maple

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()`.

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.

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'.

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.

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.

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.

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.

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.

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.

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.

Onboard a project to Maple by installing OpenTelemetry traces, logs, and metrics across every app and service in the repo. Triggers on requests like 'install Maple', 'set up Maple', 'add Maple telemetry', 'onboard this repo to Maple', 'instrument with OpenTelemetry for Maple'.

General OpenTelemetry onboarding style for Maple: native APIs, signal quality, inline keys, VCS resource attributes, LLM metrics, and smoke checks.

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.

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.

Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.

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.

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.

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.

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.

Use when a user wants to build an application with ClickHouse, set up a local ClickHouse development environment, install ClickHouse, create a local server, create tables, or start developing with ClickHouse. Covers the full flow from zero to a working local ClickHouse setup.

Index of all COSS UI particle examples. Use when implementing UI features to find copy-paste-ready component patterns built on coss primitives. Each particle has a description and a JSON URL for easy installation.

Helps implement coss UI components correctly. Use when building UIs with coss primitives (buttons, dialogs, selects, forms, menus, tabs, inputs, toasts, etc.), migrating from shadcn/Radix to coss/Base UI, composing trigger-based overlays, or troubleshooting coss component behavior. Covers imports, accessibility, Tailwind styling, and common pitfalls.