maple

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.

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.

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.

Trace Agno agents with Maple: installs the OpenInference Agno instrumentor with an OTLP exporter and GenAI attributes, and sets session_id per conversation so each chat is one Maple Agent Session with transcript, tool calls, team members, tokens and cost. Triggers on 'trace my agno agent', 'add Maple to agno', 'agent sessions for agno', 'OpenTelemetry for agno'.

Trace Claude Agent SDK agents (TypeScript and Python) and Claude Code CLI sessions with Maple: configure Claude Code's built-in OpenTelemetry so each conversation becomes one Maple Agent Session with prompts, model calls, tool calls and tokens. Triggers on 'trace my claude agent sdk agent', 'add Maple to claude agent sdk', 'agent sessions for claude code', 'OpenTelemetry for claude agent sdk', 'send my claude code sessions to Maple'.

Trace Cloudflare Agents SDK agents (AIChatAgent, Agent on Durable Objects, npm `agents` / `@cloudflare/ai-chat`) with Maple: export the Vercel AI SDK's OpenTelemetry spans from a Worker/Durable Object over fetch, flush per turn, and pass the agent instance name as the conversation id so each chat is one Agent Session with transcript, tool calls, sub-agents and tokens. Triggers on 'trace my cloudflare agent', 'add Maple to cloudflare agents', 'agent sessions for AIChatAgent', 'OpenTelemetry in a durable object agent', 'trace agents sdk on workers'.

Trace CrewAI crews and flows with Maple: OpenInference CrewAI instrumentor plus the model-SDK instrumentor, GenAI dual-write, one Maple Agent Session per conversation with transcript, model and tool calls, tokens, failed tools and one lane per agent. Triggers on 'trace my crewai agent', 'add Maple to crewai', 'agent sessions for crewai', 'OpenTelemetry for crewai'.

Trace DSPy programs and ReAct agents with Maple: OpenInference DSPy instrumentor with GenAI dual-write, a DSPy callback for tokens, cost, tool names and agent spans, using_session for one session per conversation, and thread context for dspy.Parallel. Triggers on 'trace my dspy agent', 'add Maple to dspy', 'agent sessions for dspy', 'OpenTelemetry for dspy'.

Trace eve agents (Vercel's eve framework, npm `eve`) with Maple: add one OpenTelemetry destination file under agent/instrumentation/ so each eve session is one Maple Agent Session with transcript, tool calls, subagent lanes, approvals and tokens. Covers eve dev, eve start, the channel audience that gates message content, and dropping the workflow engine's spans. Triggers on 'trace my eve agent', 'add Maple to eve', 'agent sessions for eve', 'OpenTelemetry for eve'.

Trace Genkit (TypeScript/Node.js) agents with Maple: export Genkit's OpenTelemetry spans to Maple, map its genkit:* attributes to the GenAI conventions with a span processor, and stamp a conversation id so each chat is one Agent Session with transcript, tool calls and tokens. Covers flows, ai.generate, generateStream and beta defineAgent chats. Triggers on 'trace my genkit agent', 'add Maple to genkit', 'agent sessions for genkit', 'OpenTelemetry for genkit', 'firebase genkit tracing'.

Trace Google ADK (Agent Development Kit) agents with Maple, in Python (google-adk) and TypeScript (@google/adk): register an OTLP tracer provider, get the transcript and tool calls into the GenAI attributes Maple reads (env switches + a plugin in Python, a span processor in TypeScript), and keep one session per ADK session id. Triggers on 'trace my ADK agent', 'add Maple to Google ADK', 'add Maple to @google/adk', 'agent sessions for Google ADK', 'OpenTelemetry for google-adk'.

Trace Haystack agents with Maple: installs a small Haystack tracer that adds OpenTelemetry GenAI attributes to Haystack's own spans, so each conversation is one Maple Agent Session with transcript, model, tokens, cost and tool failures. Triggers on 'trace my haystack agent', 'add Maple to haystack', 'agent sessions for haystack', 'OpenTelemetry for haystack'.

Trace LangChain and LangGraph agents (Python, and LangChain.js / LangGraph.js in TypeScript) with Maple: export OpenInference LangChain spans with GenAI attributes so each thread is one Maple Agent Session with transcript, tool calls, sub-agent lanes and tokens. Triggers on 'trace my langchain agent', 'trace my langgraph agent', 'add Maple to langchain', 'add Maple to langgraph', 'agent sessions for langchain', 'OpenTelemetry for langgraph', 'trace langchain.js', 'trace langgraph.js', 'createAgent tracing'.

Trace LiteLLM agents with Maple: export LiteLLM's v2 OpenTelemetry spans (Python SDK or self-hosted LiteLLM Proxy) plus your own agent/tool spans so each conversation is one Maple Agent Session with transcript, tool calls, tokens and cost. Triggers on 'trace my litellm agent', 'add Maple to litellm', 'agent sessions for litellm', 'OpenTelemetry for litellm', 'trace the litellm proxy'.

Trace LlamaIndex agents with Maple: OpenInference LlamaIndex instrumentor with GenAI output, a conversation id per chat, agent names and one span per model call, so each conversation is one Maple Agent Session with transcript, tools and tokens. Triggers on 'trace my llamaindex agent', 'add Maple to llamaindex', 'agent sessions for llamaindex', 'OpenTelemetry for llamaindex', 'llama-index observability'.