Anomaly detection
Curated, auditable, benchmark-ready Agent Skills library for Claude Code, Codex, OpenCode, Cursor, and more.
npx -y skills add shinzoxD/knackbox --skill anomaly-detectionAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 27 days oldThe repository was created 27 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Investigate metric and log anomalies with structured hypotheses and next checks. Use whenever the user sees a spike, drop, or weird pattern in metrics or logs and asks what is going on, whether it is real, or how to investigate.
The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
1.5 KB, as published. Nobody here has run it
Anomaly Detection (investigation)
Start with “is it real?”, then scope, then hypotheses, then disconfirm. Avoid declaring root cause from one graph.
Workflow
- Define the signal, window, and expected baseline.
- Check instrumentation changes, deploys, traffic, and batch jobs.
- Segment: region, endpoint, tenant, version.
- Correlate logs/traces/events (not only CPU).
- Rank hypotheses; suggest next 2–3 checks.
- If alerting: propose better signal vs noise.
Output format
## Anomaly read: <signal>
**Real?** likely|unclear|artifact
**Scope:** …
**Hypotheses:** 1…
**Next checks:** …
**Mitigations if user-impacting:** …
Rules
- Do not invent timestamps or deploy times.
- Distinguish level shift vs spike vs missing data.
- Seasonality and weekdays matter.
- Prefer actionable next steps over ML buzzwords.
- Security anomalies → involve security path.
Edge cases
- Counter resets / rate math errors.
- Canary vs full traffic.
- Silent dashboard timezone bugs.