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Agent skill

Opik SDK Integrations

by comet-ml in comet-ml/opik

Builds, updates and verifies integrations inside the Opik Python and TypeScript SDKs so users can trace a framework or provider with one call.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Opik SDK Integrations

skills CLI
$ npx skills add comet-ml/opik --skill opik-integrations -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install comet-ml/opik opik-integrations --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/comet-ml/opik.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/opik-integrations .claude/skills/opik-integrations && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
opik-integrations
GitHub stars
22k
Token cost
~1.4k tokens
SKILL.md length
692 words
Files
4
Skills in repo
19
Repo updated
First seen
Licence
Apache-2.0

At a glance

Builds, updates and verifies integrations inside the Opik Python and TypeScript SDKs so users can trace a framework or provider with one call.

  • Works in 9 steps: Prepare — install/resolve the target… → Investigate the target library (API… → Collect findings + a minimal runnable… → …
  • Adding a new framework or provider integration to the Opik SDK
  • SKILL.md covers Start with the questionnaire, When to use, The workflow and Golden rule: clone the closest…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

This skill is for contributors editing the Opik SDK itself, the code in sdks/python and sdks/typescript that lets users trace frameworks such as OpenAI, LangChain or Mistral with one call. It is distinct from the skills that add Opik tracing to someone else's application, and integrations that live outside the repo go to opik-external-integrations.

It begins with a questionnaire and never suggests a target: what to integrate with reference links, where it lives, the language (Python, TypeScript or both), the mode (new, update or maintain) and any specific flows to cover. The modes cover a framework with no dedicated integration yet, updates that follow new methods, fields or upstream changes, and verifying that the trace and span tree is still right after a dependency bump or refactor.

By default the work runs autonomously: it prepares dependencies, credentials and a backend, investigates the target library's API surface, hooks, streaming shape, usage format and errors, collects findings and a minimal example script, verifies itself, and ends with a high-level report, stopping early only on a true blocker. An interactive variant lets you approve the design first. Guides for Python and TypeScript and a workflow file with the Opik-MCP verification loop are included.

When your agent uses it

  • Adding a new framework or provider integration to the Opik SDK
  • Updating an integration to capture new fields or follow an upstream change
  • Verifying an integration still logs the correct trace and span tree after a dependency bump
  • Documenting and testing an SDK integration in Python or TypeScript

Example prompts

  • “Add an Opik integration for the Groq Python client to the Python SDK.”
  • “Check that the LangChain integration still logs the right span tree after the dependency update.”
  • “Update the OpenAI integration to capture the new usage fields, and show me the design first.”
  • “Build a TypeScript integration for a new provider SDK and write the tests.”

Requirements

  • A checkout of the Opik repository with its Python and TypeScript SDKs
  • Credentials for the target library and a backend the Opik MCP can read

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Prepare — install/resolve the target library, locate credentials (without printing them), pick a backend the MCP can read.
  2. Investigate the target library (API surface, hooks/callbacks, streaming shape, usage/token format, errors).
  3. Collect findings + a minimal runnable example script.
  4. Design — pick the pattern, file layout, entrypoint. (Interactive mode pauses for approval here; autonomous mode records it in the report.)
  5. Implement by cloning the closest existing same-pattern integration.
  6. Verify the logged data through the Opik MCP (read/list the trace & spans).
  7. Test with the language's integration-test harness.
  8. Document the Fern page and wire its routing.
  9. Report — a high-level summary: what was done, what's supported (with evidence), what's not, and how to use it.

What it can do on your machine

Read from SKILL.md and the folder at commit f217a86. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Opik SDK Integrations loads about 1.4k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 692 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from comet-ml/opik at commit f217a86, republished under its Apache-2.0 licence (© comet-ml). 692 words, ~1,427 tokens.

Download SKILL.mdSave it as .claude/skills/opik-integrations/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
opik-integrations
description
Build, update, test, and document Opik SDK integrations (Python & TypeScript). Use when adding a new framework/provider integration under sdks/python/src/opik/integrations or sdks/typescript/src/opik/integrations, updating an existing one, or verifying that an integration logs traces correctly.

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 / opik skills, which add Opik tracing to someone else's application. This skill is for SDK contributors editing sdks/python and sdks/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 the opik-external-integrations skill instead. This skill assumes the code ships inside sdks/.

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:

  1. Prepare — install/resolve the target library, locate credentials (without printing them), pick a backend the MCP can read.
  2. Investigate the target library (API surface, hooks/callbacks, streaming shape, usage/token format, errors).
  3. Collect findings + a minimal runnable example script.
  4. Design — pick the pattern, file layout, entrypoint. (Interactive mode pauses for approval here; autonomous mode records it in the report.)
  5. Implement by cloning the closest existing same-pattern integration.
  6. Verify the logged data through the Opik MCP (read/list the trace & spans).
  7. Test with the language's integration-test harness.
  8. Document the Fern page and wire its routing.
  9. Report — a high-level summary: what was done, what's supported (with evidence), what's not, and how to use it.
Show full SKILL.md (283 more words)Show less

Golden rule: clone the closest sibling

Never build an integration from a blank file. Identify the existing integration that shares the target's mechanism, copy its structure, and adapt. The decision tree:

Target shapePython patternTS patternClone from
SDK client with methods to wrap (most providers)Method patching (BaseTrackDecorator subclass)Proxy wrapperopenai/ · opik-openai
Framework with a callback/tracer interfacePure callback (BaseTracer)Callback handlerlangchain/ · opik-langchain
Framework already emitting OpenTelemetry spansOTelOTel exporterotel/ · opik-vercel
Callbacks exist but are unreliable / need method hooks tooHybrid(rare)adk/

If the target exposes an OpenAI-compatible endpoint, first check whether track_openai(..., provider=...) already covers the need before building a dedicated integration — sometimes the right answer is a docs page, not new code.

