Agent skill

Opik Analytics Instrumentation

by comet-ml in comet-ml/opik

Shows how to add product analytics events to Opik's frontend, Java backend and Python SDK, all reporting through Segment to PostHog with an opik_ name prefix.

Apache-2.0Auto-check passedData & Analytics

Install Opik Analytics Instrumentation

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

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

GitHub CLI
$ gh skill install comet-ml/opik analytics-instrumentation --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/analytics-instrumentation .claude/skills/analytics-instrumentation && 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
analytics-instrumentation
GitHub stars
22k
Token cost
~4.4k tokens
SKILL.md length
1,922 words
Files
1
Skills in repo
19
Repo updated
First seen
Licence
Apache-2.0

At a glance

Shows how to add product analytics events to Opik's frontend, Java backend and Python SDK, all reporting through Segment to PostHog with an opik_ name prefix.

  • Works in 2 steps: Add the event name to the OpikEvent… → Call trackEvent from the component or…
  • Wiring a new product analytics event into an Opik frontend feature
  • SKILL.md covers Event Naming, Frontend Events, Backend Events and Python SDK Events, plus 4 more sections
  • Calls pytest and python; needs OPIK_POSTHOG_KEY

What it does

Every event name must start with opik_, because Segment routes opik_ events on to PostHog; tooling enforces the prefix and names in code should already carry it. On the frontend, you add the name to the OpikEvent constant in tracking.ts and call trackEvent from the component or hook where the action happens. trackEvent does nothing when Segment is not loaded (open-source mode), adds the analytics environment to event properties, and PostHog still handles pageviews, identification and feature flags directly.

On the backend, an AnalyticsService in the Java infrastructure package exposes two trackEvent overloads: one resolves identity from the current request scope, the other takes an explicit identity, needed outside a request such as in reactive schedulers, background threads and event listeners. Tracking is a no-op unless OPIK_ANALYTICS_ENABLED is true, and it is false by default. Configuration lives in AnalyticsConfig and an analytics section of config.yml, and the Python SDK reports through the same Segment pipeline.

When your agent uses it

  • Wiring a new product analytics event into an Opik frontend feature
  • Adding a tracked event from Opik's Java backend
  • Instrumenting the Python SDK so its events reach PostHog through Segment

Example prompts

  • “Track when a user creates an evaluation suite in the Opik frontend.”
  • “Add a backend analytics event for optimization creation, using an explicit identity.”
  • “Name the new onboarding event so Segment forwards it to PostHog.”

Requirements

  • A checkout of the Opik repository

Workflow steps

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

  1. Add the event name to the OpikEvent const in tracking.ts
  2. Call trackEvent from the component or hook where the action happens

What it can do on your machine

Read from SKILL.md and the folder at commit 8e3f6e5. 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

    Shell commands in SKILL.md call:

    • pytest
    • python

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

  • Network

    Links to these hosts (documentation or services it may open):

    • us.posthog.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPIK_POSTHOG_KEY

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

Context cost

Opik Analytics Instrumentation loads about 4.4k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 1,922 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

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 8e3f6e5, republished under its Apache-2.0 licence (© comet-ml). 1,922 words, ~4,381 tokens.

Download SKILL.mdSave it as .claude/skills/analytics-instrumentation/SKILL.md (or your agent's skills folder).
name
analytics-instrumentation
description
Add product analytics (BI) events to Opik features. Use when wiring events on the frontend, the backend, or the Python SDK - all three report through Segment to PostHog.

Analytics Instrumentation

Event Naming

All events MUST be prefixed with opik_. Segment routes opik_* events to PostHog. The tooling enforces this automatically, but event names defined in code should already include the prefix.

