TiDB X is an object-storage-native, elastic, and multi-tenant SQL engine designed specifically for AI agent workloads with query-based billing.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tidb-x
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install tidb-x using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
TiDB X represents a shift in database architecture designed for the AI era, where context and agent memory become structured data. It is a distributed SQL engine that uses S3 object storage as its primary source of truth, offering infinite scalability and high durability. For developers building with Openclaw Skills, TiDB X provides a MySQL-compatible interface that handles everything from session context to multi-agent coordination without the complexity of traditional database management.
This system is built for high-concurrency and bursty traffic patterns typical of autonomous agents. By decoupling compute from storage, it allows for stateless scaling and provides a unique Request Unit (RU) billing model. This ensures that your Openclaw Skills only incur costs when queries are actually executed, making it perfect for sprawling agentic ecosystems where many databases might sit idle for periods.
To provision a free, ephemeral TiDB Cloud Zero instance for testing your Openclaw Skills, run the following command:
curl -s -X POST "https://zero.tidbapi.com/v1alpha1/instances" -H "Content-Type: application/json" -d '{"tag":"my-agent"}' | tee tidb-zero.json
To install this skill into your local environment, use:
mkdir -p ~/.openclaw/skills/tidb-x && curl -so ~/.openclaw/skills/tidb-x/SKILL.md https://raw.githubusercontent.com/siddontang/tidb-x-skill/main/SKILL.md
TiDB X organizes data using standard relational tables with enhanced JSON support for flexible agent metadata. Below are common schemas used in Openclaw Skills implementation:
| Table Name | Purpose | Key Columns |
|---|---|---|
agent_memory |
Long-term state | tenant_id, content, tags (JSON) |
agent_context |
Session history | session_id, step, type, tokens |
tasks |
Multi-agent queue | task_key, status, claimed_at |
chain_events |
Web3 Indexing | tx_hash, chain, decoded_args (JSON) |
Loading
A secure, shared persistent memory service designed to help AI agents like OpenClaw store and retrieve context across different sessions and platforms.

A high-performance voice automation skill that enables Openclaw agents to make, manage, and analyze AI-powered phone calls via Ringg AI.

A comprehensive interface to fetch public data from The Ohio State University Content APIs, including transit, dining, and academic resources.

A command-line interface for macOS that enables AI agents to programmatically manage Apple Calendar events using EventKit and structured JSON.

An AI nutrition assistant that uses vision analysis to log calories and macros directly from food photos.

A sophisticated AI orchestration skill that applies strict Context Engineering and a four-phase methodology to deliver maintainable, secure, and architecturally sound code.








































