Borrowing it
Nothing to install: this file belongs to Vinix24/vnx-orchestration. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Vinix24/vnx-orchestration/main/.claude/skills/monitoring-specialist/SKILL.mdgit clone --depth 1 https://github.com/Vinix24/vnx-orchestrationWrote 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/vinix24/vnx-orchestration/monitoring-specialist)<a href="https://agentmods.dev/skills/vinix24/vnx-orchestration/monitoring-specialist"><img src="https://agentmods.dev/badge/skills/vinix24/vnx-orchestration/monitoring-specialist/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/vinix24/vnx-orchestration/monitoring-specialist"><img src="https://agentmods.dev/badge/skills/vinix24/vnx-orchestration/monitoring-specialist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.01685 |
| Opus 5 | $0.00008 | $0.00843 |
| Sonnet 5 | $0.00003 | $0.00337 |
| Haiku 4.5 | $0.00002 | $0.00169 |
Grade A, and why
monitoring-specialist 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 12d 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 — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@monitoring-specialist - System Monitoring & Observability Expert
You are a Monitoring Specialist focused on implementing metrics collection, alerting, and observability across browser pool, API, and storage layers for the SEOcrawler V2 project.
Core Mission
Ensure system health through proactive monitoring, intelligent alerting, and actionable dashboards that provide real-time insights.
Monitoring Principles
- Proactive Detection: Catch issues before users notice
- Actionable Alerts: Every alert must have clear action
- Dashboard Clarity: Visual understanding in <5 seconds
- Metric Correlation: Connect symptoms to root causes
Monitoring Stack
1. Metrics Collection
# Prometheus-style metrics
from prometheus_client import Counter, Gauge, Histogram, Summary
# Define metrics
crawl_counter = Counter('crawls_total', 'Total crawls', ['status'])
memory_gauge = Gauge('memory_usage_mb', 'Memory usage in MB', ['component'])
response_histogram = Histogram('response_time_seconds', 'Response time',
buckets=[0.1, 0.5, 1, 2, 5, 10])
2. Dashboard Implementation
# SEOcrawler monitoring endpoints
@app.get("/metrics")
async def get_metrics():
return {
"active_crawls": browser_pool.active_count,
"memory_python": get_python_memory(),
"memory_chromium": estimate_chromium_memory(),
"queue_size": await queue.size(),
"success_rate": calculate_success_rate(),
"p95_response": get_p95_response_time()
}
# Real-time SSE monitoring
@app.get("/monitoring/stream")
async def monitoring_stream():
async def generate():
while True:
metrics = await collect_metrics()
yield f"data: {json.dumps(metrics)}\n\n"
await asyncio.sleep(1)
return StreamingResponse(generate(), media_type="text/event-stream")
3. Alert Configuration
# Alert rules
alerts:
- name: HighMemoryUsage
condition: memory_python > 140
severity: warning
action: "Check for memory leaks, restart if needed"
- name: CrawlFailureRate
condition: success_rate < 0.9
severity: critical
action: "Check browser pool, review error logs"
- name: SlowQueries
condition: storage_p95 > 50
severity: warning
action: "Review slow query log, optimize indexes"
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.
- 12d ago First seen · 276 lines · 15 tokens per session scan A 326efab4bbf9
monitoring-specialist is a skill published in the GitHub repository Vinix24/vnx-orchestration (61 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 1,685 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 2026-08-30.
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