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 Osipchuk/agent-skills --skill article-translatorgit clone --depth 1 https://github.com/Osipchuk/agent-skillsWrote 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/osipchuk/agent-skills/article-translator)<a href="https://agentmods.dev/skills/osipchuk/agent-skills/article-translator"><img src="https://agentmods.dev/badge/skills/osipchuk/agent-skills/article-translator.svg" alt="Measured on agentmods" 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 | $0.00175 | $0.03915 |
| Opus 5 | $0.00088 | $0.01958 |
| Sonnet 5 | $0.00035 | $0.00783 |
| Haiku 4.5 | $0.00017 | $0.00392 |
Grade A, and why
article-translator 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 8d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Article Translator
A workflow for translating long-form prose with fidelity, consistency, and a preserved authorial voice. Translation here is treated as a series of deliberate choices over a whole text — not a sentence-by-sentence transcoding.
When to use
Input is more than ~3 paragraphs of prose bound for another language. Out of scope: UI copy, code, single sentences, tweet-sized fragments, song lyrics, metered poetry (verse translation needs a different toolkit), legal contracts (specialized terminology and disclaimers).
Core workflow at a glance
- Analyze the source: language, locale, register, voice, purpose, and a brief structural outline of the argument.
- Build a glossary of names, terms, numbers, dates, and recurring phrases — fixed renderings, applied throughout.
- Decide strategy on idioms, units, punctuation, and format preservation.
- Translate in paragraph-sized semantic units (Phase 4).
- Footnote sparingly — only for preserved-but-opaque expressions or load-bearing ambiguities.
- Self-review against the criteria below: drift, omissions, additions, naturalness, register, style.
Priority order on conflicts: accuracy → terminology consistency → naturalness → style preservation → pragmatic equivalence → cultural adaptation → skopos-fit.
Calibrate depth to the text. A 300-word blog snippet does not need a full glossary-and-review pass; a 3000-word longread for publication does. Skip nothing in spirit, but compress phases proportionally for short or low-stakes inputs.
Default output target: a separate file next to the original (see "File output" below). The chat reply summarizes; it does not paste the translation back.
Guiding criteria
The output should satisfy all of these criteria together. They are listed in priority order — when two conflict, the higher-listed criterion wins, and the choice should be made consciously.
- Accuracy. Every proposition in the source must be present in the target. Nothing added, nothing dropped. Implications, hedges, modality, and irony all count as meaning that must survive.
- Terminological consistency. The same concept gets the same word throughout, governed by the glossary built in Phase 2. Drift is the most common defect in long-form translation, and it is the easiest to verify.
- Naturalness. The result must read as if originally written in the target language. If a native reader would never phrase it that way, rewrite. "Translator-ese" is a failure mode, not a neutral default — and the fix for it is bolder rewriting at the sentence and clause level, not closer adherence to the source.
- Style and register preservation. Academic stays academic, casual stays casual. Sentence rhythm, signature devices (anaphora, parentheticals, fragments, em-dashes), and the author's idiolect must survive. Resist the urge to "clean up" deliberate quirks.
- Pragmatic equivalence. Produce the same effect on the target reader as the original produced on the source reader. A joke must remain funny; a polemic must remain pointed.
- Cultural appropriateness. Handle realia, allusions, units, and idioms with a deliberate, consistent strategy (see Phase 3).
- Fit-to-purpose (skopos). A translation for publication, for personal understanding, and for an internal memo are graded on different curves.
What ships with it
1 file 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.
- 8d ago First seen · 206 lines · 175 tokens per session scan A 0c8e64579425
article-translator is a skill published in the GitHub repository Osipchuk/agent-skills (5 stars, last pushed 27d ago), licensed MIT. It adds 175 tokens to every session and 3,915 once invoked, about $0.0009 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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