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 notque/vexjoy-agent --skill translategit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/translate)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/translate"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/translate/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/notque/vexjoy-agent/translate"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/translate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.01494 |
| Opus 5 | $0.00010 | $0.00747 |
| Sonnet 5 | $0.00004 | $0.00299 |
| Haiku 4.5 | $0.00002 | $0.00149 |
Grade A, and why
translate 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 5d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Translate Skill
Translate documents across languages using one of three modes: quick (single-pass), normal (analyze-then-translate), or refined (full four-step with polish). Core principle: rewrite as a skilled native writer, not word-for-word conversion.
Reference Loading Table
| Signal | Load These Files | Why |
|---|---|---|
| Any translation task | references/modes.md |
Mode detection, chunking algorithm, parallel dispatch pattern |
| "technical", "specialized", "glossary", "terms", or domain vocabulary in request | references/glossary-template.md |
Glossary build, chunk injection, term-preservation rules |
Phase 1: DETECT MODE AND PREPARE
Goal: Identify mode, language pair, and document scale before any translation work.
Step 1: Infer mode from request language
| Request contains | Mode |
|---|---|
| "quick", "fast", "draft", "rough" | quick |
| "professional", "publication-quality", "polished", "refined" | refined |
| anything else | normal (default) |
Step 2: Detect language pair
- Source language: identify from content if not stated; flag ambiguity to user.
- Target language: take from request; ask if absent.
Step 3: Load references
- Load
references/modes.mdfor all modes. - Load
references/glossary-template.mdwhen the request contains "technical", "specialized", "glossary", "terms", or a domain-specific vocabulary word.
Step 4: Assess document size
- Count approximate words.
- Flag documents over 2000 words for chunked parallel translation (details in
references/modes.md).
Gate: Mode, language pair, and size class confirmed. Proceed only when gate passes.
Phase 2: ANALYZE
Goal: Extract structural and stylistic facts that guide accurate translation. Skip this phase in quick mode.
Step 1: Language and dialect
State the identified source language and dialect (e.g., Brazilian Portuguese vs European Portuguese, Simplified vs Traditional Chinese).
Step 2: Register and tone
What ships with it
2 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.
- 5d ago First seen · 187 lines · 21 tokens per session scan A 106bd2b29f36
translate is a skill published in the GitHub repository notque/vexjoy-agent (419 stars, last pushed 4d ago), licensed MIT. It adds 21 tokens to every session and 1,494 once invoked, about $0.0001 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 2026-09-03.
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