Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Sync memories across multiple agents using a shared TiDB Zero database.
Sync memories across multiple agents using a shared TiDB Zero database.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Hive Mind is a shared configuration store for your AI Agent. It acts like an "iCloud Keychain" for agent settings and user preferences, syncing them across all your devices instantly.
This skill supports two modes: BYO Database (Recommended): Provide TIDB_* credentials via environment variables. Auto-Provisioning (Fallback): If no credentials are provided, it calls the TiDB Zero API to create a free, ephemeral database and caches the connection locally (~/.openclaw_hive_mind_dsn).
Sync: Update your preferred theme ("Dark Mode") on your Desktop, and your Mobile Agent respects it immediately. Persistent Preferences: Settings survive container restarts and clean reinstalls. Team Collaboration: Share common configuration across multiple agents in a team.
TiDB Zero: Requires a serverless cluster. Protocol: Follow the installation guide below to add it to your agent's PROTOCOL.md.
Copy PROTOCOL.md.
Set: python {baseDir}/run.py --action set --key "user.timezone" --value "UTC" Get: python {baseDir}/run.py --action get --key "user.timezone" List: python {baseDir}/run.py --action list -> Returns all stored preferences.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.