Mongodb Mongoose

Optimized for current MongoDB server releases, Mongoose 8.x+, Node.js 22+, and TypeScript 5.5+.

Comprehensive guidance for MongoDB database design, Mongoose ODM patterns, and Atlas integration for Node.js/Next.js applications.

• Leverage native parallel subagent dispatch and 200k+ context windows where available.

When to Use This Skill

Use symptom -> action triggers: when one matches, apply this skill and verify with the protocol below.

• Designing MongoDB schemas and data models
• Building Mongoose models with validation and middleware
• Implementing the repository pattern for data access
• Writing aggregation pipelines for complex queries
• Managing MongoDB Atlas connections and configuration
• Integrating MongoDB with Next.js API routes
• Database migration strategies

---

Anti-Patterns

• Modeling documents like normalized tables by default: MongoDB performance depends on query-driven shape, not relational purity.
• Returning full hydrated documents for every request: Over-fetching and hydration overhead accumulate quickly in API paths.
• Adding middleware without write-path tests: Hooks can silently change create, update, and migration behavior.

Verification Protocol

Before claiming "skill applied successfully":

1. Pass/fail: The Mongodb Mongoose implementation names the target runtime, framework version, and affected files.
2. Pass/fail: Build, lint, test, or equivalent local validation is run for the changed surface.
3. Pass/fail: Edge cases for errors, dependency drift, and environment differences are addressed or explicitly out of scope.
4. Pressure-test scenario: Apply the workflow to a change that passes happy-path tests but fails one boundary condition.
5. Success metric: Zero untested success claims; every implementation claim maps to a command or artifact.

Before and After Example

// Before
const recipes = await Recipe.find({ author: userId }).populate('author');

// After
const recipes = await Recipe.find({ author: userId, isPublished: true })
  .select({ title: 1, slug: 1, createdAt: 1 })
  .sort({ createdAt: -1 })
  .lean();

Narrows the query shape, avoids unnecessary hydration, and aligns the result with the view model actually needed.

Schema Design

Data Modeling Principles

• Embed when data is accessed together and has a 1:few relationship
• Reference when data is accessed independently or has a 1:many/many:many relationship
• Design schemas around query patterns, not normalized relational models
• Use denormalization strategically for read performance

Mongoose Model Pattern

import mongoose from 'mongoose';

const recipeSchema = new mongoose.Schema({
  title: {
    type: String,
    required: [true, 'Title is required'],
    trim: true,
    maxlength: [200, 'Title cannot exceed 200 characters'],
    index: true,
  },
  slug: {
    type: String,
    unique: true,
    lowercase: true,
  },
  ingredients: [{
    name: { type: String, required: true },
    amount: { type: Number, required: true },
    unit: { type: String, enum: ['g', 'kg', 'ml', 'l', 'cup', 'tbsp', 'tsp', 'piece'] },
  }],
  author: {
    type: mongoose.Schema.Types.ObjectId,
    ref: 'User',
    required: true,
    index: true,
  },
  tags: [{ type: String, lowercase: true, trim: true }],
  isPublished: { type: Boolean, default: false },
}, {
  timestamps: true,
  toJSON: { virtuals: true },
  toObject: { virtuals: true },
});

// Indexes for common queries
recipeSchema.index({ title: 'text', tags: 'text' });
recipeSchema.index({ author: 1, createdAt: -1 });

// Virtual fields
recipeSchema.virtual('ingredientCount').get(function() {
  return this.ingredients.length;
});

// Pre-save middleware
recipeSchema.pre('save', function(next) {
  if (this.isModified('title')) {
    this.slug = this.title.toLowerCase().replace(/[^a-z0-9]+/g, '-');
  }
  next();
});

export const Recipe = mongoose.models.Recipe || mongoose.model('Recipe', recipeSchema);

Schema Best Practices

• Always define required, type, and validation rules
• Use timestamps: true for automatic createdAt/updatedAt
• Add indexes for frequently queried fields
• Use enum for fields with fixed values
• Define virtuals for computed properties
• Use middleware (pre/post hooks) for side effects

---

Repository Pattern

class RecipeRepository {
  async findAll(filter = {}, options = {}) {
    const { page = 1, limit = 20, sort = '-createdAt', populate = '' } = options;
    const skip = (page - 1) * limit;

    const [recipes, total] = await Promise.all([
      Recipe.find(filter)
        .sort(sort)
        .skip(skip)
        .limit(limit)
        .populate(populate)
        .lean(),
      Recipe.countDocuments(filter),
    ]);

    return {
      data: recipes,
      pagination: {
        page,
        limit,
        total,
        pages: Math.ceil(total / limit),
      },
    };
  }

  async findById(id) {
    return Recipe.findById(id).populate('author', 'name avatar').lean();
  }

  async create(data) {
    const recipe = new Recipe(data);
    return recipe.save();
  }

  async update(id, data) {
    return Recipe.findByIdAndUpdate(id, data, {
      new: true,
      runValidators: true,
    });
  }

  async delete(id) {
    return Recipe.findByIdAndDelete(id);
  }

  async search(query, options = {}) {
    return this.findAll(
      { $text: { $search: query } },
      { ...options, sort: { score: { $meta: 'textScore' } } }
    );
  }
}

export const recipeRepository = new RecipeRepository();

