maple

Python OpenTelemetry style for Maple: module-scope tracers/meters, decorators for bounded work, error spans, OTLP-bridged logs via LoggingHandler + LoggingInstrumentor, inline endpoint + ingest key, and no helper-API wrappers.

Rust OpenTelemetry style for Maple: opentelemetry + opentelemetry_sdk + opentelemetry-otlp HTTP exporter, tracing-opentelemetry bridge for the tracing crate, inline endpoint + ingest key, semconv resource attributes.

Maple's OpenTelemetry conventions: custom span attribute keys (`maple.*` vendor namespace, `query.context`, `db.query.*`, `result.*`, `cache.*`, `tenant.*`), Title Case status codes (`Ok`/`Error`/`Unset`), resource attribute dual-emit (`deployment.environment` + `deployment.environment.name`), span kinds, Tinybird MV pre-extracted columns, loop-prevention filters, and sampling. Use whenever writing or reviewing instrumentation code in any language (TypeScript, Rust, Python) in this repo: adding `setAttribute`/`setAttributes`/`record`/`#[instrument(fields(...))]` calls, setting span status, configuring an OTLP exporter, defining a new resource attribute, or wiring a new query through `WarehouseQueryService.compiledQuery()`.

When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.

Use when finishing a feature, fixing a bug, before committing React code, or when the user wants to improve code quality or clean up a codebase. Checks for score regression. Covers lint, dead code, accessibility, bundle size, architecture diagnostics.

Use when finishing a feature, fixing a bug, before committing React code, or when the user wants to improve code quality or clean up a codebase. Checks for score regression. Covers lint, dead code, accessibility, bundle size, architecture diagnostics.

Tinybird file formats, SQL rules, optimization patterns, and best practices for datasources, pipes, endpoints, and materialized views.

Tinybird CLI commands, workflows, and operations. Use when running tb commands, managing local development, deploying, or working with data operations.

Tinybird Python SDK for defining datasources, pipes, and queries in Python. Use when working with tinybird-sdk, Python Tinybird projects, or data ingestion and queries in Python.

Tinybird TypeScript SDK for defining datasources, pipes, and queries with full type inference. Use when working with @tinybirdco/sdk, TypeScript Tinybird projects, or type-safe data ingestion and queries.