manutej/luxor-claude-marketplace/postgresql

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Comprehensive PostgreSQL database administration skill for customer support tech enablement, covering database design, optimization, performance tuning, backup/recovery, and advanced query techniques

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npx vskill@latest install manutej/luxor-claude-marketplace/postgresql

SKILL.md

PostgreSQL Database Administration for Customer Support

Overview

This comprehensive skill covers PostgreSQL database administration specifically tailored for customer support tech enablement. PostgreSQL is a powerful, open-source object-relational database system with over 35 years of active development, known for its reliability, feature robustness, and performance. In customer support environments, PostgreSQL excels at handling complex ticket management, user analytics, audit logging, and real-time reporting requirements.

Core Competencies

1. Customer Support Database Design
Schema Design Principles

When designing databases for customer support systems, focus on:

  • Normalization: Balance between 3NF and denormalization for performance
  • Scalability: Design for growth in ticket volume and user base
  • Auditability: Track all changes with timestamps and user attribution
  • Flexibility: Use JSONB for dynamic metadata and custom fields
  • Performance: Strategic indexing for common query patterns
Support Ticket Schema Design
sql
-- Core tables for customer support system
CREATE TABLE users (
user_id BIGSERIAL PRIMARY KEY,
email VARCHAR(255) NOT NULL UNIQUE,
full_name VARCHAR(255) NOT NULL,
role VARCHAR(50) NOT NULL CHECK (role IN ('customer', 'agent', 'admin')),
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
is_active BOOLEAN DEFAULT true,
metadata JSONB DEFAULT '{}'::jsonb
);
CREATE TABLE tickets (
ticket_id BIGSERIAL PRIMARY KEY,
ticket_number VARCHAR(50) NOT NULL UNIQUE,
customer_id BIGINT NOT NULL REFERENCES users(user_id),
assigned_agent_id BIGINT REFERENCES users(user_id),
subject VARCHAR(500) NOT NULL,
description TEXT NOT NULL,
status VARCHAR(50) NOT NULL DEFAULT 'open'
CHECK (status IN ('open', 'in_progress', 'waiting', 'resolved', 'closed')),
priority VARCHAR(20) NOT NULL DEFAULT 'medium'
CHECK (priority IN ('low', 'medium', 'high', 'critical')),
category VARCHAR(100),
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
resolved_at TIMESTAMP WITH TIME ZONE,
closed_at TIMESTAMP WITH TIME ZONE,
first_response_at TIMESTAMP WITH TIME ZONE,
tags TEXT[] DEFAULT '{}',
custom_fields JSONB DEFAULT '{}'::jsonb,
search_vector tsvector
);
CREATE TABLE ticket_comments (
comment_id BIGSERIAL PRIMARY KEY,
ticket_id BIGINT NOT NULL REFERENCES tickets(ticket_id) ON DELETE CASCADE,
user_id BIGINT NOT NULL REFERENCES users(user_id),
comment_text TEXT NOT NULL,
is_internal BOOLEAN DEFAULT false,
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
attachments JSONB DEFAULT '[]'::jsonb,
search_vector tsvector
);
CREATE TABLE ticket_history (
history_id BIGSERIAL PRIMARY KEY,
ticket_id BIGINT NOT NULL REFERENCES tickets(ticket_id) ON DELETE CASCADE,
user_id BIGINT NOT NULL REFERENCES users(user_id),
field_name VARCHAR(100) NOT NULL,
old_value TEXT,
new_value TEXT,
changed_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE organizations (
org_id BIGSERIAL PRIMARY KEY,
org_name VARCHAR(255) NOT NULL UNIQUE,
domain VARCHAR(255),
plan_type VARCHAR(50) NOT NULL DEFAULT 'free',
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
settings JSONB DEFAULT '{}'::jsonb
);
CREATE TABLE user_organizations (
user_id BIGINT NOT NULL REFERENCES users(user_id) ON DELETE CASCADE,
org_id BIGINT NOT NULL REFERENCES organizations(org_id) ON DELETE CASCADE,
role VARCHAR(50) NOT NULL DEFAULT 'member',
