Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jnPiyush/AgentXnpx agentmods add skills/jnpiyush/agentx/logging-monitoringWrote 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/jnpiyush/agentx/logging-monitoring)<a href="https://agentmods.dev/skills/jnpiyush/agentx/logging-monitoring"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/logging-monitoring/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/jnpiyush/agentx/logging-monitoring"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/logging-monitoring.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 | $0.00052 | $0.01423 |
| Opus 5.5 | $0.00021 | $0.00569 |
| Sonnet 5.5 | $0.00010 | $0.00285 |
| Haiku 4.5 | $0.00005 | $0.00142 |
Grade A, and why
logging-monitoring 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logging & Monitoring
Purpose: Implement observability for production systems. Goal: Structured logs, correlation across requests, actionable metrics. Note: For implementation, see C# Development or Python Development.
When to Use This Skill
- Adding structured logging to applications
- Implementing request correlation IDs
- Configuring metrics collection
- Setting up distributed tracing (OpenTelemetry)
- Designing alerting rules and health checks
Prerequisites
- Logging framework installed
- Monitoring platform access
Decision Tree
Observability concern?
+- What to log?
| +- Request start/end -> INFO with correlation ID
| +- Expected errors -> WARN (validation, not-found)
| +- Unexpected errors -> ERROR with stack trace
| - Debug details -> DEBUG (disabled in production)
+- What NOT to log?
| - PII, passwords, tokens, credit cards -> NEVER
+- Metrics needed?
| +- RED metrics: Rate, Errors, Duration (for services)
| - USE metrics: Utilization, Saturation, Errors (for resources)
+- Distributed tracing?
| - OpenTelemetry for cross-service correlation
- Alerting?
+- SLO-based: alert on error budget burn rate
- Avoid alert fatigue: page only for actionable issues
Structured Logging
Concept
Log structured data (key-value pairs) instead of plain text for better searchability and analysis.
[FAIL] Unstructured (hard to parse):
"User [email protected] logged in from 192.168.1.1 at 2024-01-15 10:30:00"
[PASS] Structured (machine-readable):
{
"event": "user_login",
"user_email": "[email protected]",
"ip_address": "192.168.1.1",
"timestamp": "2024-01-15T10:30:00Z",
"level": "INFO"
}
Benefits
- Searchable: Query by any field
- Filterable: Show only errors, specific users, etc.
- Aggregatable: Count events, calculate averages
- Parseable: Tools can process automatically
Log Levels
Standard Levels
| Level | When to Use | Example |
|---|---|---|
| TRACE | Very detailed debugging | "Entering function with params: {x: 1, y: 2}" |
| DEBUG | Debugging information | "Cache hit for key: user_123" |
| INFO | Normal operations | "User logged in", "Order created" |
| WARN | Unexpected but recoverable | "Retry attempt 2 of 3", "Rate limit approaching" |
| ERROR | Failures requiring attention | "Payment failed", "Database connection lost" |
| FATAL | Application cannot continue | "Out of memory", "Configuration invalid" |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed 2c3a8c41e267
- 1mo ago First seen · 162 lines · 52 tokens per session scan A 509efcf53fab
logging-monitoring is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 52 tokens to every session and 1,423 once invoked, about $0.0002 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 2026-09-03.
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