Mirrord DB Branching
metalbear-co/mirrord
Helps users configure mirrord.json for database branching, enabling isolated database copies for safe development and testing.
Build use when you need to work with NoSQL data modeling. An agent skill from jeremylongshore/tons-of-skills-marketplace.
Install Modeling Nosql Data
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace modeling-nosql-data --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/modeling-nosql-data .claude/skills/modeling-nosql-data && rm -rf skills-srcUse ~/.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/
Install the "modeling-nosql-data" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-data into .claude/skills/modeling-nosql-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "modeling-nosql-data", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-dataType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace modeling-nosql-data --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.curated/modeling-nosql-data .agents/skills/modeling-nosql-data && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "modeling-nosql-data" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-data into .agents/skills/modeling-nosql-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "modeling-nosql-data", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace modeling-nosql-data --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.curated/modeling-nosql-data .cursor/skills/modeling-nosql-data && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "modeling-nosql-data" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-data into .cursor/skills/modeling-nosql-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "modeling-nosql-data", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jeremylongshore/tons-of-skills-marketplace.git --path skills/.curated/modeling-nosql-data--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace modeling-nosql-data --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.curated/modeling-nosql-data .gemini/skills/modeling-nosql-data && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "modeling-nosql-data" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-data into .gemini/skills/modeling-nosql-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "modeling-nosql-data", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jeremylongshore/tons-of-skills-marketplace modeling-nosql-dataInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.curated/modeling-nosql-data .github/skills/modeling-nosql-data && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "modeling-nosql-data" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-data into .github/skills/modeling-nosql-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "modeling-nosql-data", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace modeling-nosql-data --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.curated/modeling-nosql-data .opencode/skills/modeling-nosql-data && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "modeling-nosql-data" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/modeling-nosql-data into .opencode/skills/modeling-nosql-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "modeling-nosql-data", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
modeling-nosql-data- GitHub stars
- 2.8k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 803 words
- Files
- 7 (incl. scripts, references, assets)
- Skills in repo
- 3,342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build use when you need to work with NoSQL data modeling. An agent skill from jeremylongshore/tons-of-skills-marketplace.
- Works in 10 steps: Catalog all application access patterns… → For MongoDB document modeling, apply the… → Design document schemas that match query… → …
- You need to work with NoSQL data modeling
- SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
- Runs Python scripts from its folder; calls aws
What it does
Modeling Nosql Data is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build use when you need to work with NoSQL data modeling. This skill provides NoSQL database design with comprehensive guidance and automation. Trigger with phrases like "model NoSQL data", "design document structure", or "optimize NoSQL schema".
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts, reference files and assets (for example `assets/README.md`, `references/README.md` and `scripts/README.md`). Compatibility notes: Designed for Claude Code
It sits in Databases, covering NoSQL databases. It works with MongoDB and Amazon DynamoDB. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.
When your agent uses it
- You need to work with NoSQL data modeling
- With phrases like model NoSQL data
- Design document structure
- Optimize NoSQL schema
Example prompts
- “model NoSQL data”
- “design document structure”
- “optimize NoSQL schema”
- “/modeling-nosql-data”
Requirements
- Python 3
- Compatibility (from SKILL.md): Designed for Claude Code
- Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(psql:*), Bash(mysql:*), Bash(mongosh:*)
Workflow steps
10 steps, taken from the first numbered list in SKILL.md.
- Catalog all application access patterns as a table with columns: pattern name, query description, frequency (queries/sec), latency…
- For MongoDB document modeling, apply the embedding vs. referencing decision framework
- Design document schemas that match query patterns. If the application needs "all orders for a customer with line items," embed line items…
- For DynamoDB, design the partition key and sort key to support the primary access pattern with a single-table design. Use composite sort…
- Evaluate denormalization trade-offs: duplicating data across documents reduces read latency but increases write complexity and storage…
- Handle one-to-many relationships by choosing between embedding (small arrays), child referencing (parent stores child IDs), or parent…
- Model many-to-many relationships using an array of references in each document or a dedicated junction collection. For DynamoDB, use…
- Plan for schema evolution by using schema versioning fields (schemaVersion: 2), writing migration scripts that update documents in…
- Validate the model against access patterns by running sample queries with explain() in MongoDB or examining consumed capacity units in…
- Document the final data model with sample documents, index definitions, and the access pattern mapping that justifies each modeling…
What it can do on your machine
Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditGrepGlobBash(psql:*)Bash(mysql:*)Bash(mongosh:*)
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
aws
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
mongodb.comdocs.aws.amazon.comredis.io
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.
