Openocta Db Ops Skills

8 production-tested skills + a reference MCP server. Turn a 20-minute DBA diagnosis into an 8-second AI analysis.

A collection of database operations skills for OpenOcta AI Agent — distilled from a financial-institution DBA practice managing a 600+-instance fleet. Coverage: MySQL / MariaDB / TiDB (slow query, replication, deadlock, capacity, schema audit, cluster inspection); Redis and PostgreSQL workflows are not part of this pack. The DBA goes from "operator" to "approver" — AI does the diagnosis, DBA says "confirm".

What's Included

Component	Description
8 Skills	Workflows for slow query, replication lag, health check, capacity, deadlock, TiDB, schema
MCP Server	Reference Python implementation exposing the 9 db_* tools the skills need — run it and everything works out of the box
Examples	End-to-end smoke test verifying all tools against a live database

The Problem

At 2:37 AM, a replication lag alert fires. Traditional response:

📱 Alert → 😴 Wake up → 💻 Open laptop → 🔒 Connect VPN
→ 🖥️ Login jumpbox → 📊 Check monitoring → 🔍 Analyze SQL → 🛠️ Manual fix
⏱ Full process: 15~20 minutes

With these skills:

📱 View AI diagnosis on phone → ✅ Confirm
⏱ Full process: ~2 minutes (10x faster)

Skills Overview

Incident Response (5 skills)

Skill	Real-World Scenario	Impact
mysql-slow-query-diagnose	Slow query diagnosis + index recommendation	144x faster (20min → 8.3s)
mysql-replication-lag-resolver	Replication lag auto-diagnosis + KILL approval	10x faster (20min → 2min)
mysql-batch-health-check	MySQL/MariaDB fleet health inspection	30x faster (half day → 10min)
sql-duration-spike-detector	Detect slow SQL before it hits the threshold	0 → 1 (previously impossible)
mysql-capacity-early-warning	Capacity prediction with growth source attribution	6-day early warning

Diagnostics & Audit (3 skills)

Skill	Description
mysql-deadlock-analyzer	Full-chain deadlock analysis: InnoDB status → lock chain → pattern classification → fix
tidb-cluster-inspector	TiDB cluster inspection: topology → health → slow queries → hot regions → TiKV → PD
mysql-schema-linter	Schema audit: PK, engine, charset, redundant indexes, FK, auto-inc overflow

Total: 8 skills, 2,800+ lines of battle-tested diagnostic workflows

Quick Start

1. Add skills to OpenOcta

cp -r mysql-slow-query-diagnose /path/to/openocta/skills/
cp -r mysql-replication-lag-resolver /path/to/openocta/skills/
cp -r mysql-batch-health-check /path/to/openocta/skills/
cp -r sql-duration-spike-detector /path/to/openocta/skills/
cp -r mysql-capacity-early-warning /path/to/openocta/skills/
cp -r mysql-deadlock-analyzer /path/to/openocta/skills/
cp -r tidb-cluster-inspector /path/to/openocta/skills/
cp -r mysql-schema-linter /path/to/openocta/skills/

2. Configure an MCP server

These skills require a MySQL MCP server exposing the following tools:

Tool	Description
db_query	Execute SQL queries
db_get_status	Get server status variables
db_get_variable	Get server configuration variables
db_get_processlist	View current connections
db_kill_query	Terminate a query/connection
db_get_slow_log	Retrieve slow query log entries
db_describe_table	Show table structure and indexes
db_list_databases	List databases with size
db_list_tables	List tables in a database with size

Compatible MCP servers:

• omnidb-mcp — Multi-database MCP server supporting 22 databases (MySQL, MariaDB, TiDB, PostgreSQL, Redis, and more)

Map the MCP server's tools to db_* naming in your OpenOcta configuration.

3. Use in conversation

User: "Check slow queries on db-prod-01"
→ AI finds TOP 3 slow queries, analyzes index hit rate, suggests indexes
→ "Add (status, create_time) index on order_detail — est. 237x speedup"

User: "Replication lag alert on db-slave-01"
→ AI identifies 3 blocking queries on master, classifies kill safety
→ DBA confirms → 623s lag → 0s in 90 seconds

User: "Check all instances health"
→ AI inspects the MySQL/MariaDB fleet, outputs TOP 5 anomalies sorted by urgency
→ "#1: disk 92%, 4 days to full — clear audit_log to free 54GB"

User: "Any SQL performance degradation?"
→ AI compares 24h vs 7d baseline, finds 14.7x spike on order_detail
→ "Index selectivity dropped — table grew 1.2M → 3.8M rows"

User: "Check capacity outlook"
→ AI breaks down disk growth by source: binlog 44%, audit 34%, data 22%
→ "6 days to full. Action: clear audit_log (→51%), reduce binlog retention"

Design Principles

1. Value over features — Each skill solves a real pain point with measurable ROI
2. Safety first — Destructive operations (KILL) require double user confirmation
3. Root cause, not symptoms — Don't just show "slow query", explain WHY and HOW to fix
4. Growth attribution — Capacity analysis breaks down growth by source (binlog/audit/data)
5. Pre-threshold detection — Find problems before they trigger alerts
6. Zero proprietary dependencies — Generic db_* tool names, compatible with any MCP server
7. Production validated — All SQL tested against real databases with 8,000+ tables

License

Apache 2.0 — See LICENSE

Author

vicleung — DBA who manages 600+ instances with AI. From "operator" to "approver".