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-modelnpx skills add Contentrain/ai --skill contentrain-modelgit 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 | $0.00029 | $0.01247 |
| Opus 5 | $0.00015 | $0.00624 |
| Sonnet 5 | $0.00006 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
contentrain-model 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.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Design and Save Models
Create or evolve Contentrain model definitions safely.
When to Use
Use this when the user wants to:
- add a new model
- change fields on an existing model
- choose between
singleton,collection,document, anddictionary - define relations, locales, or custom content paths
Steps
1. Inspect Project State
Call contentrain_status first:
- confirm the project is initialized
- see existing model IDs and domains
- avoid creating duplicates
If the user is extending an existing model, call contentrain_describe(model: "<model-id>").
2. Confirm Storage Contract
Call contentrain_describe_format before proposing structure changes.
Use it to confirm:
- model kinds and storage expectations
- locale behavior
- document vs JSON model tradeoffs
- how custom
content_pathandlocale_strategyaffect files
3. Choose the Right Kind
singleton: one object per locale, e.g. hero, navigation, footercollection: repeated entries with IDs, e.g. testimonials, faq items, authorsdocument: long-form markdown content with frontmatter, e.g. blog posts, docs pagesdictionary: flat key-value strings, e.g. UI labels, error messages
Default rules:
- use
dictionaryfor UI/system strings - use
documentfor markdown-heavy long-form content - use
collectionfor repeated structured items - use
singletonfor one page/section config per locale
4. Design Fields
Follow these rules:
- model IDs must be kebab-case
- field names must be snake_case
- relation fields must define a target
model - prefer small, explicit schemas over large generic
objectblobs - only mark fields as
requiredwhen the content truly cannot function without them
Relation guidance:
relation: single referencerelations: multiple references- use
slug-driven relations for document-like linking
5. Decide i18n and Path Strategy
Ask or infer:
- should this model be localized?
- should it use the default
.contentrain/content/...path or a customcontent_path? - if custom path is needed, which
locale_strategymatches the project layout?
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 · 174 lines · 29 tokens per session scan A bdad0b8a1d77
contentrain-model is a skill published in the GitHub repository Contentrain/ai (4 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 1,247 once invoked, about $0.0001 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.
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