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pinkietech

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@w-pinkietech

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10Public Repos
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3Published Skills

pinkietech provides skills for sanitizing generated deliverables, reviewing IoTKit pull requests against field evidence, and enforcing Japanese technical writing standards.

Skills Distribution
DomainGrowth, Mark...Technical Writing .. (40%)Content Sanitizati.. (30%)IoT Code Review & .. (30%)

Agent Skills by pinkietech

Showing 3 vetted skills indexed across 1 GitHub repositories.

Frequently Asked Questions About pinkietech

FAQPage Schema
What tasks can I accomplish with pinkietech's skills?โ–ผ

You can sanitize generated artifacts to remove leaked prompts and process artifacts, review IoTKit pull requests for operational failure risks, triage IoTKit field reports into review entries or runbooks, and write or rewrite Japanese technical documents following strict formatting and argumentation norms.

Who should use pinkietech's skills?โ–ผ

Technical writers producing Japanese books, chapters, and articles; editors polishing machine-generated deliverables for end users; and IoT engineers or maintainers reviewing IoTKit pull requests and converting field evidence into regression tests and operational runbooks.

How does the iotkit-battle-tested-review skill work in practice?โ–ผ

Invoke it when reviewing an IoTKit pull request or triaging a field report. It guides assessment of operational failure risks and helps decide whether field evidence should update an existing review entry, trigger a new regression test, or modify a runbook.

What writing rules does the japanese-tech-writing skill enforce?โ–ผ

It enforces one-sentence-per-line formatting, quote blocks, footnotes, and column notation; paragraph-based argumentation; removal of logical gaps; management of reader cognitive load; controlled viewpoint and narration; restrained rhetoric; and elimination of LLM-style filler and redundancy.

Are there prerequisites for using the sanitize-artifacts skill?โ–ผ

No special dependencies are required. You need a generated artifact intended for end users; the skill inspects and revises it so the output reads as a natural, standalone deliverable free of prompt instructions, conversation history, and implementation constraints.