---

Aggregation Pipelines

Common Patterns

// Group recipes by tag with counts
const tagStats = await Recipe.aggregate([
  { $match: { isPublished: true } },
  { $unwind: '$tags' },
  { $group: { _id: '$tags', count: { $sum: 1 } } },
  { $sort: { count: -1 } },
  { $limit: 20 },
]);

// Author statistics with lookup
const authorStats = await Recipe.aggregate([
  { $group: {
    _id: '$author',
    recipeCount: { $sum: 1 },
    avgRating: { $avg: '$rating' },
  }},
  { $lookup: {
    from: 'users',
    localField: '_id',
    foreignField: '_id',
    as: 'authorInfo',
  }},
  { $unwind: '$authorInfo' },
  { $project: {
    name: '$authorInfo.name',
    recipeCount: 1,
    avgRating: { $round: ['$avgRating', 1] },
  }},
  { $sort: { recipeCount: -1 } },
]);

// Date-based analytics
const monthlyRecipes = await Recipe.aggregate([
  { $match: { createdAt: { $gte: new Date('2024-01-01') } } },
  { $group: {
    _id: { $dateToString: { format: '%Y-%m', date: '$createdAt' } },
    count: { $sum: 1 },
  }},
  { $sort: { _id: 1 } },
]);

---

Atlas Connection

Connection Setup (Next.js)

import mongoose from 'mongoose';

const MONGODB_URI = process.env.MONGODB_URI;

if (!MONGODB_URI) {
  throw new Error('MONGODB_URI environment variable is not defined');
}

let cached = global.mongoose;
if (!cached) {
  cached = global.mongoose = { conn: null, promise: null };
}

export async function connectDB() {
  if (cached.conn) return cached.conn;

  if (!cached.promise) {
    cached.promise = mongoose.connect(MONGODB_URI, {
      bufferCommands: false,
    });
  }

  cached.conn = await cached.promise;
  return cached.conn;
}

Connection Best Practices

• Cache connection in development to prevent multiple connections
• Use bufferCommands: false for explicit error handling
• Set connection pool size via maxPoolSize for production
• Use Atlas connection string with retryWrites=true&w=majority

---

Migration Strategies

Document Versioning

const userSchema = new mongoose.Schema({
  schemaVersion: { type: Number, default: 2 },
  // ... fields
});

userSchema.pre('save', function(next) {
  if (this.schemaVersion < 2) {
    // Migrate old fields to new format
    this.schemaVersion = 2;
  }
  next();
});

Batch Migration Script

async function migrateUsers() {
  const batchSize = 100;
  let processed = 0;
  let batch;

  do {
    batch = await User.find({ schemaVersion: { $lt: 2 } }).limit(batchSize);
    for (const user of batch) {
      user.schemaVersion = 2;
      await user.save();
      processed++;
    }
    console.log(`Migrated ${processed} users`);
  } while (batch.length === batchSize);
}

---

Performance Tips

• Use .lean() for read-only queries (returns plain objects, 5-10x faster)
• Use .select() to return only needed fields
• Create compound indexes matching your query patterns
• Use $project early in aggregation to reduce working set
• Avoid $lookup in high-frequency queries; denormalize instead
• Use explain() to analyze query performance

Troubleshooting

Issue	Solution
Slow queries	Add indexes, use .lean(), check with explain()
Connection timeouts	Check Atlas network access, increase pool size
Validation errors	Review schema constraints, check middleware order
Duplicate key errors	Ensure unique indexes, handle with try/catch
Memory issues	Use cursors for large datasets, limit batch sizes

---

Common Pitfalls

• Modeling data like a normalized relational schema by default: MongoDB performance depends on query-driven document shape, not tables-first design.
• Returning full hydrated documents everywhere: Hydration and over-fetching add cost when a lean projection would do.
• Adding middleware without explicit write-path tests: Hooks can silently change behavior in create, update, and migration flows.

References & Resources

Documentation

• Aggregation Reference — Pipeline stages, accumulator operators, and common aggregation recipes
• Indexing Strategies — Index types, ESR rule, compound indexes, and performance analysis

Scripts

• Seed Database — Zero-dependency MongoDB seeding script with sample recipe data

Examples

• Recipe API Example — Complete Mongoose + Next.js Recipe CRUD API with models, routes, and validation

---

<!-- MCP:START --> <!-- PORTABILITY:START -->

Cross-Client Portability

This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.

• GitHub Copilot: keep the folder in a Copilot-visible skill path or wrap the workflow in project instructions when folder discovery is unavailable.
• Claude Code: keep the folder in a local skills directory or a compatible plugin source.
• Codex: install or sync the folder into $CODEX_HOME/skills/mongodb-mongoose and restart Codex after major changes.

<!-- PORTABILITY:END -->

MCP Availability And Fallback

Preferred MCP Server: MongoDB MCP

• Fallback prompt: "Use the Mongodb Mongoose skill without MCP. Rely on the local SKILL.md, bundled references or scripts, and manual verification. Show the exact commands, evidence, and final checks you used before concluding."
• Use mongosh, MongoDB Atlas UI, local schema files, and Mongoose model inspection when the MCP server is unavailable.
• Validate indexes, queries, and aggregation pipelines against a local or staging database before finalizing changes.

<!-- MCP:END -->

Related Skills

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• sql-development: Use it when the workflow also needs SQL query, schema, and performance tuning work.
• code-quality: Use it when the workflow also needs two-stage review (spec compliance first, then code quality), maintainability, and refactoring guidance.