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.
npx agentmods add skills/contentrain/ai/contentrain-querynpx skills add Contentrain/ai --skill contentrain-querygit clone --depth 1 https://github.com/Contentrain/aiWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00046 | $0.01123 |
| Opus 5 | $0.00023 | $0.00562 |
| Sonnet 5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
contentrain-query 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- contentrain-sdk — 86% identical, 85 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contentrain Query SDK
@contentrain/query is a Prisma-pattern generated client that provides type-safe access to Contentrain content. Generated output lives in .contentrain/client/ — never edit it manually.
Quick Start
npx contentrain generate # Generate client from models
import { query, singleton, dictionary, document } from '#contentrain'
// Collection
const posts = query('blog-post').where('category', 'eq', 'engineering').sort('published_at', 'desc').limit(10).all()
// Singleton
const hero = singleton('hero').get()
// Dictionary
const t = dictionary('ui-texts').get()
const loginLabel = t['auth.login.button']
// Document
const doc = document('blog-post').bySlug('getting-started')
Runtime API
QueryBuilder (collections)
| Method | Description |
|---|---|
.all() |
Get all entries |
.first() |
Get first entry |
.count() |
Return count of matching entries |
.where(field, value) |
Equality filter (shorthand for eq) |
.where(field, op, value) |
Operator filter: eq, ne, gt, gte, lt, lte, in, contains |
.sort(field, direction?) |
Sort (asc/desc, default asc) |
.limit(n) |
Limit results |
.offset(n) |
Skip results |
.include(relation) |
Resolve relation (1 level deep) |
SingletonAccessor
| Method | Description |
|---|---|
.get() |
Get the singleton data |
.include(relation) |
Resolve relation fields |
DictionaryAccessor
| Method | Description |
|---|---|
.get() |
Get all key-value pairs |
.get(key) |
Get single value by key |
.get(key, params) |
Get with interpolation: {placeholder} → value |
DocumentQuery
| Method | Description |
|---|---|
.all() |
Get all documents |
.bySlug(slug) |
Get document by slug |
.first() |
Get first document |
.count() |
Return count of matching documents |
.where(field, value) |
Equality filter (shorthand) |
.where(field, op, value) |
Operator filter (same ops as QueryBuilder) |
.include(relation) |
Resolve relation fields |
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.
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.
- 2d ago First seen · 130 lines · 46 tokens per session scan A 994c179a3797
contentrain-query is a skill published in the GitHub repository Contentrain/ai (4 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 1,123 once invoked, about $0.0002 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.
Other skills, from other repositories
chinese-documentation
中文文档排版参考——中英文空格、全半角标点、术语保留、链接格式、中文文案排版指北约定。仅在用户显式 /chinese-documentation 时调用，不要根据上下文自动触发。.
inclusive-design
Use when working on inclusion, i18n/localization, global name/address forms, low-end devices, slow/metered networks, affordability, or first-time/low-confidence users; not WCAG/screen readers (see accessibility).
unreal-large-blueprint-analysis
Systematically analyze and translate large UE Blueprints (10+ functions) into C++ or other targets. Use when the user asks to convert a Blueprint to C++, audit a complex Blueprint, or understand a large Blueprint's full logic.
unreal-plugin-localization
翻译 UE 插件本地化 PO 文件。当用户要求本地化、翻译或国际化 Unreal Engine 插件时使用。 处理 GatherText 采集、PO 导出、AI 翻译、PO 导入和 locres 编译的完整流程。.
hyper-cli
Use the Hyper CLI to run Hyper marketing skills from a terminal. Use when the user wants to use hyperai, inspect live tool schemas, translate MCP tool names from a skill into CLI commands, switch connected accounts, or run marketing tools outside an MCP-native agent host.
lov-image-translation-errata
核对截图中的原文与机翻，生成保留错误痕迹、给出正确译文且尽量维持原布局的勘误图；用于“图片翻译勘误”“指出机翻错误”和 create an in-image translation errata。.