Examples: opik_onboarding_agent_name_submitted, opik_eval_suite_created, opik_optimization_created

Frontend Events

Files
  • Tracking utility: apps/opik-frontend/src/lib/analytics/tracking.ts (mode-agnostic; safe to import from any project code)
  • Segment init: apps/opik-frontend/src/plugins/comet/analytics/index.ts (comet-only)
  • Plugin init: apps/opik-frontend/src/plugins/comet/init.tsx (comet-only)
Adding a new event
  1. Add the event name to the OpikEvent const in tracking.ts:
typescript
export const OpikEvent = {
  ONBOARDING_AGENT_NAME_SUBMITTED: "opik_onboarding_agent_name_submitted",
} as const;
  1. Call trackEvent from the component or hook where the action happens:
typescript
import { trackEvent, OpikEvent } from "@/lib/analytics/tracking";

trackEvent(OpikEvent.ONBOARDING_AGENT_NAME_SUBMITTED, {
  agent_name: agentName,
});
How it works
  • trackEvent() safely no-ops when Segment isn't loaded (OSS mode)
  • opik_ prefix is enforced at runtime as a safety net
  • OPIK_ANALYTICS_ENVIRONMENT is injected into event properties automatically by trackEvent()
  • Frontend custom events flow through Segment (same pipeline as backend): Segment → PostHog
  • PostHog still handles automatic pageviews, user identification, and feature flags directly

Backend Events

Files
  • Service: apps/opik-backend/src/main/java/com/comet/opik/infrastructure/bi/AnalyticsService.java
  • Config: apps/opik-backend/src/main/java/com/comet/opik/infrastructure/AnalyticsConfig.java
  • YAML config: apps/opik-backend/config.yml (under analytics:)
API

AnalyticsService exposes two overloads:

java
void trackEvent(String eventType, Map<String, String> properties);
void trackEvent(String eventType, Map<String, String> properties, String identity);
  • 2-arg resolves identity from the current request scope via RequestContext.
  • 3-arg takes an explicit identity — use it any time the call executes outside a request scope (reactive schedulers, background threads, event listeners).
How it works
  • trackEvent() no-ops when OPIK_ANALYTICS_ENABLED is false (default).
  • opik_ prefix is auto-prepended if missing — but keep the prefix in code for grep-ability.
  • environment property is auto-injected from OPIK_ANALYTICS_ENVIRONMENT.
  • Events flow: Backend → comet-stats → Segment → PostHog.
  • AnalyticsService.sendEvent wraps the body in catch (RuntimeException) — callers must not add their own try/catch.
From a synchronous request handler

Inject and call inline. The 2-arg overload resolves identity from RequestContext.

java
private final @NonNull AnalyticsService analyticsService;

analyticsService.trackEvent("opik_onboarding_first_trace",
        Map.of("trace_id", traceId, "project_id", projectId));
From a reactive chain (doOnSuccess, doOnNext, etc.)

Two things are required: offload with Schedulers.boundedElastic() and pass identity explicitly.

Why offload: when identity is absent AnalyticsService.resolveIdentity() falls back to UsageReportService.getAnonymousId(), which is a synchronous JDBC read. Inside a doOnSuccess lambda that runs on the reactor event loop, that read blocks a scheduler-critical thread.

Why explicit identity: RequestContext is bound to the request thread via a Guice scope — inside the scheduler's lambda it throws ProvisionException, and you silently degrade to the anonymous-ID fallback, losing user attribution.

Capture userName up front from the reactor context alongside workspaceId, then pass both into the scheduled call:

java
return Mono.deferContextual(ctx -> {
    String workspaceId = ctx.get(RequestContext.WORKSPACE_ID);
    // Use getOrDefault on paths that internal/system callers reach without seeding USER_NAME
    // (e.g. a self-triggered cancellation written only with WORKSPACE_ID in the context).
    String userName = ctx.getOrDefault(RequestContext.USER_NAME, null);

    return someDao.write(...)
            .doOnSuccess(__ -> Schedulers.boundedElastic().schedule(
                    () -> analyticsService.trackEvent("opik_thing_happened",
                            Map.of(
                                    "thing_id", thing.id().toString(),
                                    "workspace_id", workspaceId),
                            userName)));
});

If you already depend on a Schedulers.boundedElastic().schedule(() -> { ... }) block that does other non-reactive work (e.g. a blocking datasetService.getById like ExperimentService.trackEvalSuiteRunIfApplicable), add the trackEvent call inside that existing lambda instead of nesting another.

