Borrowing it
Nothing to install: this file belongs to Fioooooooo/FylloCode. 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/Fioooooooo/FylloCode/main/.agents/skills/product-writing/SKILL.mdgit clone --depth 1 https://github.com/Fioooooooo/FylloCodeWrote 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/fioooooooo/fyllocode/product-writing)<a href="https://agentmods.dev/skills/fioooooooo/fyllocode/product-writing"><img src="https://agentmods.dev/badge/skills/fioooooooo/fyllocode/product-writing.svg" alt="Measured on agentmods" 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 | $0.00066 | $0.00926 |
| Opus 5 | $0.00033 | $0.00463 |
| Sonnet 5 | $0.00013 | $0.00185 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
product-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 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Writing
Turn product facts into natural, actionable writing for a specific reader. The goal is not to make AI assistance undetectable. It is to prevent unsupported certainty, formulaic structure, empty language, and writing without a recognizable authorial voice.
Before Writing
- Read the repository
AGENTS.md, inspectgit status --short, and read the project guidance that applies to the deliverable. For product documentation, read the relevant documentation architecture and style guidance. Ground feature descriptions in merged code, approved specifications, designs, and tests. - Establish the deliverable, target reader, reader task, publication channel, language, length, and desired action. Do not make one generic piece of writing serve incompatible audiences.
- Gather source material: behavior, prerequisites, limitations, compatibility, risks, data, examples, customer language, established terminology, and release dates. Keep confirmed facts, plans, and assumptions distinct.
- When useful examples of comparable work or brand voice exist, read a small representative sample. Derive voice decisions from it; do not imitate living authors or copy incidental phrasing into a new context.
When essential information is missing, list concise questions or use explicit placeholders. Never invent capabilities, data, customer feedback, quotations, compatibility, release dates, personal experiences, or conclusions.
Select a Mode
- Draft from scratch: establish the information structure and factual gaps first. Draft short text directly; propose an outline for long or high-risk content.
- Edit existing copy: diagnose the most consequential one to three AI-writing signals before choosing a light edit or restructuring. Do not mechanically replace words.
- Review: preserve intended meaning and give actionable feedback on facts, reader task, specificity, structure, voice, and actionability.
- Localize: preserve intent, terminology, and product facts while writing naturally in the target language. Do not translate sentence by sentence or distort risk disclosure between languages.
What ships with it
5 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.
- 8d ago First seen · 57 lines · 66 tokens per session scan A f06c1417676e
product-writing is a skill published in the GitHub repository Fioooooooo/FylloCode (29 stars, last pushed 3d ago), licensed MIT. It adds 66 tokens to every session and 926 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 2026-08-30.
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