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 contentraingit 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)<a href="https://agentmods.dev/skills/contentrain/ai/contentrain"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain/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"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain.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 | $0.00036 | $0.02137 |
| Opus 5.5 | $0.00014 | $0.00855 |
| Sonnet 5.5 | $0.00007 | $0.00427 |
| Haiku 4.5 | $0.00004 | $0.00214 |
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 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 — 202 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 9 packages that work together:
| Package | Role | How agent uses it |
|---|---|---|
| @contentrain/mcp | 27 MCP tools (scan, apply, validate, merge, reconcile, doctor...) | MCP tool calls |
| contentrain (CLI) | init, serve, generate, doctor, diff, status, import | Shell commands |
| @contentrain/types | Shared TypeScript contracts | Type safety |
| @contentrain/query | Generated SDK client (Prisma-pattern) | import from '#contentrain' |
| @contentrain/rules | Behavioral guardrails (~145 lines, always-loaded) | Auto-loaded by IDE |
| @contentrain/skills | Procedural workflows (this file) | On-demand by agent |
| @contentrain/wp-import | WordPress WXR/REST → RawIR → a .contentrain store |
contentrain import wordpress |
| @contentrain/emitter-astro | ProjectIR → a complete Astro project |
Called by the migration engine |
| @contentrain/verify | Offline SEO, indexing, link and asset gates for a built site | npx @contentrain/verify dist |
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
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.
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 · +3 lines 4561fe771c54
- 1mo ago First seen · 199 lines · 36 tokens per session scan A 00fe5efad92d
contentrain is a skill published in the GitHub repository Contentrain/ai (6 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 2,137 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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