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Harryoung
GitHub creator profile

Harryoung

Repository-level view of 6 collected skills across 1 GitHub repositories, including approximate occupation coverage.

skills collected
6
repositories
1
occupation fields
2
updated
2026-01-17
occupation focus
Major fields detected across this creator.
repository explorer

Repositories and representative skills

#001
efka
6 skills10119updated 2026-01-17
100% of creator
batch-notification
カスタマーサービス担当者

Send IM messages to users in batch. Used for notifying specific user groups, sending after table filtering, all-staff notifications, etc. Use this Skill when administrators request batch notifications, mass messaging, or notifications after table filtering. Trigger words: notify/send/mass + users/batch/table.

2026-01-17
document-conversion
ウェブ開発者

Convert DOC/DOCX/PDF/PPT/PPTX documents to Markdown format. Automatically detect PDF type (electronic/scanned), extract images to separate directory. Use this Skill when administrator onboards non-Markdown documents. Trigger condition: Onboard DOC/DOCX/PDF/PPT/PPTX format files.

2026-01-17
expert-routing
事務・管理支援従事者の現場監督

Domain expert routing. When the knowledge base cannot answer user questions, find and notify the corresponding expert based on the question domain. Only available in IM mode. Trigger condition: No results in 6-stage retrieval.

2026-01-17
large-file-toc
統計補助員

Generate table of contents overview for large files. When onboarded Markdown file exceeds threshold (default 30KB), extract heading structure to create navigation file. Trigger condition: Markdown file size >= 30KB.

2026-01-17
satisfaction-feedback
カスタマーサービス担当者

Handle user satisfaction feedback. When users respond with "satisfied"/"unsatisfied", update FAQ usage count or record BADCASE. Trigger words: satisfied/unsatisfied/resolved/not resolved/thanks/满意/不满意/解决了/没解决/谢谢.

2026-01-17
excel-parser
データサイエンティスト

Smart Excel/CSV file parsing with intelligent routing based on file complexity analysis. Analyzes file structure (merged cells, row count, table layout) using lightweight metadata scanning, then recommends optimal processing strategy - either high-speed Pandas mode for standard tables or semantic HTML mode for complex reports. Use when processing Excel/CSV files with unknown or varying structure where optimization between speed and accuracy is needed.

2026-01-06
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