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 agentmods add commands/danielbodnar/skills/anomaliesgit clone --depth 1 https://github.com/danielbodnar/skillsWrote 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/commands/danielbodnar/skills/anomalies)<a href="https://agentmods.dev/commands/danielbodnar/skills/anomalies"><img src="https://agentmods.dev/badge/commands/danielbodnar/skills/anomalies.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00264 |
| Opus 5 | $0.00000 | $0.00132 |
| Sonnet 5 | $0.00000 | $0.00053 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
anomalies 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 4d 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.
What it actually says
Anomaly Detection Report
Use Netdata's ML-powered anomaly detection to identify unusual system behavior:
-
Find anomalous metrics in the last 1 hour:
- Set cardinality_limit to 30 for top anomalies
- Include anomaly rate percentages
- Show min/max/avg values for context
-
For each anomaly found:
- Explain what the metric represents
- Show the anomaly rate (% of samples that were anomalous)
- Identify if this is concerning or expected behavior
- Provide context from system knowledge (CLAUDE.md)
-
Categorize anomalies by severity:
- 🔴 Critical (>10% anomaly rate or system-critical metrics)
- 🟡 Warning (5-10% anomaly rate)
- 🔵 Info (1-5% anomaly rate or expected variations)
-
Check for correlated anomalies:
- Do multiple related metrics show anomalies?
- Are anomalies concentrated in specific time windows?
- Are they related to known incidents?
-
Compare to baseline from CLAUDE.md:
- SSH threads (12.5% anomaly rate - known)
- WireGuard drops (4.1% - investigate)
- Container CPU spikes (3.3% - monitor)
Provide actionable recommendations for each significant anomaly.
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.
- 4d ago First seen · 32 lines · 0 tokens per session scan A 4dde795f7c7f
anomalies is a command published in the GitHub repository danielbodnar/skills (2 stars, last pushed 26d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 264 tokens. 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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