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-servegit 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-serve)<a href="https://agentmods.dev/skills/contentrain/ai/contentrain-serve"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain-serve/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-serve"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain-serve.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 | $0.00032 | $0.01009 |
| Opus 5.5 | $0.00013 | $0.00404 |
| Sonnet 5.5 | $0.00006 | $0.00202 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
contentrain-serve 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Review the Project with contentrain serve
Start the local review UI as a bridge between agent and developer.
When to Use
Use this when:
- a normalize plan, branch, or validation result needs developer review
- the developer wants to browse models, content, or history visually
- another skill needs to hand off to the UI for approval (normalize, review)
Steps
1. Check if Serve is Already Running
Before starting a new instance, check if port 3333 is already in use:
lsof -ti:3333
If a process is running, serve is already up — skip to Step 3.
2. Start Serve
Run from the project root (where .contentrain/ lives):
contentrain serve
Optional flags:
--port(default: 3333)--host(default: localhost)--open=false(prevent auto-opening browser)--stdio(MCP stdio transport for IDE integration — no web UI)
Wait for the "Contentrain Serve" banner confirming the server is ready.
3. Direct the Developer to the Right Page
Based on the current context, tell the developer exactly where to go:
| Context | URL | What to do |
|---|---|---|
| Normalize plan ready | http://localhost:3333/normalize |
Review extractions, approve or reject |
| Pending branches | http://localhost:3333/branches |
Review and merge branches |
| Validation issues | http://localhost:3333/validate |
Inspect errors and warnings |
| Content browsing | http://localhost:3333/content |
Browse entries and models |
| General overview | http://localhost:3333 |
Dashboard with project stats |
4. Wait for Developer Action
After directing the developer to the UI:
- Normalize flow: Check
.contentrain/normalize-plan.json— if deleted, check for new branches to determine approve vs reject - Branch flow: Check branch list — if branch was merged or deleted, proceed accordingly
- Validation flow: Re-read validation results after developer reviews
The UI communicates back through filesystem changes (plan files, branches, context.json). Poll these to detect the developer's decision.
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 First seen · 122 lines · 32 tokens per session scan A ed64dc83baee
contentrain-serve is a skill published in the GitHub repository Contentrain/ai (6 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 1,009 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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