Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Unified guide for working with MongoDB (document-oriented) and PostgreSQL (relational) databases. Choose the right database for your use case and master both systems.
Use when:
Best for: Content management, catalogs, IoT time series, real-time analytics, mobile apps, user profiles
Best for: Financial systems, e-commerce transactions, ERP, CRM, data warehousing, analytics
# Atlas (Cloud) - Recommended
# 1. Sign up at mongodb.com/atlas
# 2. Create M0 free cluster
# 3. Get connection string
# Connection
mongodb+srv://user:pass@cluster.mongodb.net/db
# Shell
mongosh "mongodb+srv://cluster.mongodb.net/mydb"
# Basic operations
db.users.insertOne({ name: "Alice", age: 30 })
db.users.find({ age: { $gte: 18 } })
db.users.updateOne({ name: "Alice" }, { $set: { age: 31 } })
db.users.deleteOne({ name: "Alice" })
# Ubuntu/Debian
sudo apt-get install postgresql postgresql-contrib
# Start service
sudo systemctl start postgresql
# Connect
psql -U postgres -d mydb
# Basic operations
CREATE TABLE users (id SERIAL PRIMARY KEY, name TEXT, age INT);
INSERT INTO users (name, age) VALUES ('Alice', 30);
SELECT * FROM users WHERE age >= 18;
UPDATE users SET age = 31 WHERE name = 'Alice';
DELETE FROM users WHERE name = 'Alice';
// MongoDB
db.users.insertOne({ name: "Bob", email: "bob@example.com" })
db.users.insertMany([{ name: "Alice" }, { name: "Charlie" }])
-- PostgreSQL
INSERT INTO users (name, email) VALUES ('Bob', 'bob@example.com');
INSERT INTO users (name, email) VALUES ('Alice', NULL), ('Charlie', NULL);
// MongoDB
db.users.find({ age: { $gte: 18 } })
db.users.findOne({ email: "bob@example.com" })
-- PostgreSQL
SELECT * FROM users WHERE age >= 18;
SELECT * FROM users WHERE email = 'bob@example.com' LIMIT 1;
// MongoDB
db.users.updateOne({ name: "Bob" }, { $set: { age: 25 } })
db.users.updateMany({ status: "pending" }, { $set: { status: "active" } })
-- PostgreSQL
UPDATE users SET age = 25 WHERE name = 'Bob';
UPDATE users SET status = 'active' WHERE status = 'pending';
// MongoDB
db.users.deleteOne({ name: "Bob" })
db.users.deleteMany({ status: "deleted" })
-- PostgreSQL
DELETE FROM users WHERE name = 'Bob';
DELETE FROM users WHERE status = 'deleted';
// MongoDB
db.users.createIndex({ email: 1 })
db.users.createIndex({ status: 1, createdAt: -1 })
-- PostgreSQL
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_status_created ON users(status, created_at DESC);
Database utility scripts in scripts/:
# Generate migration
python scripts/db_migrate.py --db mongodb --generate "add_user_index"
# Run backup
python scripts/db_backup.py --db postgres --output /backups/
# Check performance
python scripts/db_performance_check.py --db mongodb --threshold 100ms
| Feature | MongoDB | PostgreSQL |
|---|---|---|
| Data Model | Document (JSON/BSON) | Relational (Tables/Rows) |
| Schema | Flexible, dynamic | Strict, predefined |
| Query Language | MongoDB Query Language | SQL |
| Joins | $lookup (limited) | Native, optimized |
| Transactions | Multi-document (4.0+) | Native ACID |
| Scaling | Horizontal (sharding) | Vertical (primary), Horizontal (extensions) |
| Indexes | Single, compound, text, geo, etc | B-tree, hash, GiST, GIN, etc |
MongoDB:
PostgreSQL:
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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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