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 agentmods add skills/contentrain/ai/contentrain-normalizenpx skills add Contentrain/ai --skill contentrain-normalizegit clone --depth 1 https://github.com/Contentrain/aiWhat 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 | $0.00053 | $0.03062 |
| Opus 5 | $0.00026 | $0.01531 |
| Sonnet 5 | $0.00011 | $0.00612 |
| Haiku 4.5 | $0.00005 | $0.00306 |
Grade A, and why
contentrain-normalize 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.
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contentrain Normalize
Normalize converts a codebase with hardcoded strings into a Contentrain-managed content architecture. It runs in two independent phases, each producing a separate branch for review.
- Phase 1 (Extraction): Pull content from source code into
.contentrain/structure. Source files are NOT modified. - Phase 2 (Reuse): Patch source files to replace hardcoded strings with content references. Requires completed extraction.
Phase 1 alone is valuable: content becomes manageable in Studio, translatable, and publishable without touching source code.
MUST Rules
- MUST scan before extract (
contentrain_scan→contentrain_apply) - MUST
dry_run: truebeforedry_run: falsefor everycontentrain_applycall - MUST merge Phase 1 branch before starting Phase 2
- MUST run
npx contentrain generateafter Phase 2 completes - MUST NOT reuse without scoped model or domain (whole-project patching is blocked)
- MUST NOT patch
.contentrain/files via reuse (content files are read-only for reuse) - MUST NOT exceed 100 patches per
contentrain_applycall
Transport Requirements
Normalize (contentrain_scan and contentrain_apply) requires local
disk access — AST scanners walk the source tree and patch files in
place. It runs only on a LocalProvider (stdio transport, or an HTTP
transport configured with a LocalProvider).
Remote providers (GitHubProvider, GitLabProvider, future
BitbucketProvider) expose astScan: false, sourceRead: false, and
sourceWrite: false. Calling these tools over a remote provider
returns a uniform capability error:
{
"error": "contentrain_scan requires local filesystem access.",
"capability_required": "astScan",
"hint": "This tool is unavailable when MCP is driven by a remote provider. Use a LocalProvider or the stdio transport."
}
If the agent is driving a remote-only MCP session, normalize must run in a separate local-checkout session before the extracted content branch is pushed.
What ships with it
3 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.
- 2d ago First seen · 294 lines · 53 tokens per session scan A 224aebd0e50f
contentrain-normalize is a skill published in the GitHub repository Contentrain/ai (4 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 3,062 once invoked, about $0.0003 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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