addyosmani/agent-skills is a collection of reusable workflows, quality checks, commands, and other instructions that guide AI coding agents through software development. It is for developers who want agents to follow consistent engineering practices, and the catalogue entries are its packaged skills, commands, agents, plugins, instructions, and hooks.
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 addyosmani/agent-skills --skill observability-and-instrumentationgit clone --depth 1 https://github.com/addyosmani/agent-skillsWrote 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/addyosmani/agent-skills/observability-and-instrumentation)<a href="https://agentmods.dev/skills/addyosmani/agent-skills/observability-and-instrumentation"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/observability-and-instrumentation/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/addyosmani/agent-skills/observability-and-instrumentation"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/observability-and-instrumentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00066 | $0.03160 |
| Opus 5.5 | $0.00026 | $0.01264 |
| Sonnet 5.5 | $0.00013 | $0.00632 |
| Haiku 4.5 | $0.00007 | $0.00316 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- observability-and-instrumentation — 100% identical, 35 lines differ
- observability-and-instrumentation — 100% identical, 37 lines differ
- observability-and-instrumentation — 100% identical, 37 lines differ
- observability-and-instrumentation — 100% identical, 37 lines differ
- observability-and-instrumentation — 100% identical, 37 lines differ
- observability-and-instrumentation — 100% identical, 37 lines differ
- observability-and-instrumentation — 100% identical, 37 lines differ
- observability-and-instrumentation — 98% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 239 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.
- 1mo ago Changed · +18 lines e7fcb0820306
- 1mo ago Changed · +17 lines 3170fdbfe029
- 1mo ago First seen · 204 lines · 66 tokens per session scan A bcec2ada212d
observability-and-instrumentation is a skill published in the GitHub repository addyosmani/agent-skills (111,152 stars, last pushed 14d ago), licensed MIT. It adds 66 tokens to every session and 3,160 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
think-before-coding
Forces explicit reasoning before writing any code. Surfaces assumptions, manages confusion, and prevents hallucination by demanding clarity upfront.
git-workflow
Trunk-based development with atomic commits, clean history, and meaningful commit messages. Every commit should be deployable.
idea-to-spec
Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec.
meeting-notes-to-tasks
Converts unstructured meeting notes into structured, assigned, time-bounded action items. Never leave a meeting without knowing who does what by when.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.