joined_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (user_id, org_id)
);
Audit Logging Schema
sql
CREATE TABLE audit_logs (
log_id BIGSERIAL PRIMARY KEY,
table_name VARCHAR(100) NOT NULL,
record_id BIGINT NOT NULL,
action VARCHAR(20) NOT NULL CHECK (action IN ('INSERT', 'UPDATE', 'DELETE')),
user_id BIGINT REFERENCES users(user_id),
old_data JSONB,
new_data JSONB,
changed_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
ip_address INET,
user_agent TEXT
);
-- Create a partition for audit logs by month
CREATE TABLE audit_logs_y2025m01 PARTITION OF audit_logs
FOR VALUES FROM ('2025-01-01') TO ('2025-02-01');
2. Indexing Strategies for Support Queries
B-Tree Indexes for Common Queries
sql
-- Index for finding tickets by status
CREATE INDEX idx_tickets_status ON tickets(status) WHERE status != 'closed';
-- Index for finding tickets by customer
CREATE INDEX idx_tickets_customer ON tickets(customer_id, created_at DESC);
-- Index for finding tickets by assigned agent
CREATE INDEX idx_tickets_agent ON tickets(assigned_agent_id, status)
WHERE assigned_agent_id IS NOT NULL;
-- Composite index for ticket filtering
CREATE INDEX idx_tickets_status_priority_created
ON tickets(status, priority, created_at DESC);
-- Index for email lookups
CREATE INDEX idx_users_email ON users(email) WHERE is_active = true;
-- Index for ticket number lookups
CREATE UNIQUE INDEX idx_tickets_ticket_number ON tickets(ticket_number);
GIN Indexes for Full-Text Search
sql
-- Full-text search on ticket subject and description
CREATE INDEX idx_tickets_search ON tickets USING GIN(search_vector);
-- Update trigger for maintaining search vector
CREATE OR REPLACE FUNCTION tickets_search_trigger() RETURNS trigger AS $$
BEGIN
NEW.search_vector :=
setweight(to_tsvector('english', COALESCE(NEW.subject, '')), 'A') ||
setweight(to_tsvector('english', COALESCE(NEW.description, '')), 'B') ||
setweight(to_tsvector('english', COALESCE(array_to_string(NEW.tags, ' '), '')), 'C');
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER tickets_search_update
BEFORE INSERT OR UPDATE ON tickets
FOR EACH ROW EXECUTE FUNCTION tickets_search_trigger();
-- Full-text search on comments
CREATE INDEX idx_comments_search ON ticket_comments USING GIN(search_vector);
CREATE OR REPLACE FUNCTION comments_search_trigger() RETURNS trigger AS $$
BEGIN
NEW.search_vector := to_tsvector('english', COALESCE(NEW.comment_text, ''));
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER comments_search_update
BEFORE INSERT OR UPDATE ON ticket_comments
FOR EACH ROW EXECUTE FUNCTION comments_search_trigger();
GIN Indexes for JSONB and Array Operations
sql
-- Index for JSONB containment queries
CREATE INDEX idx_tickets_custom_fields ON tickets USING GIN(custom_fields);
-- Index for array tag searches
CREATE INDEX idx_tickets_tags ON tickets USING GIN(tags);
-- Index for specific JSONB keys
CREATE INDEX idx_tickets_custom_source
ON tickets USING GIN((custom_fields -> 'source'));
Partial Indexes for Specific Use Cases
sql
-- Index only open and in-progress tickets
CREATE INDEX idx_tickets_active
ON tickets(created_at DESC)
WHERE status IN ('open', 'in_progress', 'waiting');
-- Index unassigned tickets
CREATE INDEX idx_tickets_unassigned
ON tickets(priority DESC, created_at ASC)
WHERE assigned_agent_id IS NULL AND status = 'open';
-- Index high-priority unresolved tickets
CREATE INDEX idx_tickets_urgent
ON tickets(created_at ASC)
WHERE priority IN ('high', 'critical') AND status != 'closed';
3. Full-Text Search Implementation
Basic Full-Text Search Queries
sql
-- Search tickets by keyword
SELECT
ticket_id,
ticket_number,
subject,
ts_rank(search_vector, query) AS rank
FROM
tickets,
to_tsquery('english', 'login & problem') AS query
WHERE
search_vector @@ query
ORDER BY
rank DESC, created_at DESC