Don'ts
  • Don't add try/catch around trackEvent — sendEvent catches RuntimeException internally. Extra catches are noise and diverge from the codebase pattern.
  • Don't add helper methods that only delegate to trackEvent — inline the call at the entry point. Wrap in a helper only when it encapsulates real logic (e.g. applicability check + enrichment + tracking).
  • Don't re-fetch ClickHouse rows to get "fresh" values for analytics payloads — a write and a read-after-write can land on different replicas, so you may see a stale snapshot or even a spurious NotFound. Use the pre-write snapshot; some analytics drift is acceptable, a failed user-facing request is not.
  • Don't add unit tests that verify(analyticsService)... — the codebase convention is for existing integration tests to exercise these paths organically. Sister analytics PRs (#6326 eval suite, #6333 onboarding, #6338 agent config) ship without emission assertions.
  • Don't assume trackEvent is fully non-blocking — the Javadoc contract is aspirational; the identity-fallback path is synchronous JDBC today. Offload from reactive chains as shown above.

Python SDK Events

Files

sdks/python/src/opik/analytics/ — api.py (public surface), rules.py (when reporting is allowed), worker.py (background thread), comet_stats.py (the HTTP call). Config lives in sdks/python/src/opik/config.py, prefixed analytics_.

Identity and environment metadata are shared with Sentry error tracking, not reimplemented. Both live at the top level so neither subsystem depends on the other: opik/environment.py::get_user_identifier() (workspace name, falling back to a hostname/username hash) and opik/environment_details.py (collect_tags_once() / collect_context_once()). Analytics and error_tracking/before_send.py both read them, so an event carries the same user id, the same session_id and the same environment details as any error report from the same run. Add environment metadata there, not in either consumer.

API

One function, called explicitly as the first line of whatever is being reported:

python
from opik import analytics

analytics.track_event("client", "create_dataset")
analytics.track_event("integration", "openai")
analytics.track_event("evaluation", "metric_created", metric=name)

No decorators, by design: the payload is written out at the call site, so what gets sent is whatever you can read right there.

The positional arguments form a path, broadest first, and go as deep as an event needs:

python
analytics.track_event("integration", "bedrock")                  # the integration
analytics.track_event("integration", "bedrock", "invoke_agent")  # one part of it

The first element is a closed set (analytics.Component): client, evaluation, integration — extend it there rather than passing a new string. Every level after it is free-form, and the second is normally just the method being reported.

A longer path is a different event, not a repeat of the shorter one, so instrumenting part of a feature never silences the feature itself.

Names are composed by joining the path with a double underscore — opik_python_sdk__integration__bedrock__invoke_agent — in one private helper, so the scheme can be changed for every event at once without touching a call site. The separator is doubled so the name splits back into the path: segments are method names, so they contain single underscores but never a pair. A test enforces that (test_event_names.py); keep it true when adding events. Extra properties are keyword arguments; adding one never changes the API.

Adding an event
  1. Pick the path. First element from the closed analytics.Component set — client, evaluation, integration. Second is normally the method being reported. Add further levels only to narrow a feature down ("integration", "bedrock", "invoke_agent"), remembering a longer path is a separate event, not a repeat of the shorter one.

  2. Check the segments. No level may contain a double underscore, because that is the separator the name is joined with. Method names never do, so this is normally free — test_event_names.py fails the build if it is ever not.

  3. Put the call on the first line of the user-facing function, before it does its work, so a call that goes on to fail still counts as usage. Do not wrap it in try/except and do not guard it with a config check; it already swallows everything and no-ops when reporting is off.

  4. Decide the properties, if any. Keyword arguments, scalars only. 97 of the 98 events carry none — reach for one only when the event genuinely has variants worth splitting, as metric_created does. Never a value the user chose: report the Opik-owned name and "custom" otherwise.

  5. Check it should be reported at all. Skip it if Opik calls the same entry point internally (litellm's track_completion), or if it is a per-call hot path (Opik.trace(), Opik.span(), an OTel on_start) — instrument the constructor or the user-facing function instead.