OpenTelemetry is backend-first. If the target already emits OpenTelemetry spans, the heavy lifting is done by Opik's OTLP ingestion endpoint on the backend — many such integrations are docs-only (point the framework's OTLP exporter at Opik with auth headers; no SDK code). Build a client-side piece only when you must shape what the backend receives — set Opik semantics, remap attributes, or bridge a framework that won't export raw OTLP. The client-side building block is a SpanProcessor in Python (integrations/otel/) or a SpanExporter in TypeScript (opik-vercel); a framework-specific OTel tracer wrapper (adk/patchers/adk_otel_tracer/) is the heavier variant. See the OTel sections in python.md / typescript.md.

Language references

  • Python → python.md — integration anatomy, shared core modules, mechanism templates, dependency/import rules, test specifics.
  • TypeScript → typescript.md — package anatomy, patterns, build/peer-dep rules. Delegates to the canonical sdks/typescript/design/INTEGRATIONS.md.

Skills this one builds on (do not duplicate them)

  • python-sdk — three-layer architecture, batching, fake_backend, testlib verifiers, error handling.
  • typescript-sdk — layered client, flush semantics, testing with vitest.
  • write-docs — Fern MDX authoring, routing YAML, callouts, images.

© comet-ml, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files in .agents/skills/opik-integrations of comet-ml/opik.

  • SKILL.md
  • python.md
  • typescript.md
  • workflow.md

Open the folder on GitHubat commit f217a86

Compare with similar skills

Opik SDK Integrations next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Opik SDK Integrations compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Opik SDK Integrations this skillcomet-ml/opik22k—~1.4kAutomated safety check: PassApache-2.0
Failproof AI SDK IntegrationFailproofAI/failproofai5.3k—~6kAutomated safety check: PassCustom licence
Langchain Dependencieslangchain-ai/langchain-skills1.3k1 repos~3.6kAutomated safety check: PassMIT
Phoenix Tracinggithub/awesome-copilot40k3 repos~1.6kAutomated safety check: PassApache-2.0
Phoenix Integration SnippetsArize-ai/phoenix12k—~1.4kAutomated safety check: PassApache-2.0
Phoenix Skills AuditArize-ai/phoenix12k—~5.1kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Opik SDK Integrations

What does Opik SDK Integrations do?

Builds, updates and verifies integrations inside the Opik Python and TypeScript SDKs so users can trace a framework or provider with one call. This skill is for contributors editing the Opik SDK itself, the code in sdks/python and sdks/typescript that lets users trace frameworks such as OpenAI, LangChain or Mistral with one call. It is distinct from the skills that add Opik tracing to someone else's application, and integrations that live outside the repo go to opik-external-integrations.

When should I use Opik SDK Integrations?

Opik SDK Integrations fits situations like: adding a new framework or provider integration to the Opik SDK; updating an integration to capture new fields or follow an upstream change; verifying an integration still logs the correct trace and span tree after a dependency bump; documenting and testing an SDK integration in Python or TypeScript.

How do I install Opik SDK Integrations in Claude Code?

Run `npx skills add comet-ml/opik --skill opik-integrations -a claude-code`. Or copy the skill folder (.agents/skills/opik-integrations in comet-ml/opik) into .claude/skills/opik-integrations in your project. Claude Code loads it when a task matches its description.

How do I install Opik SDK Integrations in Codex?

Run `npx skills add comet-ml/opik --skill opik-integrations -a codex`. Or copy the skill folder (.agents/skills/opik-integrations in comet-ml/opik) into .agents/skills/opik-integrations in your project. Codex loads it when a task matches its description.

Can I use Opik SDK Integrations in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add comet-ml/opik --skill opik-integrations -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/opik-integrations, .gemini/skills/opik-integrations, .github/skills/opik-integrations and .opencode/skills/opik-integrations in your project.

What does Opik SDK Integrations need to run?

SKILL.md names no scripts, command-line tools or credentials: Opik SDK Integrations is instructions for the agent only. Our summary lists: A checkout of the Opik repository with its Python and TypeScript SDKs; Credentials for the target library and a backend the Opik MCP can read.

Does Opik SDK Integrations access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Opik SDK Integrations safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Opik SDK Integrations use?

Opik SDK Integrations is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Opik SDK Integrations use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Opik SDK Integrations?

Skills that share tags, products or a category with Opik SDK Integrations: Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars), Langchain Dependencies (langchain-ai/langchain-skills, 1.3k stars), Phoenix Tracing (github/awesome-copilot, 40k stars) and Phoenix Integration Snippets (Arize-ai/phoenix, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Opik SDK Integrations?

comet-ml (a GitHub organization) maintains it in comet-ml/opik, which has 22,412 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 7, 2026.

Source: comet-ml/opik on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.