validate-genai-spanslisted

# Validate GenAI spans Broken instrumentation fails silently: the app works, the dashboards just quietly miss the model, the tokens, or the cost. This skill ships a validator that asserts your spans carry the required OpenTelemetry `gen_ai.*` fields, so you catch gaps in a test instead of in a half-empty dashboard. ## Use the bundled script [`scripts/validate_span.py`](scripts/validate_span.py) is pure Python, no install needed: ```python from validate_span import validate_gen_ai_span, is_valid_gen_ai_span problems = validate_gen_ai_span(span_attributes) # [] means valid validate_gen_ai_span(span_attributes, strict=True) # also flag recommended fields assert is_valid_gen_ai_span(span_attributes) # use in an instrumentation test ``` It checks the required fields (`gen_ai.system`, `gen_ai.operation.name`, `gen_ai.request.model`) and, in strict mode, the recommended usage fields (`gen_ai.usage.input_tokens`/`output_tokens`, `gen_ai.response.model`) that cost analysis depends on. ## How to apply it 1. **Add an instrumentation test:** capture the span your app emits for a sample call (most SDKs have an in-memory span exporter for tests) and assert `is_valid_gen_ai_span(attrs)`. 2. **Run it in CI** so a change that breaks instrumentation fails the build, not production. 3. **Use strict mode** once basic spans pass, to push toward full cost/usage coverage. Pairs with `instrument-llm-observability` (which gets the spans emitted in the first place