prometheus-configuration▌
wshobson/agents · updated Apr 8, 2026
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Complete Prometheus setup guide covering scrape configuration, recording rules, and alerting.
- ›Includes Kubernetes and Docker Compose installation methods with example configurations for static targets, file-based discovery, and Kubernetes service discovery
- ›Provides pre-built recording rules for HTTP metrics (request rates, error rates, latency percentiles) and resource metrics (CPU, memory, disk utilization)
- ›Covers alert rule examples for service availability, error rates, latency th
Prometheus Configuration
Complete guide to Prometheus setup, metric collection, scrape configuration, and recording rules.
Purpose
Configure Prometheus for comprehensive metric collection, alerting, and monitoring of infrastructure and applications.
When to Use
- Set up Prometheus monitoring
- Configure metric scraping
- Create recording rules
- Design alert rules
- Implement service discovery
Prometheus Architecture
┌──────────────┐
│ Applications │ ← Instrumented with client libraries
└──────┬───────┘
│ /metrics endpoint
↓
┌──────────────┐
│ Prometheus │ ← Scrapes metrics periodically
│ Server │
└──────┬───────┘
│
├─→ AlertManager (alerts)
├─→ Grafana (visualization)
└─→ Long-term storage (Thanos/Cortex)
Installation
Kubernetes with Helm
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install prometheus prometheus-community/kube-prometheus-stack \
--namespace monitoring \
--create-namespace \
--set prometheus.prometheusSpec.retention=30d \
--set prometheus.prometheusSpec.storageVolumeSize=50Gi
Docker Compose
version: "3.8"
services:
prometheus:
image: prom/prometheus:latest
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prometheus-data:/prometheus
command:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention.time=30d"
volumes:
prometheus-data:
Configuration File
prometheus.yml:
global:
scrape_interval: 15s
evaluation_interval: 15s
external_labels:
cluster: "production"
region: "us-west-2"
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
- alertmanager:9093
# Load rules files
rule_files:
- /etc/prometheus/rules/*.yml
# Scrape configurations
scrape_configs:
# Prometheus itself
- job_name: "prometheus"
static_configs:
- targets: ["localhost:9090"]
# Node exporters
- job_name: "node-exporter"
static_configs:
- targets:
- "node1:9100"
- "node2:9100"
- "node3:9100"
relabel_configs:
- source_labels: [__address__]
target_label: instance
regex: "([^:]+)(:[0-9]+)?"
replacement: "${1}"
# Kubernetes pods with annotations
- job_name: "kubernetes-pods"
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels:
[__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
target_label: __address__
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: namespace
- source_labels: [__meta_kubernetes_pod_name]
action: replace
target_label: pod
# Application metrics
- job_name: "my-app"
static_configs:
- targets:
- "app1.example.com:9090"
- "app2.example.com:9090"
metrics_path: "/metrics"
scheme: "https"
tls_config:
ca_file: /etc/prometheus/ca.crt
cert_file: /etc/prometheus/client.crt
key_file: /etc/prometheus/client.key
Reference: See assets/prometheus.yml.template
Scrape Configurations
Static Targets
scrape_configs:
- job_name: "static-targets"
static_configs:
- targets: ["host1:9100", "host2:9100"]
labels:
env: "production"
region: "us-west-2"
File-based Service Discovery
scrape_configs:
- job_name: "file-sd"
file_sd_configs:
- files:
- /etc/prometheus/targets/*.json
- /etc/prometheus/targets/*.yml
refresh_interval: 5m
targets/production.json:
[
{
"targets": ["app1:9090", "app2:9090"],
"labels": {
"env": "production",
"service": "api"
}
}
]
Kubernetes Service Discovery
scrape_configs:
- job_name: "kubernetes-services"
kubernetes_sd_configs:
- role: service
relabel_configs:
- source_labels:
[__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels:
[__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
Reference: See references/scrape-configs.md
Recording Rules
Create pre-computed metrics for frequently queried expressions:
# /etc/prometheus/rules/recording_rules.yml
groups:
- name: api_metrics
How to use prometheus-configuration on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add prometheus-configuration
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches prometheus-configuration from GitHub repository wshobson/agents and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate prometheus-configuration. Access the skill through slash commands (e.g., /prometheus-configuration) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★43 reviews- ★★★★★Ganesh Mohane· Dec 16, 2024
I recommend prometheus-configuration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Neel Yang· Dec 16, 2024
We added prometheus-configuration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Shikha Mishra· Dec 12, 2024
prometheus-configuration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Lucas Choi· Dec 12, 2024
Useful defaults in prometheus-configuration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Neel Martin· Nov 7, 2024
Useful defaults in prometheus-configuration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yash Thakker· Nov 3, 2024
Keeps context tight: prometheus-configuration is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Liam Gupta· Nov 3, 2024
We added prometheus-configuration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Neel Sharma· Oct 26, 2024
prometheus-configuration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Dhruvi Jain· Oct 22, 2024
Registry listing for prometheus-configuration matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Omar Chawla· Oct 22, 2024
Solid pick for teams standardizing on skills: prometheus-configuration is focused, and the summary matches what you get after install.
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