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 borhen68/SkillEngine --skill observability-and-instrumentationgit clone --depth 1 https://github.com/borhen68/SkillEngineWrote 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/borhen68/skillengine/observability-and-instrumentation)<a href="https://agentmods.dev/skills/borhen68/skillengine/observability-and-instrumentation"><img src="https://agentmods.dev/badge/skills/borhen68/skillengine/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 | $0.02771 |
| Opus 5 | $0.00033 | $0.01385 |
| Sonnet 5 | $0.00013 | $0.00554 |
| Haiku 4.5 | $0.00007 | $0.00277 |
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 8d 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
86% identical to observability-and-instrumentation — 53 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability and Instrumentation
Overview
When production breaks at 3 AM, the engineer on call doesn't have time to read your code. They need to know — in seconds — what the system is doing, where it's failing, and why. Without observability, debugging becomes archaeology: digging through logs hoping to find a clue. With observability, it's diagnosis: the system tells you what's wrong.
The observability contract: Every feature that runs in production must emit enough telemetry to answer "what is the system doing and why?" without reading code. Logs for narrative, metrics for aggregates, traces for causality. Instrumentation is not a post-launch add-on — it's written alongside the feature, the same way tests are.
Real-world impact: The median time to resolve an incident with good observability is 15 minutes. Without it, it's 4+ hours. When users are losing money or data, that difference isn't academic — it's the difference between a brief hiccup and a news headline.
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.
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.
- 8d ago First seen · 233 lines · 66 tokens per session scan A 17b18bce8319
observability-and-instrumentation is a skill published in the GitHub repository borhen68/SkillEngine (17 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 2,771 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to observability-and-instrumentation, differing in 53 lines, and is treated as a copy.
Other skills, from other repositories
debugging-and-error-recovery
A systematic debugging procedure for finding the underlying cause of failed tests, broken builds, bugs, and unexpected behavior. It emphasizes reproducing the problem, preserving evidence, fixing the cause, and checking the fix.
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now…
debugging-and-error-recovery
Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.
error-handling
Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.
code-review-and-quality
A code-review guide for checking changes across correctness, readability, architecture, security, and performance before they are merged.