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/prabhdeepsingh/claude-plugins/observabilitynpx skills add PrabhdeepSingh/claude-plugins --skill observabilitygit clone --depth 1 https://github.com/PrabhdeepSingh/claude-pluginsWrote 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/prabhdeepsingh/claude-plugins/observability)<a href="https://agentmods.dev/skills/prabhdeepsingh/claude-plugins/observability"><img src="https://agentmods.dev/badge/skills/prabhdeepsingh/claude-plugins/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 | $0.00088 | $0.02394 |
| Opus 5 | $0.00044 | $0.01197 |
| Sonnet 5 | $0.00018 | $0.00479 |
| Haiku 4.5 | $0.00009 | $0.00239 |
Grade A, and why
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 3d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability — instrument for the question you'll ask at 2am
The [[debugging]] skill's first production move is "pull the real event." That move only works if someone instrumented the service before it broke — and that someone is you, now, while writing it. You can't debug what you didn't record, and you can't retrofit telemetry onto an incident that already happened. The bar: every service answers four questions without anyone SSHing anywhere — is it up, is it erroring, is it slow, and for whom?
How to apply this
Instrumentation is part of the feature, not a follow-up: a new endpoint ships with its metrics, error capture, and trace context wired, the same way it ships with tests. Run the self-check before calling any service change done.
Define "working" before instrumenting. Section 1's four questions are the per-operation floor every service owes; on top of them, write down 2–4 questions an on-call engineer will ask about this feature specifically ("is the retry actually recovering payments?", "which provider is slow?") — every signal you add beyond the floor must help answer one. Telemetry without a question is noise: if you can't name the questions, you'll log everything and learn nothing. The division of labor, as a rule of thumb: metrics tell you that something is wrong, traces tell you where, logs tell you why.
1. The four questions drive what you instrument
For every operation a service performs (each endpoint, each job, each consumer), emit enough to answer:
- Traffic — how often is this happening? (a counter)
- Errors — how often is it failing, split by kind? (a counter with a failure label)
- Latency — how long does it take, as a distribution? (a histogram — averages hide the slow tail where users actually live)
- Saturation — for resources (pools, queues, memory): how close to full? (a gauge)
If an operation emits these four, most incidents are diagnosable from a dashboard. If it emits none, every incident starts with "let's add logging and wait for it to happen again" — the most expensive sentence in operations.
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.
- 3d ago First seen · 96 lines · 88 tokens per session scan A 69b92fff3753
observability is a skill published in the GitHub repository PrabhdeepSingh/claude-plugins (3 stars, last pushed 4d ago), licensed MIT. It adds 88 tokens to every session and 2,394 once invoked, about $0.0004 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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