contentrain-content

A workflow for adding content to models that already exist in Contentrain. A model is a defined content structure, such as a blog post, hero section, or list of testimonials.

In plain words
What is it for?
Use it to add collection entries, fill single content items, write documents, manage dictionary text, and generate updated code after model changes.
Why use it?
It ensures new entries match the required fields and includes every configured language when translations are enabled.

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/contentrain/ai/contentrain-content
Any agent
npx skills add Contentrain/ai --skill contentrain-content
Clone the repo
git clone --depth 1 https://github.com/Contentrain/ai

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,991 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 $0.00036 $0.01991
Opus 5 $0.00018 $0.00996
Sonnet 5 $0.00007 $0.00398
Haiku 4.5 $0.00004 $0.00199

Measured 2d ago against content hash 40d38510ea0a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

contentrain-content 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.

packages/skills/skills/contentrain-content/SKILL.md · 203 lines

How it starts

The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Create Content for Existing Models

Generate and save content entries for models already defined in the project.


When to Use

The user wants to create new content, add entries to a collection, populate a singleton, or says something like "add a blog post", "create content", "fill in the hero section".


MUST Rules

  • MUST call contentrain_describe before writing content (know the schema)
  • MUST provide ALL required fields in entries
  • MUST create entries for ALL supported locales when model has i18n: true
  • MUST NOT set system fields (id, slug, createdAt, updatedAt, status, source)
  • MUST run npx contentrain generate after adding new models or changing fields

Steps

1. Check Project State

Call contentrain_status to get the full project context:

  • Confirm the project is initialized.
  • List available models, their kinds, domains, and field counts.
  • Note supported locales and the source locale.
  • Check for any pending changes or validation errors.

If .contentrain/ does not exist, stop and suggest running /contentrain-init first.

2. Identify the Target Model

If the user specified a model, use it. Otherwise:

  • Present the list of available models from status.
  • Ask the user which model to create content for.
  • If the user describes content without naming a model (e.g., "write a blog post about X"), infer the model from context.

3. Read the Model Definition

Call contentrain_describe(model: "<model-id>", include_sample: true) to get:

  • All field definitions (name, type, required, constraints).
  • A sample entry if one exists (use it as a structural reference).
  • Relation fields and their target models.

If the user's request does not match an existing model, offer to create one first with contentrain_model_save.

4. Read Vocabulary and Context

  • Check .contentrain/vocabulary.json for canonical terms. All content must use these terms consistently across locales. Do not use alternative spellings or synonyms for vocabulary-defined terms.
  • Check .contentrain/context.json for tone conventions (professional, casual, technical).
  • For dictionaries: Before creating any new key, call contentrain_content_list on the target model and scan existing values. If the value you intend to write already exists under a different key, REUSE the existing key instead of creating a new one. MCP will also warn you via advisories in the save response if duplicates are detected. Replacing an existing key's value is allowed — the response reports what was replaced, so read the advisories rather than assuming a save was purely additive.

Read the full file on GitHub · 203 lines

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. 2d ago First seen · 203 lines · 36 tokens per session scan A 40d38510ea0a

Subscribe to this mod's changes

contentrain-content is a skill published in the GitHub repository Contentrain/ai (4 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,991 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.

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