Compatibility
Designed for Claude Code
From compatibility in the SKILL.md frontmatter.
Context cost
Modeling Nosql Data loads about 1.7k tokens when it runs, and up to ~1.7k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 803 words of instructions outside code blocks.
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 jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 803 words, ~1,697 tokens.
Download SKILL.mdSave it as
.claude/skills/modeling-nosql-data/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- modeling-nosql-data
- description
- Build use when you need to work with NoSQL data modeling. This skill provides NoSQL database design with comprehensive guidance and automation. Trigger with phrases like "model NoSQL data", "design document structure", or "optimize NoSQL schema".
- allowed-tools
- Read, Write, Edit, Grep, Glob, Bash(psql:*), Bash(mysql:*), Bash(mongosh:*)
- compatibility
- Designed for Claude Code
- version
- 1.29.0
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- tags
- database, modeling-nosql
NoSQL Data Modeler
Overview
Design data models for NoSQL databases including MongoDB (document), DynamoDB (key-value/wide-column), Redis (key-value), and Cassandra (wide-column). Unlike relational modeling where normalization drives design, NoSQL modeling starts from access patterns and query requirements, then shapes the data to serve those patterns efficiently.
Prerequisites
mongosh,aws dynamodbCLI,redis-cli, orcqlshinstalled depending on target database- Documented list of application access patterns (read/write queries the application performs)
- Expected data volumes (document count, average document size, growth rate)
- Read/write ratio and latency requirements for each access pattern
- Understanding of consistency requirements (strong vs. eventual consistency)
Instructions
Catalog all application access patterns as a table with columns: pattern name, query description, frequency (queries/sec), latency requirement, and data fields accessed. This drives every modeling decision.
For MongoDB document modeling, apply the embedding vs. referencing decision framework:
- Embed when: data is always accessed together, child data has no independent lifecycle, cardinality is bounded (1:few), and updates are infrequent.
- Reference when: data has independent access patterns, cardinality is unbounded (1:many/many:many), child documents are large, or data is shared across parents.
Design document schemas that match query patterns. If the application needs "all orders for a customer with line items," embed line items inside the order document. If the application needs "all products across all orders," use references to a products collection.
For DynamoDB, design the partition key and sort key to support the primary access pattern with a single-table design. Use composite sort keys (e.g.,
ORDER#2024-01-15#12345) for hierarchical data. Plan GSIs (Global Secondary Indexes) for secondary access patterns, keeping total GSI count under 5.Evaluate denormalization trade-offs: duplicating data across documents reduces read latency but increases write complexity and storage. Denormalize data that changes rarely (user names, product categories) but reference data that changes frequently (prices, inventory counts).
Handle one-to-many relationships by choosing between embedding (small arrays), child referencing (parent stores child IDs), or parent referencing (child stores parent ID). For unbounded one-to-many, always use parent referencing to avoid document size limits (16MB in MongoDB).
Model many-to-many relationships using an array of references in each document or a dedicated junction collection. For DynamoDB, use adjacency list patterns with inverted GSIs.
Plan for schema evolution by using schema versioning fields (
schemaVersion: 2), writing migration scripts that update documents in batches, and ensuring application code handles both old and new document shapes during rollout.Validate the model against access patterns by running sample queries with
explain()in MongoDB or examining consumed capacity units in DynamoDB. Verify that primary access patterns require only single-partition reads.Document the final data model with sample documents, index definitions, and the access pattern mapping that justifies each modeling decision.
Output
- Data model diagrams showing document/collection structure, embedded vs. referenced relationships
- Sample documents in JSON format for each collection/table with realistic data
- Index definitions including compound indexes, partial indexes, and TTL indexes
- Access pattern mapping table linking each query to its supporting collection and index
- Migration scripts for evolving schemas from existing relational models to NoSQL
Show full SKILL.md (298 more words)Show less
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Document exceeds 16MB size limit (MongoDB) | Unbounded array growth from embedding too many child documents | Switch from embedding to referencing; use the bucket pattern to chunk large arrays into fixed-size sub-documents |
| Hot partition in DynamoDB | Partition key with low cardinality causes uneven distribution | Add a random suffix or use a composite key; distribute writes across partitions with write sharding |
| High read latency on referenced documents | Too many round trips to resolve references (N+1 query problem) | Denormalize frequently accessed reference data; use $lookup aggregation for server-side joins; batch reference resolution |
| Inconsistent denormalized data | Write to source succeeds but denormalized copies not updated | Implement change streams (MongoDB) or DynamoDB Streams to propagate updates; use transactional writes where supported |
| Query requires full collection scan | Missing index on query filter fields | Create compound indexes matching query predicates and sort order; use explain() to verify index usage |
Examples
E-commerce product catalog in MongoDB: Products embed variant arrays (size, color, price) since variants are always accessed with the product. Reviews reference the product by ID since reviews are accessed independently and grow unboundedly. A compound index on {category: 1, price: 1} supports filtered browsing.
