Schema analyzer

Skill hoqo/claude-plugins/plugins/mongodb/skills/schema-analyzer

Claude Code plugins & skills

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npx -y skills add hoqo/claude-plugins --skill schema-analyzer

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Analyze MongoDB collection schemas, infer data types, detect inconsistencies, and recommend schema design improvements. Use when the user asks about database structure, data modeling, schema validation, or wants to understand their MongoDB collections.

SKILL.md

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MongoDB Schema Analyzer

You analyze MongoDB schemas to understand data structure, detect problems, and recommend improvements.

Analysis Process

Sample documents: Fetch 100 random documents from the collection
Infer types: Detect field types, nesting depth, array contents
Check consistency: Find fields that vary in type or presence
Assess design: Evaluate embedding vs referencing choices
Recommend: Suggest schema validation rules, indexes, and restructuring

Schema Discovery Script

Run this to analyze a collection's schema:

mongosh "$MONGODB_URI" --quiet --eval "
  const coll = '<COLLECTION>';
  const sample = db.getCollection(coll).aggregate([{ \$sample: { size: 100 } }]).toArray();

  const schema = {};
  sample.forEach(doc => {
    function analyze(obj, prefix) {
      Object.entries(obj).forEach(([key, val]) => {
        const path = prefix ? prefix + '.' + key : key;
        const type = Array.isArray(val) ? 'array' : typeof val;
        if (!schema[path]) schema[path] = { types: {}, count: 0 };
        schema[path].types[type] = (schema[path].types[type] || 0) + 1;
        schema[path].count++;
        if (type === 'object' && val !== null) analyze(val, path);
      });
    }
    analyze(doc, '');
  });

  Object.entries(schema)
    .sort(([a], [b]) => a.localeCompare(b))
    .forEach(([path, info]) => {
      const types = Object.entries(info.types).map(([t, c]) => t + '(' + c + ')').join(', ');
      print(path + '  |  ' + types + '  |  present in ' + info.count + '/100 docs');
    });
"

Schema Validation

Recommend JSON Schema validation rules when appropriate:

db.runCommand({
  collMod: "collection",
  validator: {
    $jsonSchema: {
      bsonType: "object",
      required: ["name", "email", "createdAt"],
      properties: {
        name: { bsonType: "string", description: "must be a string" },
        email: { bsonType: "string", pattern: "^.+@.+$" },
        age: { bsonType: "int", minimum: 0, maximum: 150 },
        createdAt: { bsonType: "date" }
      }
    }
  },
  validationLevel: "moderate",
  validationAction: "warn"
})

Schema Design Guidelines

When to Embed (denormalize)

Data is read together frequently
Child documents are bounded in number
One-to-few relationships
Data doesn't change independently

When to Reference (normalize)

Data is large or unbounded
Many-to-many relationships
Data is read independently
Documents would exceed 16MB limit

Anti-patterns to Flag

Arrays that grow without bound (unbounded arrays)
Deeply nested documents (>3 levels)
Storing large blobs in documents
Inconsistent field types across documents
Missing indexes on frequently queried fields
Using $where or JavaScript expressions in queries

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