contentrain

Reference guidance for Contentrain, a Git-based content management system that stores content as JSON and Markdown files. It explains the system's models, command-line tools, generated client, and agent workflows.

In plain words
What is it for?
Use it when initializing, inspecting, serving, generating, validating, reviewing, or querying a Contentrain project.
Why use it?
It gives an agent the project-specific rules needed to work with Contentrain files and tools correctly.

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
Any agent
npx skills add Contentrain/ai --skill contentrain
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 2,045 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 $0.02045
Opus 5 $0.00018 $0.01022
Sonnet 5 $0.00007 $0.00409
Haiku 4.5 $0.00004 $0.00204

Measured 2d ago against content hash 00fe5efad92d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

contentrain 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/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Contentrain Skill

Contentrain is a Git-based, AI-first content management system. Content lives in .contentrain/ as JSON and Markdown files, managed through MCP tools. This skill covers architecture, content formats, and tool usage.

Ecosystem Overview

Contentrain consists of 6 packages that work together:

Package Role How agent uses it
@contentrain/mcp 22 MCP tools (scan, apply, validate, merge, reconcile, doctor...) MCP tool calls
contentrain (CLI) init, serve, generate, doctor, diff, status Shell commands
@contentrain/types Shared TypeScript contracts Type safety
@contentrain/query Generated SDK client (Prisma-pattern) import from '#contentrain'
@contentrain/rules Behavioral guardrails (~86 lines, always-loaded) Auto-loaded by IDE
@contentrain/skills Procedural workflows (this file) On-demand by agent

CLI Serve — Review & Approval Bridge

For workflows requiring human review (normalize, content review):

contentrain serve  # http://localhost:3333
Page When to use
/normalize Extraction/reuse preview and approval
/branches Merge or delete pending branches
/validate Review validation errors visually
/content Browse models and entries

Architecture: Agent vs MCP

The system separates intelligence (agent) from infrastructure (MCP tools).

Agent responsibilities:

  • Analyze the project (tech stack, architecture, existing patterns)
  • Decide what constitutes content vs code
  • Assign domain grouping and model structure
  • Create replacement expressions (stack-aware: {t('key')} vs {{ $t('key') }})
  • Make all semantic and content decisions

MCP responsibilities:

  • Build project graph (import/component relationships)
  • Find string candidates (regex + filter)
  • Read/write/delete content and models
  • Patch source files (exact string replacement)
  • Validate against schema rules
  • Manage Git transactions (worktree, branch, commit, merge/push)

Read the full file on GitHub · 199 lines

Files

What ships with it

9 files 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. 2d ago First seen · 199 lines · 36 tokens per session scan A 00fe5efad92d

Subscribe to this mod's changes

contentrain 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 2,045 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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