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 Arize-ai/arize-skills --skill arize-instrumentationgit clone --depth 1 https://github.com/Arize-ai/arize-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/arize-ai/arize-skills/arize-instrumentation)<a href="https://agentmods.dev/skills/arize-ai/arize-skills/arize-instrumentation"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-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/arize-ai/arize-skills/arize-instrumentation"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-instrumentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 15 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00086 | $0.02331 |
| Opus 5 | $0.00043 | $0.01166 |
| Sonnet 5 | $0.00017 | $0.00466 |
| Haiku 4.5 | $0.00009 | $0.00233 |
Grade A, and why
arize-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 10d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Instrumentation Skill
Add Arize AX tracing to an app for the first time: detect the stack → fetch the one matching integration doc → wire auto-instrumentation → verify a trace lands.
Route locally. Map the detected stack to a single doc page via references/integration-routing.md (exhaustive for tracing integrations) and fetch only that page. When the app uses an agent framework, route on the framework, not the provider SDK it wraps — a bare openai/anthropic import inside a framework app is not the integration target; route to a provider page only when the app calls the provider SDK directly with no framework in play. If the stack isn't listed there, it has no dedicated integration — use manual instrumentation. Never bulk-fetch the PROMPT.md/llms.txt aggregates.
Rules: inspect before mutating; tracing is additive, never change business logic; follow existing style; no secrets in code and never ask the user to paste secrets (API keys, tokens) into the chat -- reference ARIZE_API_KEY/ARIZE_SPACE_ID env vars only, set by the user in their own .env/shell; preserve or ask for the app's Arize region/export endpoint instead of assuming US -- see references/regions-and-endpoints.md; ask before persistent local state (ax profiles, .zshrc, env vars) -- see references/ax-profiles.md.
Phase 1: Analysis (read-only — no code/files)
Detect from manifests + imports: language, package manager, LLM providers, frameworks, existing tracing (TracerProvider, register(), ARIZE_*/OTEL_*, Datadog/Honeycomb), existing Arize endpoint/region config (ARIZE_COLLECTOR_ENDPOINT, an in-code Arize endpoint option, or an OTEL_EXPORTER_OTLP_ENDPOINT confirmed to target Arize), and whether the app runs tools / an agent loop (manual CHAIN/TOOL spans only if the matched framework instrumentor doesn't already cover them — decided in Phase 2). Confirm scope first — a monorepo, multiple services, or multiple frameworks needs a "which one?" question before touching anything; don't pick for the user.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/ax-profiles.md 6.1 KB
- references/credentials-and-config.md 4.2 KB
- references/go.md 7.1 KB
- references/integration-routing.md 3.7 KB
- references/manual-spans.md 18 KB
- references/regions-and-endpoints.md 3.1 KB
- references/session-tracking.md 3.5 KB
- references/tracing-assistant-mcp.md 942 B
- references/verification.md 4.8 KB
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.
- 10d ago First seen · 54 lines · 86 tokens per session scan A 0dec4242ab83
arize-instrumentation is a skill published in the GitHub repository Arize-ai/arize-skills (50 stars, last pushed 3d ago), licensed MIT. It adds 86 tokens to every session and 2,331 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 2026-08-30.
Other skills, from other repositories
trulens-evaluation-setup
Configure feedback functions and selectors for TruLens evaluations.
trulens-instrumentation
Instrument LLM apps with TruLens OTEL-based tracing - from setup to debugging and optimization.
trulens-evaluation-workflow
Systematically evaluate your LLM application with TruLens.
trulens-blocking-guardrails
Configure and use feedback functions as runtime blocking guardrails.
trulens-dataset-curation
Create and curate evaluation datasets with ground truth for TruLens.
annotate-spans
Write effective, consistent annotations on LLM/agent spans and traces, and coach the user on annotation practice. Load this whenever you are about to record structured feedback with the ui.spans.annotate operation (via executebrowseraction), or when the user asks how to annotate, label, score, or review spans/traces…