  6. Verify it locally. Intercepting the HTTP call is the quickest way to see the exact payload without sending anything:

    python
    import json, os, unittest.mock
    import httpx
    
    os.environ["OPIK_ANALYTICS_ENABLE"] = "true"
    
    sent = []
    
    def fake_post(self, url, **kwargs):
        sent.append(kwargs["json"])
        return type("R", (), {"status_code": 201})()
    
    # Patched for the block only. Replacing `post` outright leaves every later
    # request in the process - the SDK's own included - talking to the stub.
    with unittest.mock.patch.object(httpx.Client, "post", fake_post):
        from opik import analytics
    
        ...                              # exercise your new call site
        analytics.flush(timeout=10)      # batched; nothing appears without this
    
    print(json.dumps(sent, indent=2))

    Reporting is off under pytest, so this has to be a plain script, not a test. Then run the suite from the SDK directory, where it lives:

    bash
    cd sdks/python && pytest tests/unit/analytics    # 67 tests
  7. Know how it will be read. The event surfaces on the Python SDK Usage dashboard, where every tile counts uniq(distinct_id). A new event needs no dashboard change to appear in the adoption tiles, which group on the name.

Show full SKILL.md (765 more words)Show less
How it works
  • track_event() never raises, never blocks on I/O, and no-ops when reporting is off.
  • Calls Opik makes into its own API are not reported. evaluate_threads calls search_threads, get_or_create_dataset calls get_dataset, the CLI calls both — 35 of the 69 instrumented Opik methods are reachable this way. An event is dropped when either the reporting function was reached from a different Opik module, or some function further up the stack is already reporting. Being called from the reporter's own module is not enough on its own, so a private helper reporting on its caller's behalf (as BaseMetric does) still works. Internal calls record nothing, so the user's own call to the same API still reports.
  • Nothing needs decorating for that to hold. Reporting functions are recognised by code object the first time they report, so a new track_event call site joins in automatically.
  • Counting is safe across threads and forks. Claiming an event is done under a lock (a check-then-add lets every racing thread report a copy), and the worker is rebuilt after fork() — with _ALREADY_REPORTED deliberately inherited, so a child reports its own events but not the parent's. Separate processes cannot share that state, so a spawn pool reports one copy per worker: count uniq(anonymous_id), never raw event volume.
  • Events go to Comet's stats collector from a single background thread (analytics/worker.py), and on through Segment to PostHog — the same route the backend reports through. The collector takes no credentials, so there is no write key to configure; OPIK_ANALYTICS_URL alone points it somewhere else.
  • Each event is reported once per process. Analytics answers "how many users use this feature", not "how often", so Opik.span() in a hot loop costs one event and a set lookup. Events differing in their properties count as different events, so one name still covers variants (each metric class, say).
  • OPIK_ANALYTICS_ENABLE=false is the only way to switch reporting off. Reporting is also skipped under pytest, which is not a user-facing switch but the thing keeping test suites from making network calls. Add another process-level veto with analytics.register_rule(lambda config: ...) before the first tracked event.
  • Config is read once, on the first tracked event - not at import time - so opik.configure(...) is taken into account.
Don'ts
  • Don't pass user data as properties. Whatever a call site passes is what gets sent — there is no scrubbing layer. Properties are typed as scalars (str | int | float | bool | None); pick each key deliberately. Counts, flags and library names only.
  • Don't report a user-defined class or function name. Report the Opik-owned name and "custom" otherwise (see _track_metric_creation in evaluation/metrics/base_metric.py).
  • Don't add try/except around track_event - it already swallows everything.
  • Don't instrument an entry point that Opik itself calls internally. The litellm track_completion case is why: LiteLLMChatModel calls it, so the event would measure Opik's own behaviour rather than the user's.
  • Don't instrument per-callback hot paths (e.g. an OTel on_start). Instrument the constructor or the user-facing function instead. Opik.trace() and Opik.span() are deliberately uninstrumented for the same reason - the backend already sees them.

