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 rules/d-padmanabhan/agent-engineering-handbook/330-observabilitygit clone --depth 1 https://github.com/d-padmanabhan/agent-engineering-handbookWrote 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/rules/d-padmanabhan/agent-engineering-handbook/330-observability)<a href="https://agentmods.dev/rules/d-padmanabhan/agent-engineering-handbook/330-observability"><img src="https://agentmods.dev/badge/rules/d-padmanabhan/agent-engineering-handbook/330-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 | $0.00000 | $0.01055 |
| Opus 5 | $0.00000 | $0.00528 |
| Sonnet 5 | $0.00000 | $0.00211 |
| Haiku 4.5 | $0.00000 | $0.00105 |
Grade A, and why
330-observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Engineering Gates
This rule owns mandatory policy only. Use the observability skill
(${HANDBOOK_ROOT}/skills/observability/SKILL.md) for signal selection,
schemas, SLOs, dashboards, OpenTelemetry, collector design, and verification.
Signal Design
- Start from critical user journeys, operational questions, SLOs, and failure modes. Do not add logs, metrics, or spans solely to satisfy a checklist.
- Define ownership, schema, units, cardinality, sensitivity, retention, cost, and missing-data behavior for each production signal.
- Correlate signals with valid propagated trace and operation context. Never treat a trace, request, or correlation identifier as authorization evidence.
Structured Logging
- Emit structured events through an encoder. For ordinary containers, write
one JSON object per physical line to
stdoutorstderrand let the platform collect and rotate logs. - Use stable event names and typed fields. Do not make automation parse human-readable messages.
- Never log credentials, tokens, authorization headers, cookies, private keys, secret values, raw request or response bodies, or unrestricted exception context.
- Minimize personal and tenant data. Apply explicit classification, purpose, access, retention, and deletion controls when such data is necessary.
- Bound event size, collections, stack traces, and caller-controlled fields. Encode newlines and control characters so values cannot forge log records.
Metrics, SLOs, and Alerts
- Use bounded metric labels. Never use user, account, tenant, session, request, trace, email, IP address, raw URL, query, exception, timestamp, or UUID values as labels without a reviewed finite-domain guarantee.
- Define metric type, unit, monotonicity, label values, and worst-case series count. Monitor cardinality and ingestion cost.
- Define SLI population, good events, denominator, exclusions, window, missing-data behavior, and data source before setting an SLO.
- Page only for urgent, actionable user impact or imminent error-budget exhaustion. Every alert requires an owner, runbook, routing, recovery condition, and test.
- Prefer SLO burn-rate alerts for user-facing reliability. Use resource and dependency alerts for diagnosis or proven capacity risk.
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.
- 5d ago First seen · 117 lines · 0 tokens per session scan A a9b531a5195e
330-observability is a cursor rule published in the GitHub repository d-padmanabhan/agent-engineering-handbook (16 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,055 tokens. 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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