Sre Monitoring And Observability logo

Sre Monitoring And Observability

Organization
TheBushidoCollective
sre-monitoring-and-observability

Use when building comprehensive monitoring and observability systems.

Overview

PublisherTheBushidoCollective
Repositoryhan
Skill namesre-monitoring-and-observability
Stars
195
Forks
20
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Sre Monitoring And 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.
  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/TheBushidoCollective/han.git /tmp/han
mkdir -p .claude/skills
cp -r /tmp/han/plugins/disciplines/site-reliability-engineering/skills/sre-monitoring .claude/skills/sre-monitoring-and-observability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sre Monitoring And 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 Sre Monitoring And 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 Sre Monitoring And 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.

SRE Monitoring and Observability

Building comprehensive monitoring and observability systems.

Four Golden Signals

Latency

Time to process requests:

prometheus
# Request duration
http_request_duration_seconds

# Query
histogram_quantile(0.95, 
  rate(http_request_duration_seconds_bucket[5m])
)

Traffic

Demand on the system:

prometheus
# Requests per second
rate(http_requests_total[5m])

# By endpoint
sum(rate(http_requests_total[5m])) by (endpoint)

Errors

Rate of failed requests:

prometheus
# Error rate
rate(http_requests_total{status=~"5.."}[5m])
/ 
rate(http_requests_total[5m])

# SLI compliance
1 - (error_rate / slo_target)

Saturation

Resource utilization:

prometheus
# CPU usage
100 - (avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

# Memory usage
(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) 
/ node_memory_MemTotal_bytes * 100

Service Level Indicators (SLIs)

Availability SLI

prometheus
# Successful requests / Total requests
sum(rate(http_requests_total{status=~"[23].."}[30d]))
/
sum(rate(http_requests_total[30d]))

Latency SLI

prometheus
# Requests faster than threshold / Total requests
sum(rate(http_request_duration_seconds_bucket{le="0.5"}[30d]))
/
sum(rate(http_request_duration_seconds_count[30d]))

Throughput SLI

prometheus
# Requests processed within capacity
clamp_max(
  rate(http_requests_total[5m]) / capacity_requests_per_second,
  1.0
)

Alerting

Alert Severity Levels

P0 - Critical: Service down or severe degradation P1 - High: Significant impact, error budget at risk
P2 - Medium: Degradation, not user-facing yet P3 - Low: Awareness, no immediate action needed

Example Alerts

yaml
# High error rate
groups:
  - name: sre
    rules:
      - alert: HighErrorRate
        expr: |
          rate(http_requests_total{status=~"5.."}[5m])
          / rate(http_requests_total[5m])
          > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate on {{ $labels.service }}"
          
      - alert: LatencyP95High
        expr: |
          histogram_quantile(0.95,
            rate(http_request_duration_seconds_bucket[5m])
          ) > 1.0
        for: 10m
        labels:
          severity: warning
          
      - alert: ErrorBudgetBurn
        expr: |
          (1 - sli_availability) > (error_budget_remaining * 10)
        for: 1h
        labels:
          severity: high

Dashboards

Overview Dashboard

  • Service health (red/yellow/green)
  • Request rate
  • Error rate
  • Latency percentiles (p50, p95, p99)
  • Saturation metrics

Detailed Dashboard

  • Per-endpoint metrics
  • Dependency health
  • Database performance
  • Cache hit rates
  • Queue depths

Distributed Tracing

OpenTelemetry

javascript
const { trace } = require('@opentelemetry/api');
const tracer = trace.getTracer('my-service');

async function handleRequest(req) {
  const span = tracer.startSpan('handle_request');
  
  try {
    span.setAttribute('user.id', req.user.id);
    span.setAttribute('request.path', req.path);
    
    const result = await processRequest(req);
    
    span.setStatus({ code: SpanStatusCode.OK });
    return result;
  } catch (error) {
    span.setStatus({
      code: SpanStatusCode.ERROR,
      message: error.message,
    });
    throw error;
  } finally {
    span.end();
  }
}

Structured Logging

javascript
logger.info('request_processed', {
  request_id: req.id,
  user_id: req.user.id,
  endpoint: req.path,
  method: req.method,
  status_code: res.statusCode,
  duration_ms: duration,
  error: error?.message,
});

Best Practices

USE Method

For resources:

  • Utilization: % time resource is busy
  • Saturation: Work queued but not serviced
  • Errors: Error count

RED Method

For requests:

  • Rate: Requests per second
  • Errors: Failed requests per second
  • Duration: Request latency distribution

Alert on Symptoms, Not Causes

yaml
# Good - alert on user impact
- alert: HighLatency
  expr: p95_latency > 1s

# Bad - alert on potential cause
- alert: HighCPU
  expr: cpu_usage > 80%

Runbook Links

yaml
annotations:
  runbook: "https://wiki.example.com/runbooks/high-error-rate"
  dashboard: "https://grafana.example.com/d/abc123"

Frequently asked questions

What does the Sre Monitoring And Observability AI skill do?

Use when building comprehensive monitoring and observability systems.

Why use Sre Monitoring And Observability on TypingMind?

Because you install it once and use it with any model. Sre Monitoring And 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 Sre Monitoring And Observability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TheBushidoCollective/han/tree/main/plugins/disciplines/site-reliability-engineering/skills/sre-monitoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sre Monitoring And 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 Sre Monitoring And Observability?

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

Is the Sre Monitoring And Observability AI skill free?

It is published on GitHub by TheBushidoCollective. Check the repository for licensing terms. 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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