observability-review
Skill0 starsChanged 5 months agoMITView on GitHub
Design or review observability — metrics, logs, traces, SLIs/SLOs/error budgets, dashboards, and alerting. Use when adding instrumentation, defining SLOs, reducing alert noise, or improving incident detection ("are we monitoring the right things", "set up SLOs", "why didn't we catch this").
1 other project uses a version of this.
Adopted by
Most recently taken up by
- abevz/devops-skillstheir own version, first seen Oct 15, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- abevz/devops-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- adr-writerSkill
- architecture-reviewSkill
- gcp-architectureSkill
- incident-postmortemSkill
- interview-modeSkill
- leadership-lensSkill
- learning-coachSkill
- llm-app-architectureSkill
- mlops-pipeline-reviewSkill
- ml-system-designSkill
- principal-code-reviewSkill
- rag-system-designSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 24
Avoid vanity metrics; measure what users feel
- Distinctive: line 28
Every alert must be actionable and have a runbook
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Observability Review
Observability is the ability to ask new questions of a system without shipping new code. Optimize for fast diagnosis, not dashboard count.
The three signals
- Metrics — RED (Rate, Errors, Duration) for services; USE (Utilization, Saturation, Errors) for resources
- Logs — structured, correlation IDs, appropriate levels, no PII/secrets
- Traces — distributed tracing across service boundaries; sampled sensibly
SLIs / SLOs
- Define SLIs from the user's perspective (availability, latency, correctness)
- Set SLO targets and error budgets; tie alerting to budget burn
- Avoid vanity metrics; measure what users feel
Alerting
- Alert on symptoms (SLO burn), not every cause
- Every alert must be actionable and have a runbook
- Page on user-impacting; ticket on the rest — kill noise
Review questions
- Can you detect a partial outage before users report it?
- Can you localize a failure to a service/dependency in minutes?
- Is on-call drowning in non-actionable pages?
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

