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 skills/komluk/scaffolding/monitoring-observabilitynpx skills add komluk/scaffolding --skill monitoring-observabilitygit clone --depth 1 https://github.com/komluk/scaffoldingWrote 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/komluk/scaffolding/monitoring-observability)<a href="https://agentmods.dev/skills/komluk/scaffolding/monitoring-observability"><img src="https://agentmods.dev/badge/skills/komluk/scaffolding/monitoring-observability.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 5.1 | $0.00056 | $0.01644 |
| Opus 5 | $0.00028 | $0.00822 |
| Sonnet 5 | $0.00011 | $0.00329 |
| Haiku 4.5 | $0.00006 | $0.00164 |
Grade A, and why
monitoring-observability 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 6d 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitoring & Observability Skill
Purpose
Standards for monitoring, metrics, alerting, and observability.
Auto-Invoke Triggers
- Setting up monitoring infrastructure
- Defining metrics and KPIs
- Configuring alerts
- Implementing distributed tracing
Three Pillars of Observability
| Pillar | Purpose | Tools |
|---|---|---|
| Logs | Event records | ELK, Loki, CloudWatch |
| Metrics | Numerical measurements | Prometheus, Datadog |
| Traces | Request flow | Jaeger, Zipkin, X-Ray |
Key Metrics (Golden Signals)
The Four Golden Signals
| Signal | Description | Example Metric |
|---|---|---|
| Latency | Response time | p50, p95, p99 latency |
| Traffic | Request volume | Requests per second |
| Errors | Failure rate | Error percentage |
| Saturation | Resource usage | CPU, memory utilization |
RED Method (Request-focused)
- Rate - Requests per second
- Errors - Failed requests per second
- Duration - Request latency
USE Method (Resource-focused)
- Utilization - Resource % used
- Saturation - Queue depth
- Errors - Error count
Application Metrics
HTTP Endpoints
| Metric | Type | Description |
|---|---|---|
http_requests_total |
Counter | Total requests |
http_request_duration_seconds |
Histogram | Request latency |
http_requests_in_flight |
Gauge | Active requests |
http_response_size_bytes |
Histogram | Response size |
Database
| Metric | Type | Description |
|---|---|---|
db_connections_active |
Gauge | Active connections |
db_query_duration_seconds |
Histogram | Query time |
db_errors_total |
Counter | Query errors |
Business Metrics
| Metric | Type | Description |
|---|---|---|
users_registered_total |
Counter | New registrations |
orders_created_total |
Counter | Orders placed |
payment_amount_total |
Counter | Revenue |
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.
- 6d ago First seen · 285 lines · 56 tokens per session scan A b13bbc37247c
monitoring-observability is a skill published in the GitHub repository komluk/scaffolding (15 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,644 once invoked, about $0.0003 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.
Other skills, from other repositories
seo-strategy
Audits and improves organic search performance — technical health, site architecture, internal linking, structured data, and the content decisions that determine what can rank. Use this to run an SEO audit, diagnose why pages are not ranking or were deindexed, plan a site's URL and navigation structure, add structured…
financial-statement-analysis
Reads a set of financial statements and establishes what changed and why — fluctuation analysis against prior period and against budget, profitability, liquidity, solvency and efficiency ratios, benchmarking, and the non-GAAP measures presented alongside them. Use this to interpret results, review a counterparty's or…
youtube-producer
Plans, packages, and scripts long-form video for retention and channel growth — idea selection, titles and thumbnails, script structure, and diagnosing why a video or channel underperforms. Use this for video ideas, packaging, scripting, a retention teardown, or channel strategy — including when someone describes a…
support-operations
Designs and runs the support function — channels, queues, routing, staffing, service levels, quality, and the metrics that show whether it is working. Use this to set up or fix support operations, choose channels, size a team, set or renegotiate service levels, reduce cost per contact, diagnose long queues or poor…
ai-ml-governance
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory…
paid-advertising
Plans, runs, and optimizes paid acquisition across search, social, and display — account structure, targeting, creative, bidding, budget, and the analysis that says whether to scale or stop. Use this to set up or restructure campaigns, write and iterate ad creative, diagnose rising costs or falling performance, decide…