logging-observability-standards

A set of rules for producing searchable, machine-readable application logs and connecting related events across a system. Observability means collecting information that helps explain what software is doing and why it failed.

In plain words
What is it for?
Use it when creating or refactoring backend services, systems with several services or functions, or monitoring and debugging setups. It assumes a logging system and structured-log support.
Why use it?
It replaces scattered plain-text messages with consistent logs that make failures easier to trace across services, while accounting for sensitive user data.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/kraitdev/skill.md/logging-observability-standards
Any agent
npx skills add KraitDev/skiLL.Md --skill logging-observability-standards
Clone the repo
git clone --depth 1 https://github.com/KraitDev/skiLL.Md

Made for: Claude Code, Codex.

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,789 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.01789
Opus 5 $0.00012 $0.00894
Sonnet 5 $0.00005 $0.00358
Haiku 4.5 $0.00002 $0.00179

Measured 2d ago against content hash ccec2f073c82, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

logging-observability-standards 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 2d 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/architecture/logging-observability-standards/SKILL.md · 187 lines

How it starts

The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Logging & Observability Standards

Purpose

Logs are the black box recorder of your system. When failures happen at 2 AM in production, logs are your only witness. This skill ensures application state and failures are highly searchable, machine-readable, and traceable across system boundaries WITHOUT leaking sensitive user data.

When to use

  • Bootstrapping a new backend microservice or monolithic API
  • Refactoring code filled with disorganized console.log or print statements
  • Designing a system that spans multiple services/functions
  • Setting up monitoring, alerting, and debugging infrastructure

When NOT to use

  • Application performance monitoring (APM) - related but different concern
  • Security incident response (use SIEM/security tools)
  • User analytics (different use case, different tool)

Inputs required

  • Backend service with multiple endpoints/functions
  • Logging infrastructure (ELK, DataDog, Grafana Loki, CloudWatch, etc.)
  • Understanding of structured logging concepts

Workflow

  1. Implement Structured Logging: Configure logger to output NDJSON (Newline Delimited JSON) instead of plain text
  2. Inject Context: Attach a correlation_id (or trace_id) at HTTP entry point and pass through all downstream calls
  3. Standardize Levels: ERROR (system broken), WARN (unexpected but recovered), INFO (lifecycle), DEBUG (verbose tracing)
  4. Sanitize Data: Implement redaction middleware to mask credentials, tokens, and PII before logs hit the stream
  5. Add Request Context: Log request duration, status code, user context (anonymized), and performance metrics
  6. Trace Async Flows: Pass correlation ID through event handlers, message queues, and inter-service calls
  7. Monitor Log Health: Alert on high ERROR rates, unexpected patterns, or missing correlation IDs

Rules

  • MUST output JSON in production environments (not plain text)
  • MUST include Request ID/Correlation ID in all HTTP requests
  • MUST NEVER log raw passwords, session tokens, or financial data
  • MUST log full stack traces for ERROR level (for debugging)
  • MUST log only message and context for WARN/INFO (not verbose)
  • MUST redact/sanitize any PII before log emission
  • MUST include timestamps in UTC
  • MUST standardize key names across all logs

Read the full file on GitHub · 187 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.

  1. 2d ago First seen · 187 lines · 23 tokens per session scan A ccec2f073c82

Subscribe to this mod's changes

logging-observability-standards is a skill published in the GitHub repository KraitDev/skiLL.Md (7 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 1,789 once invoked, about $0.0001 per session on Opus 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.

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