Environment Variables

VariableDefaultPurpose
OPIK_ANALYTICS_ENABLEDfalseBackend: controls whether analytics events are sent
OPIK_ANALYTICS_ENVIRONMENTemptyFrontend and backend: tags events with deployment name (e.g. staging, production)
OPIK_POSTHOG_KEY—Frontend: PostHog API key (set in config.js)
OPIK_POSTHOG_HOST—Frontend: PostHog API host (set in config.js)
OPIK_ANALYTICS_ENABLEtruePython SDK: controls whether usage events are sent
OPIK_ANALYTICS_URLstats.comet.com/notify/event/Python SDK: where events are sent. Needs no credentials; set it empty to stop reporting

Backend analytics is disabled by default; the Python SDK's is opt-out. OSS installations are unaffected on the backend.

Event Flow

Frontend custom events:  Browser → Segment → PostHog
Backend events:          Java → comet-stats → Segment → PostHog
Python SDK events:       Python → comet-stats → Segment → PostHog (background thread)
PostHog native:          Browser → posthog-js → PostHog (pageviews, feature flags, identification)

Event Property Conventions

  • Consistent typing per property: A given property key should always carry the same kind of value. Don't pass a UUID in one code path and a human-readable name in another for the same key.
  • Separate ID and name properties: When both a UUID and a display name exist, use distinct keys (e.g. blueprint_id for the UUID, blueprint_name for the display name). If one is unavailable in a code path, omit the key or send an empty string — don't repurpose the other key.
  • Include workspace_id: All backend analytics events should include the workspace ID for segmentation.

Deciding Frontend vs Backend vs Python SDK

  • Frontend: UI interactions (button clicks, wizard steps, form submissions, page visits)
  • Backend: SDK-triggered actions (trace creation, test suite runs), server-side computations, events that happen without the user being on the page
  • Python SDK: which SDK APIs, integrations and metrics users reach for, and in which environment (Python version, OS, cloud vs self-hosted vs local). Use it when the backend cannot see the difference - e.g. track_openai vs track_anthropic both produce ordinary spans server-side.

© 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

Just SKILL.md in .agents/skills/analytics-instrumentation of comet-ml/opik.

Open the folder on GitHubat commit 8e3f6e5

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Questions about Opik Analytics Instrumentation

What does Opik Analytics Instrumentation do?

Shows how to add product analytics events to Opik's frontend, Java backend and Python SDK, all reporting through Segment to PostHog with an opik_ name prefix. Every event name must start with opik_, because Segment routes opik_ events on to PostHog; tooling enforces the prefix and names in code should already carry it.ts and call trackEvent from the component or hook where the action happens.

When should I use Opik Analytics Instrumentation?

Opik Analytics Instrumentation fits situations like: wiring a new product analytics event into an Opik frontend feature; adding a tracked event from Opik's Java backend; instrumenting the Python SDK so its events reach PostHog through Segment.

How do I install Opik Analytics Instrumentation in Claude Code?

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

How do I install Opik Analytics Instrumentation in Codex?

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

Can I use Opik Analytics Instrumentation 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 analytics-instrumentation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analytics-instrumentation, .gemini/skills/analytics-instrumentation, .github/skills/analytics-instrumentation and .opencode/skills/analytics-instrumentation in your project.

What does Opik Analytics Instrumentation need to run?

Going by SKILL.md and its folder, Opik Analytics Instrumentation needs the command-line tools its instructions call (pytest and python) and credentials named OPIK_POSTHOG_KEY. Our summary lists: A checkout of the Opik repository.

Does Opik Analytics Instrumentation access the network?

SKILL.md names 1 domain. As links in the text: us.posthog.com. This is read from the text; nothing was executed.

Is Opik Analytics Instrumentation 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 Analytics Instrumentation use?

Opik Analytics Instrumentation 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 Analytics Instrumentation use?

About 4.4k tokens (SKILL.md is roughly 18k 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 Analytics Instrumentation?

Skills that share tags, products or a category with Opik Analytics Instrumentation: Cross-Language Coding Standards (zereight/gitlab-mcp, 2k stars), Dbg (theodo-group/debug-that, 158 stars), Supercov (supercorp-ai/supercov, 150 stars) and CodeScope Codebase Graph Analysis (QwenLM/qwen-code, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Opik Analytics Instrumentation?

comet-ml (a GitHub organization) maintains it in comet-ml/opik, which has 22,443 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 8, 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.