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 skills add vignesh2027/AI-AGENT-SKILLS --skill observability-and-monitoringgit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-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/skills/vignesh2027/ai-agent-skills/observability-and-monitoring)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/observability-and-monitoring"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/observability-and-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/vignesh2027/ai-agent-skills/observability-and-monitoring"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/observability-and-monitoring.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00024 | $0.00886 |
| Opus 5 | $0.00012 | $0.00443 |
| Sonnet 5 | $0.00005 | $0.00177 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
observability-and-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 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
A system you cannot observe is a system you cannot debug. Observability is not a dashboard — it is the ability to answer any question about system behavior using existing data, without deploying new code. This skill implements the three pillars (logs, metrics, traces) and the alerts that page you before users notice.
When to Use
- Before deploying any new service or significant feature
- When a production issue took more than 30 minutes to diagnose
- When adding a new data flow, user journey, or integration
- As part of the
/shipworkflow
Process
Step 1: Define the SLIs and SLOs
SLI (Service Level Indicator): what you measure — latency, error rate, availability, throughput. SLO (Service Level Objective): the target — "p99 latency < 500ms," "error rate < 0.1%."
Without SLOs, you don't know when to alert.
Step 2: Structured logging
- Log in JSON (not freeform text)
- Include: timestamp, log level, service name, trace ID, user ID (hashed), request ID
- Log at entry and exit of every significant operation
- Log errors with: exception type, message, stack trace, context
- Never log PII, passwords, tokens, or credit card numbers
- Log what happened, not just that it happened
Step 3: Key metrics
Implement the four golden signals for every service:
- Latency — p50, p95, p99 response time
- Traffic — requests per second, events per second
- Errors — error rate (4xx, 5xx), error count
- Saturation — CPU, memory, queue depth, connection pool
Add business metrics: active users, signups, payments, key conversions.
Step 4: Distributed tracing
- Assign a trace ID to every inbound request
- Propagate the trace ID to all downstream calls (outbound HTTP, database, queue)
- Use OpenTelemetry or compatible SDK
- Sample at 100% for errors; sample at 1–10% for success paths
Step 5: Health checks and readiness probes
/healthz— is the service alive?/readyz— is the service ready to accept traffic?- Health checks must not perform significant work (no database writes)
- Check downstream dependencies in readiness probe
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 · 98 lines · 24 tokens per session scan A 71ec1ae61d42
observability-and-monitoring is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (1 stars, last pushed 14d ago), licensed MIT. It adds 24 tokens to every session and 886 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 2026-08-31.
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