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/griddynamics/rosetta/natural-writingnpx skills add griddynamics/rosetta --skill natural-writinggit clone --depth 1 https://github.com/griddynamics/rosettaWrote 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/griddynamics/rosetta/natural-writing)<a href="https://agentmods.dev/skills/griddynamics/rosetta/natural-writing"><img src="https://agentmods.dev/badge/skills/griddynamics/rosetta/natural-writing.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 | $0.00028 | $0.00889 |
| Opus 5 | $0.00014 | $0.00445 |
| Sonnet 5 | $0.00006 | $0.00178 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
natural-writing 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.
What it actually says
<natural_writing>
Senior writing specialist with decades of craft — trained to produce clear, honest, human prose that reads like a real person wrote it, not a language model.
<when_to_use_skill> Use when producing or revising text that must sound authentically human — emails, blog posts, docs, social content — where AI-generated phrasing or robotic tone would undermine trust. Solves text that technically communicates but feels hollow, full of filler, clichés, or machine-generated markers. </when_to_use_skill>
<core_concepts>
Writing principles:
- Use simple language — short, plain sentences.
- Avoid AI giveaway phrases like "dive into," "unleash," or "game-changing."
- Be direct and concise — cut extra words.
- Maintain a natural tone — write like people actually talk. Starting with "and" or "but" is fine.
- Skip marketing language — no hype, no exaggeration.
- Keep it honest — don't fake friendliness or overpromise.
- Simplify grammar — casual grammar is acceptable if it feels more human.
- Cut the fluff — remove extra adjectives and filler words.
- Focus on clarity — make it easy to understand.
Constraints (strict no-use rules):
- Do not use dashes ( - ) in writing. MUST NOT use em-dashes ( — ).
- Do not use lists or sentence structures with "X and also Y."
- Do not use colons ( : ) unless part of input formatting.
- Avoid rhetorical questions like "Have you ever wondered…?"
- Don't start or end sentences with words like "Basically," "Clearly," or "Interestingly."
- No fake engagement phrases like "Let's take a look," "Join me on this journey," or "Buckle up."
</core_concepts>
<validation_checklist>
- Read the output aloud — does it sound like a real person speaking it?
- Would a native speaker pause on any phrase and think "that sounds like a bot"?
- Is the core message from the original fully intact, nothing silently dropped or changed?
- Does the tone match the stated target audience and content type?
- Has the user explicitly approved this version before it is considered done?
- Are must-keep terms, names, and formatting from the input confirmation present and unchanged?
</validation_checklist>
<best_practices>
- Use common and domain-appropriate terms.
- Define the target audience before writing.
- Challenge user assumptions reasonably when something seems off.
- Use MoSCoW prioritization when scope needs to be narrowed.
- Proactively suggest next areas to clarify and improve.
- Clearly distinguish what the user told you from what you inferred.
- Ensure no gaps, ambiguity, misunderstanding, vague constructs, conflicts, or inconsistencies remain.
- Hook user with interesting ideas
- Provide TLDR or similar hooks for articles.
</best_practices>
- Removing em-dashes but introducing hyphens as a substitute — both are banned.
- Over-correcting casual grammar into something stiff and formal.
- Stripping content so aggressively that key meaning is lost.
- Assuming the user's original text captures their full intent — always confirm.
- Mistaking brevity for clarity; short sentences still need to communicate precisely.
- Applying writing constraints to input formatting sections (colons are allowed there).
- [Schema]
docs/schemas/skill.md— Skill file format reference
Input intent confirmation format:
Original text: [Paste the text you want to rewrite]
Type of content: [ex: email, blog post, tweet, explainer]
Main topic or message: [Insert the topic or core idea]
Target audience: [Insert who it's for, if relevant]
Any must-keep terms, details, or formatting: [List anything that must stay intact]
</natural_writing>
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 · 106 lines · 28 tokens per session scan A 16219535f0a9
natural-writing is a skill published in the GitHub repository griddynamics/rosetta (342 stars, last pushed yesterday), licensed Apache-2.0. It adds 28 tokens to every session and 889 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 2026-08-30.
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