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/junmystery/agent-guidance-python/observability-and-instrumentationnpx skills add JunMystery/Agent-Guidance-Python --skill observability-and-instrumentationgit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWrote 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/junmystery/agent-guidance-python/observability-and-instrumentation)<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/observability-and-instrumentation"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/observability-and-instrumentation.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 | $0.00066 | $0.02493 |
| Opus 5 | $0.00033 | $0.01247 |
| Sonnet 5 | $0.00013 | $0.00499 |
| Haiku 4.5 | $0.00007 | $0.00249 |
Grade A, and why
observability-and-instrumentation 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 2d 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.
This is a copy
98% identical to observability-and-instrumentation — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability and Instrumentation
Overview
Code you can't observe is code you can't operate. Observability is the ability to answer "what is the system doing and why?" from the outside, using the telemetry the code emits. Instrumentation is not a post-launch add-on — it's written alongside the feature, the same way tests are. If a feature ships without telemetry, the first user-reported bug becomes archaeology instead of a query.
When to Use
- Building any feature that will run in production
- Adding a new service, endpoint, background job, or external integration
- A production incident took too long to diagnose ("we couldn't tell what happened")
- Setting up or reviewing alerting rules
- Reviewing a PR that adds I/O, retries, queues, or cross-service calls
NOT for:
- Diagnosing a failure happening right now — use the
debugging-and-error-recoveryskill (observability is what makes that skill fast next time) - Profiling and optimizing measured slowness — use the
performance-optimizationskill - Launch-day monitoring checklists and rollback triggers — see the
shipping-and-launchskill; this skill covers the instrumentation that feeds them
Process
1. Define "working" before instrumenting
Telemetry without a question is noise. Before adding any instrumentation, write down 2–4 questions an on-call engineer will ask about this feature:
FEATURE: checkout payment retry
QUESTIONS ON-CALL WILL ASK:
1. What fraction of payments succeed on first attempt vs after retry?
2. When a payment fails permanently, why? (provider error? timeout? validation?)
3. Is the payment provider slower than usual?
→ Every signal below must help answer one of these.
If you can't name the questions, you're not ready to instrument — you'll log everything and learn nothing.
2. Pick the right signal for each question
| Signal | Answers | Cost profile | Example |
|---|---|---|---|
| Structured log | "What happened in this specific case?" | Per-event; grows with traffic | payment_failed with provider error code |
| Metric | "How often / how fast, in aggregate?" | Fixed per series; cheap to query | p99 latency of provider calls |
| Trace | "Where did time go across services?" | Per-request; usually sampled | One slow checkout, broken down by hop |
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.
- 2d ago First seen · 202 lines · 66 tokens per session scan A 0f6b1602d65e
observability-and-instrumentation is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 2,493 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to observability-and-instrumentation, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
chrome-devtools-cli
Use this skill to write shell scripts or run shell commands to automate tasks in the browser or otherwise use Chrome DevTools via CLI.
chrome-devtools
Uses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to --slim mode (MCP configuration).
n8n-validation-expert
Interpret validation errors and guide fixing them. Use when encountering validation errors, validation warnings, false positives, operator structure issues, or need help understanding validation results. Also use when asking about validation profiles, error types, the validation loop process, or auto-fix capabilities.…
agentcore-investigation
Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session/trace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
memory-leak-debugging
Diagnoses and resolves memory leaks in JavaScript/Node.js applications. Use when a user reports high memory usage, OOM errors, or wants to capture, compare, or inspect heap snapshots with Chrome DevTools MCP memory tools.