Reference Scenarios Skill

Use this skill to add or update reference implementations under reference/scenarios/<library>/ when conventions change.

Workflow

1. Find libraries: Check which scenarios under reference/scenarios/ support the changed operation(s).
2. Update scenario.py and README.md:
   • Wrap real library calls in spans. Call or patch private methods if needed to instrument the library only when it's not possible to do so through the public API.
   • Set attributes inline using only real values from SDK inputs, outputs, errors, or library state.
   • If the library cannot provide a value, do not fake it, leave it unset.
   • Update reference/scenarios/<library>/README.md operation table and status.
3. Run and test:

   cd reference
   uv run run-scenario <library>       # run scenario and update data.json
   uv run run-scenario --all           # run all scenarios
   uv run update-reports               # update report tables in README.md

4. Regenerate docs (from repo root):

   make generate-all

Scenario README (reference/scenarios/<library>/README.md)

When adding or updating a reference scenario, update its README.md:

• Libraries only: Scenarios are for libraries and SDKs only. Do NOT add application examples.
• Short description: 1-2 sentences explaining what the library is (e.g. model-call boundary, agent framework) and what it owns vs delegates.
• Operations table: List relevant operations and their status:

| Operation | Should be instrumented here | Status |
| --- | --- | --- |
| inference (`chat`) | Yes - calls model directly | ✅ Implemented |
| execute_tool | No - app runs tools | ➖ Not instrumentable |
| retrieval | Yes - Vector Stores search | ❌ Not implemented |

Good vs Bad Patterns

1. Where values come from

Good: Read values from SDK arguments or response objects:

# 'model' is an SDK parameter; response fields come from the SDK object
with tracer.start_as_current_span("chat", attributes={"gen_ai.request.model": model}) as span:
    response = client.chat.completions.create(model=model, messages=messages)
    span.set_attribute("gen_ai.response.id", response.id)
    span.set_attribute("gen_ai.usage.output_tokens", response.usage.completion_tokens)

Bad: Hardcoding values or using fake test data:

# Bad: hardcoded value not returned by SDK
span.set_attribute("gen_ai.agent.name", "my-agent")

# Bad: reading from test config instead of SDK request/response
span.set_attribute("gen_ai.response.model", test_config["expected_model"])

2. Set attributes inline

Good: Set attributes right next to the SDK call:

with tracer.start_as_current_span("invoke_agent") as span:
    result = agent.invoke(prompt)
    span.set_attribute("gen_ai.agent.id", agent.id)

Bad: Hiding attribute logic in helper functions:

with tracer.start_as_current_span("invoke_agent") as span:
    result = agent.invoke(prompt)
    _set_attributes(span, agent, result)  # Hides where values come from

3. Span boundaries

Good: Span wraps the specific library call representing the operation:

with tracer.start_as_current_span("chat") as span:
    response = client.chat.completions.create(model=model, messages=messages)

Bad: Span wraps scenario setup, multiple calls, or application loops:

# Bad: wraps multiple separate calls and setup code
with tracer.start_as_current_span("chat"):
    setup_environment()
    resp1 = client.chat.completions.create(...)
    resp2 = client.chat.completions.create(...)

# Bad: wraps custom application logic instead of instrumenting library API
with tracer.start_as_current_span("invoke_agent"):
    for step in app_steps:
        process_step(step)