marklogic-data-modeling

marklogic-data-modeling is a skill for Claude Code, Codex from tternquist/marklogic-mcp. It costs 117 tokens per session (2,021 once invoked), scanned A, original, MIT.

A guide for designing MarkLogic databases that combine documents, linked facts, and vector data. It also covers document naming, collections, and data-integration envelopes.

In plain words
What is it for?
Use it when modelling a new domain in MarkLogic, deciding between nested documents and linked facts, planning document URIs and collections, or preparing query views.
Why use it?
It helps choose the simplest data model that supports the queries you need, instead of adding relationships, similarity search, or analytics without a clear purpose.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/tternquist/marklogic-mcp/marklogic-data-modeling
Any agent
npx skills add tternquist/marklogic-mcp --skill marklogic-data-modeling
Clone the repo
git clone --depth 1 https://github.com/tternquist/marklogic-mcp

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for marklogic-data-modeling

README.md
[![agentmods](https://agentmods.dev/badge/skills/tternquist/marklogic-mcp/marklogic-data-modeling.svg)](https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-data-modeling)
Your own site
<a href="https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-data-modeling"><img src="https://agentmods.dev/badge/skills/tternquist/marklogic-mcp/marklogic-data-modeling.svg" alt="Measured on agentmods" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,021 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00117 $0.02021
Opus 5 $0.00059 $0.01010
Sonnet 5 $0.00023 $0.00404
Haiku 4.5 $0.00012 $0.00202

Measured 3d ago against content hash 5d38fcaad122, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

marklogic-data-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/marklogic-data-modeling/SKILL.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MarkLogic Data Modeling

Three decisions, in order: which models, how documents are shaped and named, and whether an envelope is warranted.

1. Model selection

Choose the least machinery that answers the query goals.

Use When Skip it when
Documents always — the base model never
Triples cross-entity relationships, graph traversal, taxonomy links relationships are simple parent-child → just nest them in the document
Vectors semantic similarity, RAG, recommendations no similarity requirement
TDE / Optic GROUP BY, joins, aggregation no analytical queries

State for each model you keep: why it fits, what it holds, what query capability it unlocks. Being explicit about what you are not using is as valuable as the inclusions.

2. Document design

  • Collection strategy — one collection per entity type; a <source>-raw collection per source system when harmonizing.
  • URI pattern — see §3.
  • Range index candidates — the fields used for filtering, sorting, or ml_values_query. Check what exists with ml_indexes_list.
  • TDE view candidates — the fields needing GROUP BY or JOIN.
  • Never store "". Empty strings pollute range indexes, break range queries, and create misleading TDE rows. Omit the field, or use null.
  • Dates as ISO-8601 strings, to match a dateTime range index scalar type.

3. URI design — six rules

URIs are stable identity. Get them wrong and every later query is awkward.

  1. Prefix with the collection or entity type. The prefix is the directory; ml_document_list scopes to it. /orders/order-{orderId}.json ✓ — /{orderId}.json ✗ (no grouping possible)
  2. Embed every primary key value. Deterministic and collision-free. Avoid UUIDs unless the source has no key. /events/gdelt/{GlobalEventID}.json ✓ — /orders/order.json ✗ (overwrites on every write)
  3. Match the URI prefix to the collection short name. Collection orders → URIs under /orders/.
  4. URL-safe characters only — letters, digits, /, -, _, .. Replace spaces and colons with -; turn slashes in values into path segments.
  5. Right extension.json / .xml for content, .sjs / .xqy for modules (Modules database), .tdej for JSON TDE templates (Schemas database).
  6. Nest child entities under the parent key. /customers/{customerId}/orders/{orderId}.json lets you list one customer's orders by directory.

Read the full file on GitHub · 173 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 173 lines · 117 tokens per session scan A 5d38fcaad122

Subscribe to this mod's changes

marklogic-data-modeling is a skill published in the GitHub repository tternquist/marklogic-mcp (3 stars, last pushed 13d ago), licensed MIT. It adds 117 tokens to every session and 2,021 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

schema-exploration

Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.

langchain-ai/deepagents · 57 tokens

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

deploy-docker-compose

Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…

omnigent-ai/omnigent · 84 tokens

moderator-page-migration

Port a moderator page from the main Next.js app (src/pages/moderator/) into apps/moderator. Use when asked to migrate, move or cut over a /moderator/ page to the spoke, or to port its tRPC procedures and Prisma services to SvelteKit loads/actions and Kysely.

civitai/civitai · 71 tokens

dsql

Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key…

awslabs/agent-plugins · 229 tokens

sql-translate

Translate SQL queries between database dialects (Snowflake, BigQuery, PostgreSQL, MySQL, etc.).

AltimateAI/altimate-code · 26 tokens