name: email-summarizer description: Email summary and contact profiling skill. Fetch emails from an IMAP mailbox or parse local exports (.pst / .mbox / .msg), build a contact profile report (HTML + Excel), and optionally send it via email. Trigger when user says "summarize my emails", "check recent emails", "analyze my contacts", "profile email contacts", "parse pst file", "analyze outlook export", "send contact report", etc.

Runtime dependencies

Python (pip install -r requirements.txt):

extract-msg>=0.52.0 — .msg file parsing

openpyxl>=3.1.0 — Excel report generation

Node.js (cd scripts && npm install):

pst-extractor>=1.10.0 — native .pst parsing (bundled via package.json)

System tools (optional):

readpst — alternative .pst engine (apt install pst-utils / brew install libpst)

node >= 16 — required for native PST parsing

External process usage (declared for security review):

parse_file.py invokes TWO local executables via subprocess — no shell=True,

no dynamic command construction, no network access:

1. node scripts/pst_extractor_helper.js [--since] [--until] [--max]

Calls the Node.js script bundled in this skill's scripts/ directory.

Used only when --pst is passed and engine is 'native' (default).

node binary is resolved from NODE_BIN env var or system PATH.

2. readpst -o -M

Calls the system-installed readpst binary (from pst-utils package).

Used only when --pst-engine readpst is explicitly requested.

Both executables accept only local file paths — no URLs, no user shell input.

Credentials (only needed for IMAP fetch and SMTP send; local file parsing needs none)

env: EMAIL_USER: description: Your email address. Used for IMAP login and SMTP sender. required: false example: "you@163.com" EMAIL_PASS: description: IMAP/SMTP app password (NOT your login password). Generate in mailbox settings under POP3/SMTP/IMAP. required: false example: "your-app-password"

Email Summarizer + Contact Profiler

Three independent stages — run any subset depending on what you need:

[Data Source]          [Analysis]          [Delivery]
fetch_imap.py    ──→                ──→
parse_file.py    ──→  build_report.py ──→  send_report.py
                       → .html + .xlsx

Each stage reads from / writes to files (or stdin/stdout), so they compose freely.

Installation

# Python dependencies
pip install -r requirements.txt

# Node.js dependency (only for native .pst parsing)
cd scripts && npm install && cd ..

Script Reference

Stage 1 — Data Source

fetch_imap.py — Fetch emails from a live IMAP mailbox

export EMAIL_USER=you@163.com
export EMAIL_PASS=your-app-password

# Fetch last 7 days (inbox only) → emails.json
python3 scripts/fetch_imap.py --days 7 --output emails.json

# Fetch a date range, inbox + sent
python3 scripts/fetch_imap.py --since 2026-03-01 --until 2026-03-28 \
    --with-sent --preset 163 --output emails.json

# Pipe directly to build_report.py (no intermediate file)
python3 scripts/fetch_imap.py --days 30 | python3 scripts/build_report.py --output report

Supported presets:

parse_file.py — Parse a local email export

# Parse a .pst file (native engine, no system install needed)
python3 scripts/parse_file.py --pst ~/Downloads/archive.pst --output emails.json

# Parse a .mbox file (inbox + sent)
python3 scripts/parse_file.py --mbox Inbox.mbox --sent-mbox Sent.mbox --output emails.json

# Parse a folder of .msg files, filter by date
python3 scripts/parse_file.py --msg-dir ./exported/ --since 2026-01-01 --output emails.json

# Pipe to build_report.py
python3 scripts/parse_file.py --pst archive.pst | python3 scripts/build_report.py --output report

PST engines:

Stage 2 — Analysis

build_report.py — Analyse emails → HTML + Excel report

# From a file
python3 scripts/build_report.py --input emails.json --output report

# Specify owner explicitly (when analysing someone else's PST)
python3 scripts/build_report.py --input emails.json --output report \
    --owner xiang-xiang.hu@connect.polyu.hk

# From stdin (piped from Stage 1)
python3 scripts/fetch_imap.py --days 30 | python3 scripts/build_report.py --output report

