose.yml plus mounted config and rules files.

Ask for environment-specific output

The skill is much more valuable when you request artifacts, not just explanation. Good asks include:

• “Generate prometheus.yml for these targets.”
• “Create Helm values overrides for retention and persistent storage.”
• “Write recording rules for HTTP request rate and p95 latency.”
• “Create alert rules for exporter down, disk pressure, and sustained error rate.”

This keeps the agent anchored to outputs you can review and apply.

Files and structures to request explicitly

Because the skill covers setup and rules, ask the agent to separate output into:

• prometheus.yml
• rules/recording_rules.yml
• rules/alert_rules.yml
• Helm values overrides if using kube-prometheus-stack

That file-oriented prompt reduces ambiguity and makes review easier.

Tips that materially improve output quality

Ask the agent to state assumptions. Prometheus configs fail more often from hidden assumptions than from syntax alone. Useful additions:

• expected scrape interval
• label strategy
• relabeling needs
• namespace scope
• retention and storage sizing assumptions

Also ask it to flag tradeoffs, especially around high-cardinality labels, scrape frequency, and long retention.

Misfit cases to recognize early

Do not expect the prometheus-configuration guide to fully solve:

• application instrumentation changes
• Grafana dashboard design
• Alertmanager routing policy in depth
• long-term storage architecture beyond basic mention of systems like Thanos or Cortex

If those are your main tasks, use this skill as the Prometheus foundation, then layer more specialized guidance.

prometheus-configuration skill FAQ

Is prometheus-configuration good for beginners?

Yes, if you already understand the basics of metrics and need help getting to a working setup. The skill includes architecture and installation context, which helps beginners orient themselves. But it does not replace operational judgment on thresholds, retention sizing, or metric hygiene.

How is this different from a normal prompt?

A normal prompt may produce plausible YAML with weak structure or missing operational pieces. The prometheus-configuration skill pushes the agent toward the actual Prometheus workflow: install path, scrape setup, rules, and service discovery. That usually means less prompt back-and-forth.

Is prometheus-configuration only for Kubernetes?

No. The source includes Kubernetes with Helm and Docker Compose examples. It is still usable for other environments, but the best-supported paths are those two deployment models.

Can it help with alert rules and recording rules?

Yes. That is one of the clearer strengths of the skill. If you provide target services, core metrics, and what conditions matter, the agent can generate much more useful starter rules than a generic request.

When should I not use prometheus-configuration?

Skip this skill if:

• you are not using Prometheus at all
• you need full observability architecture across logs, traces, and metrics
• you mainly need instrumentation code in an app language
• you need advanced Alertmanager policy design more than Prometheus setup

Does it cover production concerns?

Partly. It points to retention, storage, and long-term storage concepts, but it is not a complete production operations manual. Ask explicitly for scaling assumptions, storage sizing, and cardinality risk review if you want production-grade output.

How to Improve prometheus-configuration skill

Give infrastructure details, not just app names

The fastest way to improve prometheus-configuration results is to provide topology:

• where Prometheus runs
• what exposes metrics
• how targets are discovered
• how long metrics should be retained
• what alerts matter to responders

“Monitor payments-service” is weak. “Monitor payments-service in Kubernetes via ServiceMonitor, scrape every 15s, retain 30 days, alert on 5xx rate and p95 latency” is much stronger.

Ask for assumptions and validation checks

Have the agent include:

• assumptions section
• config file breakdown
• likely failure points
• post-deploy validation steps

For example, ask it to tell you how to verify scrape targets in the Prometheus UI and how to confirm rules loaded successfully. This catches bad output early.

Reduce ambiguity around labels and cardinality

One common failure mode is generating configs that scrape too much or preserve dangerous labels. Ask the agent to:

• identify high-cardinality labels to avoid
• recommend relabeling if needed
• explain why a given scrape interval is appropriate

This matters more for production than adding more example YAML.

Improve rule quality with real service signals

Alert and recording rules get much better when you provide:

• metric names actually emitted by your services
• expected traffic level
• acceptable latency and error thresholds
• whether alerts should be fast-noisy or slow-stable

Otherwise the agent will fall back to generic rules that may not match your metric names or operating tolerances.

Iterate from install to operations

A strong prometheus-configuration guide prompt sequence looks like:

1. “Generate install approach for my environment.”
2. “Now create the base config files.”
3. “Now add scrape jobs for these services.”
4. “Now add recording rules for common queries.”
5. “Now add alerts tuned for these thresholds.”
6. “Now review for cardinality, retention, and storage risks.”

This sequence usually produces better final output than a single large prompt.

Request output in deployable artifacts

If the first answer is too explanatory, tighten the prompt:

• “Return only the Helm values override file.”
• “Return prometheus.yml plus two rule files.”
• “Include comments only where they help operators maintain the config.”

That makes the skill more useful in real implementation work.

Watch for these common failure modes

Review outputs for:

• scrape jobs missing target labels or paths
• rule expressions using metrics you do not have
• retention settings that ignore available disk
• Kubernetes examples that assume CRDs you have not installed
• static configs suggested where service discovery would be safer

These are normal places where prometheus-configuration usage benefits from one more iteration.

Pair the skill with your own repo context

The skill is strongest when the agent can see your existing deployment files, Helm charts, or service manifests. If possible, include:

• current monitoring namespace setup
• existing ServiceMonitors or PodMonitors
• exporters already deployed
• metric endpoint paths
• sample metric names

That lets the agent adapt the Prometheus configuration instead of inventing one from scratch.