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 tomzx/agents --skill audit-observabilitygit clone --depth 1 https://github.com/tomzx/agentsWrote 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/tomzx/agents/audit-observability)<a href="https://agentmods.dev/skills/tomzx/agents/audit-observability"><img src="https://agentmods.dev/badge/skills/tomzx/agents/audit-observability/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/tomzx/agents/audit-observability"><img src="https://agentmods.dev/badge/skills/tomzx/agents/audit-observability.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 | $0.00020 | $0.01785 |
| Opus 5.5 | $0.00008 | $0.00714 |
| Sonnet 5.5 | $0.00004 | $0.00357 |
| Haiku 4.5 | $0.00002 | $0.00178 |
Grade A, and why
audit-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 22d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Observability
Scans the codebase and running services for missing or insufficient observability: logging, metrics, tracing, and alerting. Produces a gaps report ranked by risk so teams can prioritize adding instrumentation before issues arise in production.
Prerequisites
- Working directory is the root of the repository
- Optional:
$1— path or service name to scope the audit (defaults to.) - Access to the codebase to search for instrumentation patterns
- Read
.sdlc/context/architecture.mdto understand the services and infrastructure
Observability Pillars
| Pillar | Purpose | Examples |
|---|---|---|
| Logging | Record discrete events for debugging | Structured logs, error logs, audit logs |
| Metrics | Quantitative measurements over time | Request count, latency histogram, error rate |
| Tracing | Follow a request across service boundaries | Distributed trace IDs, span propagation |
| Alerting | Notify on-call when conditions degrade | PagerDuty, OpsGenie, Grafana alerts |
Steps
-
Read
.sdlc/context/architecture.mdto identify services, endpoints, databases, and external dependencies. -
Detect which observability libraries and frameworks are already in use:
rg -n -i "(structlog|logging|logger|prometheus|datadog|opentelemetry|otel|jaeger|zipkin|sentry)" \ -g '*.{py,ts,js,go}' ${1:-.} | head -40 -
Audit logging coverage:
- Are errors logged with context (request ID, user ID, stack trace)?
- Are external service calls logged (request/response, latency, status)?
- Are business-critical operations logged (auth events, data mutations, payments)?
- Is structured logging used consistently?
- Are log levels appropriate (not everything at INFO/DEBUG)?
rg -n -i "(except|catch|error|raise|throw)" \ -g '*.{py,ts,js,go}' ${1:-.} | rg -v "log|logger|sentry|capture" | head -30Flag error handlers that catch exceptions without logging them.
-
Audit metrics coverage:
- Are HTTP endpoints instrumented (request count, latency, error rate)?
- Are database queries instrumented (query latency, connection pool usage)?
- Are external service calls instrumented (call count, latency, error rate)?
- Are business metrics tracked (orders placed, emails sent, jobs completed)?
- Are resource metrics available (CPU, memory, disk, connections)?
rg -n -i "(counter|histogram|gauge|summary|metric|observe|inc\(|time\()" \ -g '*.{py,ts,js,go}' ${1:-.} | head -30Identify endpoints and services with no metrics instrumentation.
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.
- 22d ago Changed · +6 lines 7298213b2fc1
- 24d ago Changed bfcd7e8175e0
- 2mo ago First seen · 176 lines · 20 tokens per session scan A 344eb0e47849
audit-observability is a skill published in the GitHub repository tomzx/agents (13 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 1,785 once invoked, about $0.0001 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 2026-08-31.
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