Social media feed in DynamoDB single-table design: Partition key is USER#userId, sort key is POST#timestamp for user timeline queries. A GSI with partition key HASHTAG#tag and sort key timestamp supports hashtag feeds. User profile data uses sort key PROFILE on the same partition.
IoT sensor data in Cassandra: Partition key is sensor_id, clustering column is timestamp DESC. Each partition holds one sensor's readings, ordered by time. TTL of 90 days automatically expires old readings. Materialized views support queries by location and sensor type.
Resources
- MongoDB data modeling patterns: https://www.mongodb.com/docs/manual/data-modeling/
- DynamoDB single-table design: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-modeling-nosql.html
- Cassandra data modeling guide:
- Redis data structures: https://redis.io/docs/data-types/
- NoSQL design patterns catalog:
© jeremylongshore, 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 6 other files (scripts, references, assets) in skills/.curated/modeling-nosql-data of jeremylongshore/tons-of-skills-marketplace.
- SKILL.md
- assets/README.md
- references/README.md
- scripts/README.md
- scripts/generate_sample_data.py
- scripts/migrate_schema.py
- scripts/validate_schema.py
Open the folder on GitHubat commit cfae287
Compare with similar skills
Modeling Nosql Data 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Modeling Nosql Data this skilljeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Mirrord DB Branchingmetalbear-co/mirrord | 5.4k | — | ~14k | Automated safety check: Pass | MIT | |
| DB SculptorEliasOulkadi/shokunin | 114 | — | ~3.1k | Automated safety check: Notes | MIT | |
| Using Document Databasesancoleman/ai-design-components | 525 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Dynamodbericrisco/rsc-harness | 180 | — | ~3k | Automated safety check: Pass | MIT | |
| Database Designerborghei/Claude-Skills | 891 | — | ~1.6k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Modeling Nosql Data
What does Modeling Nosql Data do?
Build use when you need to work with NoSQL data modeling. An agent skill from jeremylongshore/tons-of-skills-marketplace. Modeling Nosql Data is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build use when you need to work with NoSQL data modeling.
When should I use Modeling Nosql Data?
Modeling Nosql Data fits situations like: you need to work with NoSQL data modeling; with phrases like model NoSQL data; design document structure; optimize NoSQL schema.
How do I install Modeling Nosql Data in Claude Code?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a claude-code`. Or copy the skill folder (skills/.curated/modeling-nosql-data in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/modeling-nosql-data in your project. Claude Code loads it when a task matches its description.
How do I install Modeling Nosql Data in Codex?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a codex`. Or copy the skill folder (skills/.curated/modeling-nosql-data in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/modeling-nosql-data in your project. Codex loads it when a task matches its description.
Can I use Modeling Nosql Data 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 jeremylongshore/tons-of-skills-marketplace --skill modeling-nosql-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/modeling-nosql-data, .gemini/skills/modeling-nosql-data, .github/skills/modeling-nosql-data and .opencode/skills/modeling-nosql-data in your project.
What does Modeling Nosql Data need to run?
Going by SKILL.md and its folder, Modeling Nosql Data needs Python for the scripts in its folder and the command-line tools its instructions call (aws). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(psql:*), Bash(mysql:*), Bash(mongosh:*). Compatibility (from SKILL.md): Designed for Claude Code.
Does Modeling Nosql Data access the network?
SKILL.md names 3 domains. As links in the text: mongodb.com, docs.aws.amazon.com and redis.io. This is read from the text; nothing was executed.
Is Modeling Nosql Data 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 Modeling Nosql Data use?
Modeling Nosql Data is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Modeling Nosql Data use?
About 1.7k tokens (SKILL.md is roughly 6.8k 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 16 tokens, read only when the agent opens those files.
What are the alternatives to Modeling Nosql Data?
Skills that share tags, products or a category with Modeling Nosql Data: Mirrord DB Branching (metalbear-co/mirrord, 5.4k stars), DB Sculptor (EliasOulkadi/shokunin, 114 stars), Using Document Databases (ancoleman/ai-design-components, 525 stars) and Dynamodb (ericrisco/rsc-harness, 180 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Modeling Nosql Data?
jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.
Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.