Agent skill

sql-database-specialist

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npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/platforms/sql-database-specialist

SKILL.md

/============================================================================/ /* SQL-DATABASE-SPECIALIST SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: sql-database-specialist version: 1.0.0 description: | [assert|neutral] SQL database specialist for PostgreSQL/MySQL optimization, EXPLAIN plan analysis, index optimization, query rewriting, partitioning strategies, connection pooling, and database performance tuning. Use [ground:given] [conf:0.95] [state:confirmed] category: Database Specialists tags:

  • general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute sql-database-specialist workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic Database Specialists processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "sql-database-specialist", category: "Database Specialists", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["sql-database-specialist", "Database Specialists", "workflow"], context: "user needs sql-database-specialist capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

SQL Database Specialist

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Expert SQL database optimization, schema design, and performance tuning for PostgreSQL and MySQL.

Purpose

Comprehensive SQL expertise including EXPLAIN plan analysis, index optimization, query rewriting, partitioning, replication, and performance tuning. Ensures databases are fast, scalable, and maintainable.

When to Use

  • Optimizing slow database queries
  • Designing efficient database schemas
  • Analyzing EXPLAIN plans
  • Creating optimal indexes
  • Implementing database partitioning
  • Setting up replication and high availability
  • Migrating data with zero downtime
  • Troubleshooting performance issues

Prerequisites

Required: SQL basics, understanding of relational databases, familiarity with PostgreSQL or MySQL

Agents: backend-dev, perf-analyzer, system-architect, code-analyzer

Core Workflows

Workflow 1: Query Optimization with EXPLAIN

Step 1: Analyze EXPLAIN Plan (PostgreSQL)

sql
-- EXPLAIN shows query plan
EXPLAIN
SELECT u.name, o.total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE o.created_at > '2024-01-01';

-- EXPLAIN ANALYZE executes and shows actual timings
EXPLAIN (ANALYZE, BUFFERS)
SELECT u.name, o.total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE o.created_at > '2024-01-01';

Key Metrics to Check:

  • Seq Scan (bad): Full table scan, add index
  • Index Scan (good): Using index
  • Bitmap Index Scan (good): Efficient for multiple conditions
  • Nested Loop (watch out): Can be slow for large datasets
  • Hash Join (usually good): Efficient join method
  • Cost: Estimated cost (lower is better)
  • Actual time: Real execution time

Step 2: Create Optimal Index

sql
-- ❌ SLOW: No index on created_at
SELECT * FROM orders WHERE created_at > '2024-01-01';

-- ✅ FAST: Create index
CREATE INDEX idx_orders_created_at ON orders (created_at);

-- ✅ COMPOUND INDEX: For multiple columns
CREATE INDEX idx_orders_user_created
ON orders (user_id, created_at);

-- ✅ PARTIAL INDEX: For filtered queries
CREATE INDEX idx_orders_pending
ON orders (created_at)
WHERE status = 'pending';

-- ✅ COVERING INDEX: Include frequently queried columns
CREATE INDEX idx_orders_covering
ON orders (user_id, created_at)
INCLUDE (total, status);

Step 3: Rewrite Query for Performance

sql
-- ❌ SLOW: N+1 query pattern
SELECT id, name FROM users;
-- Then for each user:
SELECT * FROM orders WHERE user_id = ?;

-- ✅ FAST: Single query with JOIN
SELECT u.id, u.name, o.*
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;

-- ❌ SLOW: NOT IN with subquery
SELECT * FROM users
WHERE id NOT IN (SELECT user_id FROM orders);

-- ✅ FAST: LEFT JOIN with NULL check
SELECT u.*
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE o.user_id IS NULL;

-- ❌ SLOW: OR conditions prevent index use
SELECT * FROM orders
WHERE user_id = 123 OR status = 'pending';

-- ✅ FAST: UNION ALL with indexes
SELECT * FROM orders WHERE user_id = 123
UNION ALL
SELECT * FROM orders WHERE status = 'pending' AND user_id != 123;

Workflow 2: Table Partitioning (PostgreSQL)

Step 1: Create Partitioned Table

sql
-- Range partitioning by date
CREATE TABLE orders (
  id BIGSERIAL,
  user_id INT NOT NULL,
  created_at DATE NOT NULL,
  total DECIMAL(10, 2),
  status VARCHAR(20)
) PARTITION BY RANGE (created_at);

-- Create partitions
CREATE TABLE orders_2024_q1 PARTITION OF orders
FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');

CREATE TABLE orders_2024_q2 PARTITION OF orders
FOR VALUES FROM ('2024-04-01') TO ('2024-07-01');

-- Create index on each partition
CREATE INDEX idx_orders_2024_q1_user_id
ON orders_2024_q1 (user_id);

-- Queries automatically use correct partition
SELECT * FROM orders
WHERE created_at >= '2024-02-01'
  AND created_at < '2024-03-01';
-- Only scans orders_2024_q1 partition

Step 2: List Partitioning by Status

sql
CREATE TABLE orders_

/*----------------------------------------------------------------------------*/
/* S4 SUCCESS CRITERIA                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := {
  primary: "Skill execution completes successfully",
  quality: "Output meets quality thresholds",
  verification: "Results validated against requirements"
} [ground:given] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S5 MCP INTEGRATION                                                          */
/*----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := {
  memory_mcp: "Store execution results and patterns",
  tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"]
} [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S6 MEMORY NAMESPACE                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := {
  pattern: "skills/Database Specialists/sql-database-specialist/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "sql-database-specialist-{session_id}",
  WHEN: "ISO8601_timestamp",
  PROJECT: "{project_name}",
  WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S7 SKILL COMPLETION VERIFICATION                                            */
/*----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := {
  agent_spawning: "Spawn agents via Task()",
  registry_validation: "Use registry agents only",
  todowrite_called: "Track progress with TodoWrite",
  work_delegation: "Delegate to specialized agents"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S8 ABSOLUTE RULES                                                           */
/*----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* PROMISE                                                                     */
/*----------------------------------------------------------------------------*/

[commit|confident] <promise>SQL_DATABASE_SPECIALIST_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]

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