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Agent skill

Using Document Databases

by ancoleman in ancoleman/ai-design-components

Document database implementation for flexible schema applications.

MITAuto-check passedDatabases

Install Using Document Databases

skills CLI
$ npx skills add ancoleman/ai-design-components --skill using-document-databases -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ancoleman/ai-design-components using-document-databases --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/using-document-databases .claude/skills/using-document-databases && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
using-document-databases
GitHub stars
526
Token cost
~2.1k tokens
SKILL.md length
523 words
Files
21 (incl. scripts, references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

Document database implementation for flexible schema applications.

  • Building content management
  • SKILL.md covers When to Use This Skill, Database Selection, Schema Design Patterns and Indexing Strategies, plus 11 more sections
  • Runs Python scripts from its folder; calls python, pip and npm
  • Tasks that involve NoSQL databases

What it does

Using Document Databases is an agent skill from ancoleman/ai-design-components. Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 26 other files, including scripts and reference files (for example `README.md`, `examples/dynamodb-serverless/handler.py` and `examples/dynamodb-serverless/serverless.yml`).

It sits in Databases, covering NoSQL databases. It works with MongoDB, Amazon DynamoDB and Cloud Firestore. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Building content management
  • Tasks that involve NoSQL databases

Example prompts

  • “/using-document-databases”

Requirements

  • Python 3
  • Node.js

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pip
    • npm
    • go

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip and npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Using Document Databases loads about 2.1k tokens when it runs, and up to ~31k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 523 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~31k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 523 words, ~2,094 tokens.

Download SKILL.mdSave it as .claude/skills/using-document-databases/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.
name
using-document-databases
description
Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

Document Database Implementation

Guide NoSQL document database selection and implementation for flexible schema applications across Python, TypeScript, Rust, and Go.

When to Use This Skill

Use document databases when applications need:

  • Flexible schemas - Data models evolve rapidly without migrations
  • Nested structures - JSON-like hierarchical data
  • Horizontal scaling - Built-in sharding and replication
  • Developer velocity - Object-to-database mapping without ORM complexity

Database Selection

Quick Decision Framework
DEPLOYMENT ENVIRONMENT?
├── AWS-Native Application → DynamoDB
│   ✓ Serverless, auto-scaling, single-digit ms latency
│   ✗ Limited query flexibility
│
├── Firebase/GCP Ecosystem → Firestore
│   ✓ Real-time sync, offline support, mobile-first
│   ✗ More expensive for heavy reads
│
└── General-Purpose/Complex Queries → MongoDB
    ✓ Rich aggregation, full-text search, vector search
    ✓ ACID transactions, self-hosted or managed
Database Comparison
DatabaseBest ForLatencyMax ItemQuery Language
MongoDBGeneral-purpose, complex queries1-5ms16MBMQL (rich)
DynamoDBAWS serverless, predictable performance<10ms400KBPartiQL (limited)
FirestoreReal-time apps, mobile-first50-200ms1MBFirebase queries

See references/mongodb.md for MongoDB details See references/dynamodb.md for DynamoDB single-table design See references/firestore.md for Firestore real-time patterns

Schema Design Patterns

Embedding vs Referencing

Use the decision matrix in references/schema-design-patterns.md

Quick guide:

RelationshipPatternExample
One-to-FewEmbedUser addresses (2-3 max)
One-to-ManyHybridBlog posts → comments
One-to-MillionsReferenceUser → events (logging)
Many-to-ManyReferenceProducts ↔ Categories
Embedding Example (MongoDB)
javascript
// User with embedded addresses
{
  _id: ObjectId("..."),
  email: "user@example.com",
  name: "Jane Doe",
  addresses: [
    {
      type: "home",
      street: "123 Main St",
      city: "Boston",
      default: true
    }
  ],
  preferences: {
    theme: "dark",
    notifications: { email: true, sms: false }
  }
}
Referencing Example (E-commerce)
javascript
// Orders reference products
{
  _id: ObjectId("..."),
  userId: ObjectId("..."),
  items: [
    {
      productId: ObjectId("..."),      // Reference
      priceAtPurchase: 49.99,          // Denormalize (historical)
      quantity: 2
    }
  ],
  totalAmount: 99.98
}

When to denormalize:

  • Frequently read together
  • Historical snapshots (prices, names)
  • Read-heavy workloads

Indexing Strategies

MongoDB Index Types
javascript
// 1. Single field (unique email)
db.users.createIndex({ email: 1 }, { unique: true })

// 2. Compound index (ORDER MATTERS!)
db.orders.createIndex({ status: 1, createdAt: -1 })

// 3. Partial index (index subset)
db.orders.createIndex(
  { userId: 1 },
  { partialFilterExpression: { status: { $eq: "pending" }}}
)

// 4. TTL index (auto-delete after 30 days)
db.sessions.createIndex(
  { createdAt: 1 },
  { expireAfterSeconds: 2592000 }
)

// 5. Text index (full-text search)
db.articles.createIndex({
  title: "text",
  content: "text"
})

Index Best Practices:

  • Add indexes for all query filters
  • Compound index order: Equality → Range → Sort
  • Use covering indexes (query + projection in index)
  • Use explain() to verify index usage
  • Monitor with Performance Advisor (Atlas)

Validate indexes with the script:

bash
python scripts/validate_indexes.py

See references/indexing-strategies.md for complete guide.

MongoDB Aggregation Pipelines

Key Operators: $match (filter), $group (aggregate), $lookup (join), $unwind (arrays), $project (reshape)

For complete pipeline patterns and examples, see: references/aggregation-patterns.md

DynamoDB Single-Table Design

Design for access patterns using PK/SK patterns. Store multiple entity types in one table with composite keys.

For complete single-table design patterns and GSI strategies, see: references/dynamodb.md

Firestore Real-Time Patterns

Use onSnapshot() for real-time listeners and Firestore security rules for access control.

For complete real-time patterns and security rules, see: references/firestore.md

Multi-Language Examples

Complete implementations available in examples/ directory:

  • examples/mongodb-fastapi/ - Python FastAPI + MongoDB
  • examples/mongodb-nextjs/ - TypeScript Next.js + MongoDB
  • examples/dynamodb-serverless/ - Python Lambda + DynamoDB
  • examples/firestore-react/ - React + Firestore real-time
Show full SKILL.md (205 more words)Show less

Frontend Skill Integration

  • Media Skill - Use MongoDB GridFS for large file storage with metadata
  • AI Chat Skill - MongoDB Atlas Vector Search for semantic conversation retrieval
  • Feedback Skill - DynamoDB for high-throughput event logging with TTL

For integration examples, see: references/skill-integrations.md

Performance Optimization

Key practices:

  • Always use indexes for query filters (verify with .explain())
  • Use connection pooling (reuse clients across requests)
  • Avoid collection scans in production

For complete optimization guide, see: references/performance.md

Common Patterns

Pagination: Use cursor-based pagination for large datasets (recommended over offset) Soft Deletes: Mark as deleted with timestamp instead of removing Audit Logs: Store version history within documents

For implementation details, see: references/common-patterns.md

Validation and Scripts

Validate Index Coverage
bash
# Run validation script
python scripts/validate_indexes.py --db myapp --collection orders

# Output:
# ✓ Query { status: "pending" } covered by index status_1
# ✗ Query { userId: "..." } missing index - add: { userId: 1 }
Schema Analysis
bash
# Analyze schema patterns
python scripts/analyze_schema.py --db myapp

# Output:
# Collection: users
# - Average document size: 2.4 KB
# - Embedding ratio: 87% (addresses, preferences)
# - Reference ratio: 13% (orderIds)
# Recommendation: Good balance

Anti-Patterns to Avoid

Unbounded Arrays: Limit embedded arrays (use references for large collections) Over-Indexing: Only index queried fields (indexes slow writes) DynamoDB Scans: Always use Query with partition key (avoid Scan)

For detailed anti-patterns, see: references/anti-patterns.md

Dependencies

Python
bash
# MongoDB
pip install motor pymongo

# DynamoDB
pip install boto3

# Firestore
pip install firebase-admin
TypeScript
bash
# MongoDB
npm install mongodb

# DynamoDB
npm install @aws-sdk/client-dynamodb @aws-sdk/util-dynamodb

# Firestore
npm install firebase firebase-admin
Rust
toml
# MongoDB
mongodb = "2.8"

# DynamoDB
aws-sdk-dynamodb = "1.0"
Go
bash
# MongoDB
go get go.mongodb.org/mongo-driver

# DynamoDB
go get github.com/aws/aws-sdk-go-v2/service/dynamodb

Additional Resources

Database-Specific Guides:

  • references/mongodb.md - Complete MongoDB documentation
  • references/dynamodb.md - DynamoDB single-table patterns
  • references/firestore.md - Firestore real-time guide

Pattern Guides:

  • references/schema-design-patterns.md - Embedding vs referencing decisions
  • references/indexing-strategies.md - Index optimization
  • references/aggregation-patterns.md - MongoDB pipeline cookbook
  • references/common-patterns.md - Pagination, soft deletes, audit logs
  • references/anti-patterns.md - Mistakes to avoid
  • references/performance.md - Query optimization
  • references/skill-integrations.md - Frontend skill integration

Examples: examples/mongodb-fastapi/, examples/mongodb-nextjs/, examples/dynamodb-serverless/, examples/firestore-react/

© ancoleman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 20 other files (scripts, references) in skills/using-document-databases of ancoleman/ai-design-components.

  • SKILL.md
  • README.md
  • examples/dynamodb-serverless/handler.py
  • examples/dynamodb-serverless/serverless.yml
  • examples/firestore-react/README.md
  • examples/mongodb-fastapi/main.py
  • examples/mongodb-fastapi/requirements.txt
  • examples/mongodb-nextjs/README.md
  • outputs.yaml
  • references/aggregation-patterns.md
  • references/anti-patterns.md
  • references/common-patterns.md
  • references/dynamodb.md
  • references/firestore.md
  • references/indexing-strategies.md
  • … and 6 more

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Using Document Databases next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Using Document Databases compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Using Document Databases this skillancoleman/ai-design-components526—~2.1kAutomated safety check: PassMIT
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Database Domain Specialistmodu-ai/moai-adk1.2k—~2.8kAutomated safety check: PassApache-2.0
Modeling Nosql Datajeremylongshore/tons-of-skills-marketplace2.8k—~1.7kAutomated safety check: PassMIT
Dynamodbericrisco/rsc-harness167—~3kAutomated safety check: PassMIT
Ak Add Capabilitiesyaalalabs/agent-kernel191—~12kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Using Document Databases

What does Using Document Databases do?

Document database implementation for flexible schema applications. Using Document Databases is an agent skill from ancoleman/ai-design-components. Document database implementation for flexible schema applications.

When should I use Using Document Databases?

Using Document Databases fits situations like: building content management; tasks that involve NoSQL databases.

How do I install Using Document Databases in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill using-document-databases -a claude-code`. Or copy the skill folder (skills/using-document-databases in ancoleman/ai-design-components) into .claude/skills/using-document-databases in your project. Claude Code loads it when a task matches its description.

How do I install Using Document Databases in Codex?

Run `npx skills add ancoleman/ai-design-components --skill using-document-databases -a codex`. Or copy the skill folder (skills/using-document-databases in ancoleman/ai-design-components) into .agents/skills/using-document-databases in your project. Codex loads it when a task matches its description.

Can I use Using Document Databases in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ancoleman/ai-design-components --skill using-document-databases -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/using-document-databases, .gemini/skills/using-document-databases, .github/skills/using-document-databases and .opencode/skills/using-document-databases in your project.

What does Using Document Databases need to run?

Going by SKILL.md and its folder, Using Document Databases needs Python for the scripts in its folder and the command-line tools its instructions call (python, pip, npm and go). Our summary lists: Python 3; Node.js.

Does Using Document Databases access the network?

SKILL.md contains no URLs. Its commands use pip and npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Using Document Databases safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Using Document Databases use?

Using Document Databases is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Using Document Databases use?

About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 28k tokens, read only when the agent opens those files.

What are the alternatives to Using Document Databases?

Skills that share tags, products or a category with Using Document Databases: DB Sculptor (EliasOulkadi/shokunin, 114 stars), Database Domain Specialist (modu-ai/moai-adk, 1.2k stars), Modeling Nosql Data (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and Dynamodb (ericrisco/rsc-harness, 167 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Using Document Databases?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 526 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.