Track and update action item status from natural language updates like 'Seanのメールには返信しておいた' or 'Lu対応予定'. Integrates with daily-comms-ops workflow. Use when managing action items across communication channels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze inbox emails to identify action-required items, prioritize by urgency/importance, classify by topic, and draft contextual replies. Use when triaging unread emails, prioritizing inbox, generating response drafts, or tracking email response status.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Analyze inbox emails to identify action-required items, prioritize them by urgency and importance using a 4-quadrant matrix, classify by topic (vendor inquiry, internal request, client follow-up), and generate contextual draft responses in appropriate tone and language. Integrates with Gmail/Outlook via gogcli or MCP tools to surface unread emails requiring attention.
gogcli configured with Gmail OAuth (for Gmail integration)Use gogcli or MCP tools to retrieve unread emails from the inbox.
# Gmail via gogcli
gogcli gmail messages list --query "is:unread" --format json --max-results 50
# Or use MCP server for Outlook
# (MCP tool invocation handled by Claude)
Run the triage script to classify and prioritize emails.
python3 scripts/triage_emails.py \
--input emails.json \
--output triage_report.json
The script performs:
Categorize emails into 4 quadrants:
| Quadrant | Urgency | Importance | Action |
|---|---|---|---|
| Q1 | High | High | Respond immediately |
| Q2 | Low | High | Schedule focused time |
| Q3 | High | Low | Delegate or quick reply |
| Q4 | Low | Low | Batch process or archive |
For each action-required email, generate a draft response:
python3 scripts/triage_emails.py \
--input emails.json \
--output drafts.json \
--generate-drafts \
--tone professional \
--language auto
Draft generation considers:
Maintain a tracking file for email response status:
python3 scripts/triage_emails.py \
--input emails.json \
--status-file email_status.json \
--update-status
Status tracking fields:
email_id: Unique identifierstatus: pending, draft_ready, sent, delegated, archivedassigned_to: Owner if delegateddue_date: Expected response deadlinelast_updated: Timestamp of last status changeCreate a triage summary for review:
python3 scripts/triage_emails.py \
--input emails.json \
--output triage_report.md \
--format markdown
{
"schema_version": "1.0",
"generated_at": "2024-01-15T09:30:00Z",
"summary": {
"total_emails": 25,
"action_required": 12,
"by_quadrant": {
"Q1_urgent_important": 3,
"Q2_important_not_urgent": 5,
"Q3_urgent_not_important": 2,
"Q4_neither": 15
},
"by_topic": {
"client_followup": 4,
"internal_request": 6,
"vendor_inquiry": 3,
"fyi_informational": 8,
"meeting_scheduling": 4
}
},
"emails": [
{
"id": "msg_12345",
"from": "client@example.com",
"subject": "Urgent: Contract Review Needed",
"received_at": "2024-01-15T08:00:00Z",
"quadrant": "Q1",
"urgency_score": 0.9,
"importance_score": 0.85,
"topic": "client_followup",
"action_required": "respond",
"detected_deadline": "2024-01-16T17:00:00Z",
"draft_response": "Thank you for sending the contract...",
"status": "draft_ready"
}
]
}
# Email Triage Report
**Generated**: 2024-01-15 09:30 AM
**Total Emails**: 25 | **Action Required**: 12
## Priority Matrix
### Q1: Urgent & Important (3 emails)
| From | Subject | Deadline | Status |
|------|---------|----------|--------|
| client@example.com | Urgent: Contract Review | Jan 16 | Draft Ready |
### Q2: Important, Not Urgent (5 emails)
...
## Topic Breakdown
- Client Follow-ups: 4
- Internal Requests: 6
- Vendor Inquiries: 3
## Recommended Actions
1. **Respond immediately** to 3 Q1 emails
2. **Schedule 30 min** for 5 Q2 emails
3. **Delegate** 2 Q3 emails to team
scripts/triage_emails.py -- Main triage and draft generation scriptreferences/email-classification.md -- Topic taxonomy and urgency markersreferences/response-templates.md -- Draft response templates by category아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Track and update action item status from natural language updates like 'Seanのメールには返信しておいた' or 'Lu対応予定'. Integrates with daily-comms-ops workflow. Use when managing action items across communication channels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI活用提案の専門コンサルタントスキル。LLM・AIエージェントの活用事例知識を基に、業界別、部門別、シーン別に最適なAI導入提案を行います。システム化、業務改善、効率化を得意とし、具体的な活用事例、期待効果、導入ステップを含む実践的な提案を提供します。Use when proposing AI/LLM adoption strategies for specific industries, departments, or business scenarios.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate AI-powered BPO service proposals for Japanese companies in the US market. Use when creating outsourcing proposals with AI service menus, ROI estimation, implementation roadmaps, and bilingual (JA/EN) proposal documents for 在米日系企業向けAI実装.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI(LLM)が生成した日本語テキストの「AI臭」を検出・診断し、人間らしい文章にリライトするスキル。 Use when: 「AIっぽい文章を直して」「人間らしくリライトして」「AI臭を消して」「この文章をもっと自然にして」 「テキストのAI感を減らして」「機械っぽさを取りたい」 "make this sound more human", "remove AI tone", "humanize this text", "detect if this is AI-generated". 6つのAI特有パターン(視覚的マーカー、単調なリズム、マニュアル的構成、非コミット姿勢、抽象語の濫用、 定型メタファー)を正規表現ベースで検出し、0-100のAI臭スコアを算出。3つの人間化技法 (バランスを崩す・客観を崩す・論理を崩す)でリライトを実行する。 Note: 検出スクリプトは日本語テキスト専用。英語テキストの場合はClaude自身がreferences/を参照して分析・リライトする。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate audit-ready internal control design documents from As-Is business process inventories. Produces control IDs, assertion mappings, procedures, SoD analysis, KPIs, materiality thresholds, and implementation roadmaps. Use when building internal controls for new audit engagements, SOX/J-SOX compliance, or process improvement initiatives.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review audit-related documents (control design documents, bottleneck analyses, requirements definitions, etc.) for quality, scoring them 0-100 with a severity-rated findings list. Use when reviewing audit documents, checking control design quality, or verifying cross-document consistency. Supports documents governed by US GAAP, IFRS, or J-GAAP.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.