Plain Node.js (Express, Fastify, Hono, Bun) OpenTelemetry style for Maple: NodeSDK + --import bootstrap, native @opentelemetry/api call sites, inline endpoint + ingest key, OTLP HTTP exporters.

Next.js / Vercel OpenTelemetry style for Maple: instrumentation.ts, @vercel/otel bootstrap, native @opentelemetry/api call sites, inline endpoint + ingest key, no raw NodeSDK replacement.

Kotlin (Ktor, Spring Boot) OpenTelemetry style for Maple: zero-code Java agent or manual SDK with OTLP HTTP exporters, inline endpoint + ingest key, semconv resource attributes, OTLP-bridged logs.

Java OpenTelemetry style for Maple: zero-code Java agent or manual SDK with OTLP HTTP exporters, inline endpoint + ingest key, semconv resource attributes, OTLP-bridged Logback / SLF4J logs.

Go OpenTelemetry style for Maple: go.opentelemetry.io/otel SDK with otlptracehttp / otlploghttp / otlpmetrichttp exporters, inline endpoint + ingest key, semconv resource attributes including vcs.repository.url.full.

Effect-TS OpenTelemetry style for Maple via @maple-dev/effect-sdk: Maple.layer() bootstrap, Effect.withSpan / Effect.annotateCurrentSpan call sites, Effect.log for trace-correlated logging, server / browser / Cloudflare entry points.

Build, repair, or review Maple dashboard widgets via the MCP. Triggers on phrases like 'create_dashboard', 'add_dashboard_widget', 'update_dashboard_widget', 'dashboard widget JSON', 'QueryDraft', 'trace dashboard widget', 'Invalid input for getQueryBuilderTimeseries', or any session that submits raw widget JSON to the maple MCP. Covers the source-discriminated QueryDraft shape, the custom whereClause grammar, valid aggregations per data source, groupBy prefix conventions, the stat-widget `reduceToValue` transform, hiding auxiliary series on formula charts, and the verification step (MCP success ≠ query success).

.NET / C# OpenTelemetry style for Maple: OpenTelemetry.Extensions.Hosting + OTLP HTTP exporter, ActivitySource for spans, ILogger bridging via OpenTelemetryLoggerProvider, inline endpoint + ingest key.

Audit an already-instrumented project against Maple's OpenTelemetry conventions, report gaps per service, and fix them. Triggers on requests like 'audit my instrumentation', 'check my telemetry', 'review my OTel setup', 'why is my service map missing edges', 'is my Maple instrumentation correct'.

Extract the JSDoc example nearest the active source selection or cursor into ./scratchpad as a TypeScript file. Use when the user asks to dump, copy, open, or try a source example in scratchpad.

Write, insert, or update Effect public API JSDoc so it satisfies the jsdocs oxlint rule. Use when adding or fixing JSDoc comments, resolving jsdocs diagnostics, preparing docs for JSON extraction, or reviewing public API documentation.

Interview the user about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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

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.

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.

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.sqlQuery()`.

Use when a user wants to deploy ClickHouse to the cloud, go to production, use ClickHouse Cloud, host a managed ClickHouse service, or migrate from a local ClickHouse setup to ClickHouse Cloud.

MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 28 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.

MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.

In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.