Opik is an open-source platform for tracing, evaluating, and monitoring applications that use large language models, retrieval systems, or AI agents. Teams use it to inspect agent activity, manage prompts, run evaluations, and monitor these applications in production.
Borrowing it
Nothing to install: this file belongs to comet-ml/opik. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/comet-ml/opik/main/.agents/skills/opik-integrations/SKILL.mdgit clone --depth 1 https://github.com/comet-ml/opikWrote 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/comet-ml/opik/opik-integrations)<a href="https://agentmods.dev/skills/comet-ml/opik/opik-integrations"><img src="https://agentmods.dev/badge/skills/comet-ml/opik/opik-integrations/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/comet-ml/opik/opik-integrations"><img src="https://agentmods.dev/badge/skills/comet-ml/opik/opik-integrations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00067 | $0.01327 |
| Opus 5.5 | $0.00027 | $0.00531 |
| Sonnet 5.5 | $0.00013 | $0.00265 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
opik-integrations 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 2mo 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opik SDK Integrations
This skill is for building integrations into the Opik SDK itself — the code that ships inside opik / opik-* packages so that users can trace a framework (OpenAI, LangChain, Mistral, …) with one call.
Do not confuse this with the user-facing
instrument/opikskills, which add Opik tracing to someone else's application. This skill is for SDK contributors editingsdks/pythonandsdks/typescript.If the integration lives outside this repo — a standalone
opik-*package, or Opik support contributed into a third-party project (LiteLLM, Dify, a plugin, …) — use theopik-external-integrationsskill instead. This skill assumes the code ships insidesdks/.
Start with the questionnaire
Never assume or suggest a target. Collect, from the user, before doing anything: what to integrate (name + reference links), where it lives (this repo vs. external — route external requests to opik-external-integrations), language (python/typescript/both), mode (new/update/maintain), and any specific flows to cover. Do not present a menu of candidate libraries — the user names the target.
When to use
- New integration — a framework/provider has no dedicated integration yet (today it's reachable only via LiteLLM, the OpenAI-compatible shim, or OpenTelemetry, or not at all).
- Update — an integration must track new methods, capture new fields, or follow an upstream SDK change.
- Maintain / verify — confirm an existing integration still logs the correct trace/span tree after a dependency bump or refactor.
The workflow
Integration work is multi-step. By default this skill runs autonomously: it makes its own preparations (deps, credentials, backend), runs every phase, self-verifies, and ends with a high-level report — only stopping early on a true blocker. Ask for the interactive variant if you want to approve the design before any code is written. The full playbook — phases, execution modes, the Opik-MCP verification loop, and the report template — lives in workflow.md. At a glance:
What ships with it
3 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.
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.
- 2mo ago First seen · 63 lines · 67 tokens per session scan A 7dcaf3aeace4
opik-integrations is a skill published in the GitHub repository comet-ml/opik (22,998 stars, last pushed today), licensed Apache-2.0. It adds 67 tokens to every session and 1,327 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.
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