LIMIT 20;
-- Advanced search with phrase matching
SELECT
ticket_id,
ticket_number,
subject,
ts_headline('english', description, query, 'MaxWords=50, MinWords=25') AS excerpt
FROM
tickets,
websearch_to_tsquery('english', '"password reset" OR authentication') AS query
WHERE
search_vector @@ query
ORDER BY
ts_rank_cd(search_vector, query) DESC
LIMIT 10;
Searching Across Multiple Tables
sql
-- Search tickets and comments together
WITH search_results AS (
SELECT
'ticket' AS source,
ticket_id,
ticket_id AS ref_id,
subject AS title,
description AS content,
created_at,
ts_rank(search_vector, query) AS rank
FROM
tickets,
to_tsquery('english', 'billing & invoice') AS query
WHERE
search_vector @@ query
UNION ALL
SELECT
'comment' AS source,
ticket_id,
comment_id AS ref_id,
'Comment' AS title,
comment_text AS content,
created_at,
ts_rank(search_vector, query) AS rank
FROM
ticket_comments,
to_tsquery('english', 'billing & invoice') AS query
WHERE
search_vector @@ query
)
SELECT * FROM search_results
ORDER BY rank DESC, created_at DESC
LIMIT 50;
4. Query Optimization for Analytics
Window Functions for Ranking and Analytics
sql
-- Agent performance metrics with ranking
SELECT
u.user_id,
u.full_name,
COUNT(t.ticket_id) AS tickets_resolved,
AVG(EXTRACT(EPOCH FROM (t.resolved_at - t.created_at)) / 3600)::numeric(10,2) AS avg_resolution_hours,
RANK() OVER (ORDER BY COUNT(t.ticket_id) DESC) AS volume_rank,
RANK() OVER (ORDER BY AVG(EXTRACT(EPOCH FROM (t.resolved_at - t.created_at))) ASC) AS speed_rank
FROM
users u
JOIN tickets t ON u.user_id = t.assigned_agent_id
WHERE
u.role = 'agent'
AND t.resolved_at >= CURRENT_DATE - INTERVAL '30 days'
AND t.status = 'resolved'
GROUP BY
u.user_id, u.full_name
ORDER BY
tickets_resolved DESC;
-- Daily ticket trends with moving average
SELECT
date_trunc('day', created_at) AS ticket_date,
COUNT(*) AS daily_tickets,
AVG(COUNT(*)) OVER (ORDER BY date_trunc('day', created_at) ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg_7day
FROM
tickets
WHERE
created_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY
date_trunc('day', created_at)
ORDER BY
ticket_date;
Materialized Views for Dashboards
sql
-- Create materialized view for agent performance dashboard
CREATE MATERIALIZED VIEW mv_agent_performance AS
SELECT
u.user_id,
u.full_name,
u.email,
COUNT(DISTINCT t.ticket_id) AS total_tickets,
COUNT(DISTINCT CASE WHEN t.status = 'resolved' THEN t.ticket_id END) AS resolved_tickets,
COUNT(DISTINCT CASE WHEN t.status IN ('open', 'in_progress') THEN t.ticket_id END) AS active_tickets,
AVG(EXTRACT(EPOCH FROM (COALESCE(t.first_response_at, CURRENT_TIMESTAMP) - t.created_at)) / 3600)::numeric(10,2) AS avg_first_response_hours,
AVG(CASE
WHEN t.resolved_at IS NOT NULL
THEN EXTRACT(EPOCH FROM (t.resolved_at - t.created_at)) / 3600
END)::numeric(10,2) AS avg_resolution_hours,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t.resolved_at - t.created_at)) / 3600) AS median_resolution_hours
FROM
users u
LEFT JOIN tickets t ON u.user_id = t.assigned_agent_id
WHERE
u.role = 'agent'
AND u.is_active = true
AND (t.created_at >= CURRENT_DATE - INTERVAL '30 days' OR t.ticket_id IS NULL)
GROUP BY
u.user_id, u.full_name, u.email;
-- Create unique index on materialized view
CREATE UNIQUE INDEX idx_mv_agent_performance_user ON mv_agent_performance(user_id);
-- Refresh strategy (daily scheduled job)
REFRESH MATERIALIZED VIEW CONCURRENTLY mv_agent_performance;
SLA Tracking Queries

This is the start of the file. Read the full SKILL.md on GitHub.

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Source

Popularity

Repository stars
62
Repository forks
14
7-day trend score
21

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 18 Mar 2026, 7 months ago.

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