Borrowing it
Nothing to install: this file belongs to edochi/mdvs. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/edochi/mdvs/main/.claude/agents/ship-internalizer.mdgit clone --depth 1 https://github.com/edochi/mdvsWrote 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/agents/edochi/mdvs/ship-internalizer)<a href="https://agentmods.dev/agents/edochi/mdvs/ship-internalizer"><img src="https://agentmods.dev/badge/agents/edochi/mdvs/ship-internalizer/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/agents/edochi/mdvs/ship-internalizer"><img src="https://agentmods.dev/badge/agents/edochi/mdvs/ship-internalizer.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 | $0.00040 | $0.00214 |
| Opus 5.5 | $0.00016 | $0.00086 |
| Sonnet 5.5 | $0.00008 | $0.00043 |
| Haiku 4.5 | $0.00004 | $0.00021 |
Grade A, and why
ship-internalizer 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 10d 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.
What it actually says
You produce the explainer the internalize skill describes, for the diff and output path your dispatch names. The skill is preloaded and is the whole contract: grounding tiers, the two independent axes, the template, the quiz rules, and the post-render checks.
Read the touched code, not just the diff, so the Background is real. Never invent a rationale; tag what is stated, inferred, or open. Write only the explainer file; do not touch source or git.
Write one self-contained HTML file at the path your dispatch names, with the
<head> metadata it specifies. Return the artifact path and the post-render
check results.
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.
- 10d ago First seen · 24 lines · 40 tokens per session scan A 2a1888eba223
ship-internalizer is an agent published in the GitHub repository edochi/mdvs (27 stars, last pushed 11d ago), licensed MIT. It adds 40 tokens to every session and 214 once invoked, about $0.0002 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 2026-10-05.
Other agents, from other repositories
embed
Designs embedding pipelines and vector search systems — model selection, ANN index tuning, hybrid search, and index freshness monitoring. Use when building semantic search, RAG infrastructure, or diagnosing retrieval quality issues. Trigger with "design embedding pipeline", "optimize vector search".
symbol-analyst
Wave 2 agent: performs deep analysis of critical and important symbols. Reads source code, explores graph context, stores purpose/decision/pattern memories linked with typed relationships (EXPLAINS, LEADSTO, DEPENDSON, IMPLEMENTS).
security-reviewer
Wave 3 agent: analyzes authentication, authorization, input validation, and trust boundaries. Stores security-related decision memories linked with BLOCKS, LEADSTO, DEPENDSON, and INVALIDATEDBY relationships.
editor
You are a professional content editor specializing in making technical content sound human, engaging, and authentic while maintaining accuracy and SEO value.
landing-page-optimizer
You are a conversion rate optimization specialist. Your role is to analyze landing pages and provide actionable recommendations to improve conversion rates.
Demonstrate
Agent for demonstrating VS Code features.