Email Triage Responder

Analyze inbox emails to identify action-required items, prioritize by urgency / importance, classify by topic, and draft contextual replies. Tracks response status across the lifecycle.

gogcli / Outlook MCP

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Pulls unread emails (Gmail via gogcli or Outlook via MCP), scores urgency/importance, classifies into 8 topic categories, places each into an Eisenhower-Matrix quadrant (Q1-Q4), drafts contextual replies in the source language/tone, and tracks response state (pending → draft_ready → sent → delegated → archived).

2. Prerequisites

  • Python 3.9+
  • gogcli (for Gmail) または Outlook MCP server
  • No API keys beyond email access

3. Quick Start 

# Install the skill locally
make install SKILL=email-triage-responder

# Or fetch the .skill package
curl -L -o email-triage-responder.skill https://github.com/takusaotome/claude-skills-library/raw/main/skill-packages/email-triage-responder.skill

Then trigger the skill in Claude Code by describing what you want — see the Usage Examples section below for trigger phrases.

4. How It Works

The skill follows the workflow documented in its SKILL.md. Key stages:

  1. Input parsing — interprets the user request and any provided source files.
  2. Core processing — applies the skill’s domain logic (see Reference section).
  3. Output generation — produces structured artifacts (markdown / JSON / templates) ready for downstream use.

For the authoritative step-by-step procedure, open skills/email-triage-responder/SKILL.md.

5. Usage Examples

  • You need to triage a backlog of unread emails by what truly needs action
  • You want bilingual (JA/EN) draft responses generated from context
  • You want a single dashboard of pending → sent → archived email states
  • You’re processing emails in bulk with consistent prioritization rules

6. Understanding the Output

The skill produces structured output following the conventions in its templates and reference docs (see Section 10). Outputs are:

  • Reproducible — identical input + same templates → same output structure.
  • Reviewable — each section is labeled and ordered consistently.
  • Composable — outputs of this skill can feed adjacent skills (see Section 8).

7. Tips & Best Practices

  • Start with a small, realistic input to validate the workflow before scaling.
  • Keep skills/email-triage-responder/SKILL.md open alongside this guide; it remains the authoritative source.
  • Read the most relevant reference file first (see Section 10) instead of trying to absorb all of them.
  • Run scripts on test data before applying to production-bound inputs.
  • Preserve intermediate outputs so you can explain assumptions and trace decisions.

8. Combining with Other Skills

  • Pair with adjacent skills in the same category to cover the planning → execution → review arc.
  • Browse the Meta & Quality category for neighboring workflows: category index.
  • See the full English skill catalog: skill catalog.

9. Troubleshooting

  • Re-check prerequisites first; missing runtime dependencies are the most common failure mode.
  • Run helper scripts on a minimal input before applying them to a full dataset.
  • Compare your input shape against the reference files to confirm expected fields, sections, or metadata.
  • Confirm Python version (3.9+) and required packages are installed in the active environment.
  • When output looks incomplete, re-read the relevant reference file to verify the input contract.

10. Reference

References:

  • skills/email-triage-responder/references/email-classification.md
  • skills/email-triage-responder/references/response-templates.md

Scripts:

  • skills/email-triage-responder/scripts/triage_emails.py

Assets:

(none)