What is observability-and-monitoring?

A method for making running software understandable through logs, metrics, traces, and alerts. Logs record events, metrics measure behavior, and traces follow a request across services.

Defining service measures and targets, adding structured logs, tracing requests, setting alerts, and checking whether new services or data flows can be debugged.

It makes production failures and slow behavior easier to investigate without adding temporary diagnostic code. Clear targets also help determine when an alert is needed.

observability-and-monitoring — Skill by vignesh2027/AI-AGENT-SKILLS

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add vignesh2027/AI-AGENT-SKILLS --skill observability-and-monitoring

Clone the repo

git clone --depth 1 https://github.com/vignesh2027/AI-AGENT-SKILLS

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/observability-and-monitoring/github.svg)](https://agentmods.dev/skills/vignesh2027/ai-agent-skills/observability-and-monitoring)

Your own site

<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/observability-and-monitoring"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/observability-and-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/observability-and-monitoring"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/observability-and-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 886 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00024	$0.00886
Opus 5.5	$0.00010	$0.00354
Sonnet 5.5	$0.00005	$0.00177
Haiku 4.5	$0.00002	$0.00089

Measured 2mo ago against content hash 71ec1ae61d42, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

observability-and-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/observability-and-monitoring/SKILL.md · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Overview

A system you cannot observe is a system you cannot debug. Observability is not a dashboard — it is the ability to answer any question about system behavior using existing data, without deploying new code. This skill implements the three pillars (logs, metrics, traces) and the alerts that page you before users notice.

When to Use

Before deploying any new service or significant feature
When a production issue took more than 30 minutes to diagnose
When adding a new data flow, user journey, or integration
As part of the /ship workflow

Process

Step 1: Define the SLIs and SLOs

SLI (Service Level Indicator): what you measure — latency, error rate, availability, throughput. SLO (Service Level Objective): the target — "p99 latency < 500ms," "error rate < 0.1%."

Without SLOs, you don't know when to alert.

Step 2: Structured logging

Log in JSON (not freeform text)
Include: timestamp, log level, service name, trace ID, user ID (hashed), request ID
Log at entry and exit of every significant operation
Log errors with: exception type, message, stack trace, context
Never log PII, passwords, tokens, or credit card numbers
Log what happened, not just that it happened

Step 3: Key metrics

Implement the four golden signals for every service:

Latency — p50, p95, p99 response time
Traffic — requests per second, events per second
Errors — error rate (4xx, 5xx), error count
Saturation — CPU, memory, queue depth, connection pool

Add business metrics: active users, signups, payments, key conversions.

Step 4: Distributed tracing

Assign a trace ID to every inbound request
Propagate the trace ID to all downstream calls (outbound HTTP, database, queue)
Use OpenTelemetry or compatible SDK
Sample at 100% for errors; sample at 1–10% for success paths

Step 5: Health checks and readiness probes

/healthz — is the service alive?
/readyz — is the service ready to accept traffic?
Health checks must not perform significant work (no database writes)
Check downstream dependencies in readiness probe

Read the full file on GitHub · 98 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 98 lines · 24 tokens per session scan A 71ec1ae61d42

Subscribe to this mod's changes

observability-and-monitoring is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 886 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metrics Claude Code Debugging agent-skills ai-agents coding-agent cursor-ai developer-tools

observability-and-monitoring next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
observability-and-monitoringvignesh2027/AI-AGENT-SKILLS	Skill	2	2mo ago	MIT	A	24	Claude Code, Codex
stuckQwenLM/qwen-code	Skill	29k	today	Apache-2.0	A	57	Claude Code
structured-debuggingQwenLM/qwen-code	Skill	29k	today	Apache-2.0	A	85	Claude Code, Codex
test-failure-explorerQwenLM/qwen-code	Skill	29k	today	Apache-2.0	A	32	Claude Code, Codex
rust-checkHmbown/CodeWhale	Skill	41k	6d ago	MIT	A	15	Claude Code, Codex
debugHmbown/CodeWhale	Skill	41k	6d ago	MIT	A	30	Claude Code, Codex
feedbackHmbown/Codewhale	Skill	41k	1mo ago	MIT	A	0	Claude Code, Codex