NoSQL Expert Patterns (Cassandra & DynamoDB)

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/nosql-expert

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

NoSQL Expert Patterns (Cassandra & DynamoDB)

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: The Mental Shift: SQL vs. Distributed NoSQL, Core Design Patterns, Specific Guidance, Expert Checklist, Common Anti-Patterns, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Designing for Scale: Moving beyond simple single-node databases to distributed clusters.
• Technology Selection: Evaluating or using Cassandra, ScyllaDB, or DynamoDB.
• Performance Tuning: Troubleshooting "hot partitions" or high latency in existing NoSQL systems.
• Microservices: Implementing "database-per-service" patterns where highly optimized reads are required.
• Use when the request clearly matches the imported source intent: Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

This skill provides professional mental models and design patterns for distributed wide-column and key-value stores (specifically Apache Cassandra and Amazon DynamoDB).

Unlike SQL (where you model data entities), or document stores (like MongoDB), these distributed systems require you to model your queries first.

Imported: The Mental Shift: SQL vs. Distributed NoSQL

The Golden Rule: In SQL, you design the data model to answer any query. In NoSQL, you design the data model to answer specific queries efficiently.

Examples

Example 1: Ask for the upstream workflow directly

Use @nosql-expert to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @nosql-expert against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @nosql-expert for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @nosql-expert using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/nosql-expert

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @monte-carlo-monitor-creation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @monte-carlo-prevent

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @monte-carlo-push-ingestion

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @monte-carlo-validation-notebook

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Core Design Patterns

1. Query-First Modeling (Access Patterns)

You typically cannot "add a query later" without migration or creating a new table/index.

Process:

1. List all Entities (User, Order, Product).
2. List all Access Patterns ("Get User by Email", "Get Orders by User sorted by Date").
3. Design Table(s) specifically to serve those patterns with a single lookup.

2. The Partition Key is King

Data is distributed across physical nodes based on the Partition Key (PK).

• Goal: Even distribution of data and traffic.
• Anti-Pattern: Using a low-cardinality PK (e.g.,

  status="active"

  or

  gender="m"

  ) creates Hot Partitions, limiting throughput to a single node's capacity.
• Best Practice: Use high-cardinality keys (User IDs, Device IDs, Composite Keys).

3. Clustering / Sort Keys

Within a partition, data is sorted on disk by the Clustering Key (Cassandra) or Sort Key (DynamoDB).

• This allows for efficient Range Queries (e.g.,

  WHERE user_id=X AND date > Y

  ).
• It effectively pre-sorts your data for specific retrieval requirements.

4. Single-Table Design (Adjacency Lists)

Primary use: DynamoDB (but concepts apply elsewhere)

Storing multiple entity types in one table to enable pre-joined reads.

USER#123

PROFILE

{ name: "Ian", email: "..." }

USER#123

ORDER#998

{ total: 50.00, status: "shipped" }

USER#123

ORDER#999

{ total: 12.00, status: "pending" }

• Query:

  PK="USER#123"

• Result: Fetches User Profile AND all Orders in one network request.

5. Denormalization & Duplication

Don't be afraid to store the same data in multiple tables to serve different query patterns.

• Table A:

  users_by_id

  (PK: uuid)
• Table B:

  users_by_email

  (PK: email)

Trade-off: You must manage data consistency across tables (often using eventual consistency or batch writes).

Imported: Specific Guidance

Apache Cassandra / ScyllaDB

• Primary Key Structure:

  ((Partition Key), Clustering Columns)

• No Joins, No Aggregates: Do not try to

  JOIN

  or

  GROUP BY

  . Pre-calculate aggregates in a separate counter table.
• Avoid

  ALLOW FILTERING

  : If you see this in production, your data model is wrong. It implies a full cluster scan.
• Writes are Cheap: Inserts and Updates are just appends to the LSM tree. Don't worry about write volume as much as read efficiency.
• Tombstones: Deletes are expensive markers. Avoid high-velocity delete patterns (like queues) in standard tables.

AWS DynamoDB

• GSI (Global Secondary Index): Use GSIs to create alternative views of your data (e.g., "Search Orders by Date" instead of by User).
  • Note: GSIs are eventually consistent.
• LSI (Local Secondary Index): Sorts data differently within the same partition. Must be created at table creation time.
• WCU / RCU: Understand capacity modes. Single-table design helps optimize consumed capacity units.
• TTL: Use Time-To-Live attributes to automatically expire old data (free delete) without creating tombstones.

Imported: Expert Checklist

Before finalizing your NoSQL schema:

• Access Pattern Coverage: Does every query pattern map to a specific table or index?
• Cardinality Check: Does the Partition Key have enough unique values to spread traffic evenly?
• Split Partition Risk: For any single partition (e.g., a single user's orders), will it grow indefinitely? (If > 10GB, you need to "shard" the partition, e.g.,

  USER#123#2024-01

  ).
• Consistency Requirement: Can the application tolerate eventual consistency for this read pattern?

Imported: Common Anti-Patterns

❌ Scatter-Gather: Querying all partitions to find one item (Scan). ❌ Hot Keys: Putting all "Monday" data into one partition. ❌ Relational Modeling: Creating

Author

Book

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.