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.
npx skills add KraitDev/skiLL.Md --skill logging-observability-standardsgit clone --depth 1 https://github.com/KraitDev/skiLL.MdWrote 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.
[](https://agentmods.dev/skills/kraitdev/skill.md/logging-observability-standards)<a href="https://agentmods.dev/skills/kraitdev/skill.md/logging-observability-standards"><img src="https://agentmods.dev/badge/skills/kraitdev/skill.md/logging-observability-standards/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.
<a href="https://agentmods.dev/skills/kraitdev/skill.md/logging-observability-standards"><img src="https://agentmods.dev/badge/skills/kraitdev/skill.md/logging-observability-standards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00023 | $0.01789 |
| Opus 5.5 | $0.00009 | $0.00716 |
| Sonnet 5.5 | $0.00005 | $0.00358 |
| Haiku 4.5 | $0.00002 | $0.00179 |
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 1mo 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.
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.logorprintstatements - 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
- Implement Structured Logging: Configure logger to output NDJSON (Newline Delimited JSON) instead of plain text
- Inject Context: Attach a
correlation_id(ortrace_id) at HTTP entry point and pass through all downstream calls - Standardize Levels: ERROR (system broken), WARN (unexpected but recovered), INFO (lifecycle), DEBUG (verbose tracing)
- Sanitize Data: Implement redaction middleware to mask credentials, tokens, and PII before logs hit the stream
- Add Request Context: Log request duration, status code, user context (anonymized), and performance metrics
- Trace Async Flows: Pass correlation ID through event handlers, message queues, and inter-service calls
- 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
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.
- 1mo ago First seen · 187 lines · 23 tokens per session scan A ccec2f073c82
logging-observability-standards is a skill published in the GitHub repository KraitDev/skiLL.Md (6 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 1,789 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.
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