mcpbeat Sign in

Mongodb Patterns Agent Skill

Document modeling, aggregation pipeline, indexing strategy, change streams, and multi-document transactions.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
521
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/vibeeval/vibecosystem --skill mongodb-patterns

The instruction itself

8 sections, as written by the author

MongoDB Patterns

Document database design and query optimization for MongoDB.

Document Modeling Strategies

// EMBED when: 1:1 or 1:few, data read together, child has no independent lifecycle
interface Order {
  _id: ObjectId
  customerId: ObjectId
  status: 'pending' | 'paid' | 'shipped'
  items: OrderItem[]        // Embedded - always read with order
  shippingAddress: Address  // Embedded - 1:1
  createdAt: Date
}

interface OrderItem {
  productId: ObjectId
  name: string              // Denormalized - avoid join at read time
  price: number             // Snapshot at purchase time
  quantity: number
}

// REFERENCE when: 1:many (unbounded), independent queries, shared across documents
interface Product {
  _id: ObjectId
  name: string
  price: number
  categoryId: ObjectId     // Reference - category queried independently
  reviews: never           // DON'T embed - unbounded array
}

// Bucket pattern: group time-series data into fixed-size documents
interface SensorBucket {
  _id: ObjectId
  sensorId: string
  startTime: Date
  endTime: Date
  count: number            // Track bucket fullness
  measurements: {          // Embed up to 200 per bucket
    timestamp: Date
    value: number
  }[]
}

Indexing Strategy

// Compound index: field order matters (ESR rule)
// Equality → Sort → Range
db.orders.createIndex({
  status: 1,       // Equality: exact match filter
  createdAt: -1,   // Sort: avoid in-memory sort
  total: 1         // Range: price > 100
})

// Partial index: only index documents matching filter (smaller index)
db.orders.createIndex(
  { customerId: 1, createdAt: -1 },
  { partialFilterExpression: { status: 'pending' } }
)

// Text index for search
db.products.createIndex({ name: 'text', description: 'text' })

// TTL index for auto-expiration
db.sessions.createIndex(
  { createdAt: 1 },
  { expireAfterSeconds: 86400 }  // Auto-delete after 24h
)

// Wildcard index for dynamic schemas
db.events.createIndex({ 'metadata.$**': 1 })

Aggregation Pipeline

// Sales analytics: top products by revenue per category
const pipeline = [
  // Stage 1: Filter date range
  { $match: {
    createdAt: { $gte: new Date('2025-01-01'), $lt: new Date('2025-02-01') },
    status: 'paid'
  }},

  // Stage 2: Unwind embedded items array
  { $unwind: '$items' },

  // Stage 3: Group by product
  { $group: {
    _id: '$items.productId',
    productName: { $first: '$items.name' },
    totalRevenue: { $sum: { $multiply: ['$items.price', '$items.quantity'] } },
    totalSold: { $sum: '$items.quantity' },
    orderCount: { $addToSet: '$_id' }
  }},

  // Stage 4: Add computed fields
  { $addFields: {
    orderCount: { $size: '$orderCount' },
    avgOrderValue: { $divide: ['$totalRevenue', { $size: '$orderCount' }] }
  }},

  // Stage 5: Sort by revenue descending
  { $sort: { totalRevenue: -1 } },

  // Stage 6: Limit to top 20
  { $limit: 20 },

  // Stage 7: Lookup category details
  { $lookup: {
    from: 'products',
    localField: '_id',
    foreignField: '_id',
    pipeline: [{ $project: { categoryId: 1 } }],
    as: 'product'
  }}
]

const results = await db.orders.aggregate(pipeline).toArray()

Change Streams (Real-time Reactivity)

async function watchOrderChanges(): Promise<void> {
  const pipeline = [
    { $match: {
      operationType: { $in: ['insert', 'update'] },
      'fullDocument.status': 'paid'
    }}
  ]

  // resumeAfter enables resuming from last processed change (crash recovery)
  const changeStream = db.orders.watch(pipeline, {
    fullDocument: 'updateLookup',  // Include full document on updates
    resumeAfter: await getLastResumeToken()
  })

  changeStream.on('change', async (event) => {
    try {
      await processOrderPayment(event.fullDocument!)
      await saveResumeToken(event._id)  // Persist for crash recovery
    } catch (err) {
      console.error('Change stream processing failed:', err)
    }
  })

  changeStream.on('error', (err) => {
    console.error('Change stream error:', err)
    // Reconnect with resume token
    setTimeout(() => watchOrderChanges(), 5000)
  })
}

Multi-Document Transactions

async function transferFunds(
  fromAccountId: string,
  toAccountId: string,
  amount: number
): Promise<void> {
  const session = client.startSession()

  try {
    await session.withTransaction(async () => {
      const from = await db.accounts.findOne(
        { _id: new ObjectId(fromAccountId) },
        { session }
      )
      if (!from || from.balance < amount) {
        throw new Error('Insufficient funds')
      }

      await db.accounts.updateOne(
        { _id: new ObjectId(fromAccountId) },
        { $inc: { balance: -amount } },
        { session }
      )

      await db.accounts.updateOne(
        { _id: new ObjectId(toAccountId) },
        { $inc: { balance: amount } },
        { session }
      )

      await db.transactions.insertOne({
        from: fromAccountId,
        to: toAccountId,
        amount,
        createdAt: new Date()
      }, { session })
    })
  } finally {
    await session.endSession()
  }
}

Checklist

  • [ ] Embed for 1:1 and 1:few; reference for 1:many and many:many
  • [ ] Follow ESR (Equality-Sort-Range) for compound index field order
  • [ ] Use partial indexes to reduce index size on filtered queries
  • [ ] Set TTL indexes for session/temp data auto-cleanup
  • [ ] Use aggregation pipeline for analytics (not client-side loops)
  • [ ] Change streams with resume tokens for crash-safe event processing
  • [ ] Keep documents under 16MB (MongoDB limit)
  • [ ] Use explain() to verify queries use indexes

Anti-Patterns

  • Unbounded arrays: reviews/comments embedded in parent (grows forever, hits 16MB)
  • Missing indexes: full collection scans on frequently queried fields
  • $lookup in hot paths: use denormalization, not joins, for read-heavy queries
  • Storing related data in separate collections when always read together
  • Using MongoDB as a relational database (normalize everything)
  • Not using write concern majority for critical writes (data loss risk)

Other skills for the same job

different authors, same section of the catalogue
Biorxiv Database
by christophacham
×4

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

9k tokens scripts
Brenda Database
by christophacham
×4

Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.

36k tokens scripts
Clinpgx Database
by christophacham
×4

Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.

13k tokens scripts
Clinvar Database
by christophacham
×4

Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.

10k tokens
Cosmic Database
by christophacham
×4

Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.

6k tokens scripts
Ensembl Database
by christophacham
×4

Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.

8k tokens scripts
Fda Database
by christophacham
×4

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

32k tokens scripts
Gene Database
by christophacham
×4

Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.

13k tokens scripts

How to use it

Copy the folder

Take vibeeval/mongodb-patterns from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.