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 skills add Contentrain/ai --skill contentrain-modelgit clone --depth 1 https://github.com/Contentrain/aiWrote 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.
[](https://agentmods.dev/skills/contentrain/ai/contentrain-model)<a href="https://agentmods.dev/skills/contentrain/ai/contentrain-model"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/contentrain/ai/contentrain-model"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01310 |
| Opus 5.5 | $0.00012 | $0.00524 |
| Sonnet 5.5 | $0.00006 | $0.00262 |
| Haiku 4.5 | $0.00003 | $0.00131 |
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 1mo 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 — for new fields. A field an existing model already has under a legacy name (
creativeWork) is kept bycontentrain_model_saveand reported inschema_warnings; never rename it just to satisfy the rule, since the rename must reach its content keys in every locale and every consumer too - 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
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.
- 1mo ago Changed dbfa087df1cd
- 1mo ago First seen · 174 lines · 29 tokens per session scan A bdad0b8a1d77
contentrain-model is a skill published in the GitHub repository Contentrain/ai (6 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,310 once invoked, about $0.0001 per session on Opus 5.5. 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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