Output files: - <prefix>.html — self-contained HTML report (open in browser or attach to email) - <prefix>.xlsx — Excel spreadsheet with the same data

Report columns: # / Preferred Name / Email / Company / Position / Subject Summary / Source / Emails (Recv/Sent)

Stage 3 — Delivery

send_report.py — Send report files via SMTP

export EMAIL_USER=you@163.com
export EMAIL_PASS=your-app-password

# Send HTML + Excel to a recipient
python3 scripts/send_report.py \
    --html report.html --xlsx report.xlsx --to friend@example.com

# Send to yourself (EMAIL_USER)
python3 scripts/send_report.py --html report.html --xlsx report.xlsx

# HTML only (no attachment)
python3 scripts/send_report.py --html report.html --to friend@example.com

# Custom subject
python3 scripts/send_report.py --html report.html --subject "March Contact Report"

Private library modules (not standalone scripts)

Complete workflow examples

A — IMAP mailbox → report → send to self

export EMAIL_USER=you@163.com
export EMAIL_PASS=your-app-password

python3 scripts/fetch_imap.py --days 30 --with-sent --output /tmp/emails.json
python3 scripts/build_report.py --input /tmp/emails.json --output /tmp/report
python3 scripts/send_report.py --html /tmp/report.html --xlsx /tmp/report.xlsx --preset 163

B — PST file → report → send to someone

python3 scripts/parse_file.py --pst ~/Downloads/archive.pst --output /tmp/emails.json
python3 scripts/build_report.py --input /tmp/emails.json --output /tmp/report
EMAIL_USER=sender@163.com EMAIL_PASS=xxx \
  python3 scripts/send_report.py --html /tmp/report.html --xlsx /tmp/report.xlsx \
    --to recipient@example.com

C — One-liner (pipe, no intermediate files)

python3 scripts/parse_file.py --pst archive.pst \
  | python3 scripts/build_report.py --output /tmp/report

D — Report only, no email (open locally)

python3 scripts/parse_file.py --mbox Inbox.mbox --output /tmp/emails.json
python3 scripts/build_report.py --input /tmp/emails.json --output /tmp/report
# Open /tmp/report.html in a browser
小葱技能7w4.net持续更新中。

AI analysis templates

After loading the email JSON into the AI context, use the following templates:

Part A: 4-Dimension Email Summary

🔥 Part 1 — Important & Action Items
  🚨 [URGENT]    Subject — Sender — Date | Summary | Action | Deadline
  ⚡ [IMPORTANT] Subject — Sender — Date | Summary | Action
  📌 [NOTE]      Subject — Sender — Date | Summary

📊 Part 2 — Grouped by Sender / Topic

✅ Part 3 — To-Do List

📅 Part 4 — Timeline (YYYY-MM-DD  Sender → Subject: summary)

Part B: Contact Profile Analysis

Sort by total interactions. For each contact:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
👤 Rank #N | Name <email>   Total: N (Recv: N / Sent: N)
🧑 Gender       M/F/Unknown  Confidence: H/M/L  Basis: …
💼 Role         …            Basis: domain / signature / keywords
🔗 Relationship Colleague / Client / Institution / Stranger
   Direction    Mutual / Owner-initiated / Contact-initiated
📝 Topics       • subject 1  • subject 2
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Heat scale: 🔥 Heavy (≥10) · ⚡ Active (5–9) · 💬 Moderate (2–4) · 🌙 Light (1)

Notes

• Credentials are passed via environment variables — never hardcoded
• IMAP connections use readonly mode — emails are never modified or deleted
• Body text is truncated to 2000 characters per email
• fetch_imap.py sends an RFC 2971 ID command required by 163/188 servers; harmless on others

这个 Skill 做得相当专业，功能覆盖面广，支持多种邮件来源和格式，报告生成美观实用，文档清晰易懂。不足是依赖较多、环境配置稍复杂，非技术用户可能需要花些时间安装配置；同时部分细节处理（如 Node 路径硬编码、异常处理）还有改进空间。整体而言是一个值得信赖的高质量工具。